DePaul University
From the SelectedWorks of Christopher J Einolf
2011
Who Volunteers? Constructing a Hybrid Theory
Susan M Chambre
Christopher J Einolf, DePaul University
Available at: http://works.bepress.com/christopher_einolf/11/
Chambre and Einolf Who Volunteers
Working Paper – June 2011
Susan Chambré and Christopher J. Einolf
Who Volunteers? Constructing a Hybrid Theory
Abstract
This paper describes three major theoretical perspectives in research on
volunteering: sociological theories which stress the importance of social context,
social roles, social integration and social networks; prosocial and value
orientation theories which emphasize the impact of individuals’ attitudes and
beliefs regarding the importance of altruistic behavior and a sense of social
responsibility; and resource theories that focus on the human capital and
economic factors which both allow individuals to volunteer in meaningful ways
and make them attractive to organizations.
Using the 1995 Midlife in the United States dataset, we operationalized the three
theories and examined their relative explanatory power. The most predictive
variables were those measuring social context, roles, and integration, followed by
measures of values, and then measures of resources. Combining variables from
all three traditions with demographic controls predicted 40.7% of the variation in
volunteering.
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In addition to its value to research, our hybrid model offers some directions for
recruitment and retention since, at different stages of the life cycle, social roles
and social networks either operate as constraints or triggers for volunteering.
Indeed, various sociological factors -- social context, roles, and integration --- are
the best predictors of volunteering, with a higher R-squared than prosocial value
orientations and resources.
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Who Volunteers? Constructing a Hybrid Theory
INTRODUCTION
Volunteering is a distinctive, significant and widespread social practice.
Surveys reveal that between one quarter and one half of American adults engage
in volunteer work over the course of a year, with estimates varying according to
differences in survey methodology and the ways that questions are worded
(Brown, 2000; Rooney, Steinberg and Schervish, 2004). Despite methodological
differences and yearly fluctuations, it is quite clear that the overall rate of
volunteering has increased over the past five decades (Corporation for National
and Community Service, 2006; Putnam, 2000). In 1965, the Americans Volunteer
Survey conducted by the U.S. Department of Labor revealed that 16% of
American over the age of 14 volunteered in the previous year (U.S. Department
of Labor, 1965). Data from the 2009 Current Population Survey shows that
26.8% of Americans over the age of 16 volunteered during the previous year
(U.S. Bureau of Labor Statistics, 2010).
Scholarship on volunteering, dating back to Sills’ (1957) study of
volunteers for the March of Dimes, has documented its myriad social and
individual benefits as well as the social, cultural, political and institutional factors
that shape individual engagement in volunteer work. Empirical research, much of
it summarized in a recent book by Musick and Wilson (2008), is indeed
voluminous. This research has documented an important Janus-faced quality of
volunteerism, that it benefits both volunteers and society. For individuals,
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volunteering enhances health and well-being (Thoits and Jewitt, 2001) and helps
people cope with various life events (Midlarsky, 1991). For society, volunteering
provides useful labor and builds social capital, contributing to the civil society is
central to a healthy democracy (Brown, 2000; Putnam, 2000; Simpson, 1996).
Volunteering, which involves donating one’s time without payment, takes diverse
forms: it ranges from participating in a social movement, soliciting names for a
petition, serving as a mentor, or working in a soup kitchen.
Most of the early research on volunteering documented individual
variables associated with involvement, such as gender, age, race, education,
income and religious attendance. Enormous strides have been made in our
empirical and conceptual understanding of volunteerism with greater use of
multivariate analysis and the introduction of a broader range of independent
variables. Recent scholarship has moved beyond the empirical to develop
theories and models of the antecedents of volunteering. However, most studies
feature a limited range of variables. Building on past research and theory, this
paper has three objectives. First, it reviews and organizes previous research into
three major theoretical traditions. Second, it empirically examines the explanatory
power of each theoretical framework. Third, it constructs a hybrid theory of
volunteering that describes three important pathways into volunteering. It then
uses the theory to develop several general recommendations for recruiting
volunteers.
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REVIEW OF THE LITERATURE
Who Volunteers?
The earliest scholarly work on volunteerism concentrated on the
descriptive question of who volunteers and who does not. The first large surveys,
conducted by the U.S. Department of Labor in 1965 and 1973, revealed that
women were more likely to volunteer than men, and showed an inverted Ushaped distribution regarding age, with the highest rates of participation among
middle aged people. Better educated people, whites, and those with larger
incomes had higher rates of volunteering. These relationships have been found
in every subsequent study, with one major change: a sharp increase in
volunteering by older people. With changed cultural messages and systematic
programs directed toward older people (Chambré, 1993), the link between age
and volunteering was altered. People over the age of sixty became much more
likely to volunteer and the probability of continued participation well into old age
increased. While most observers view increases in older volunteering as a
powerful trend likely to continue (Einolf, 2008; Freedman, 1999), others view it as
primarily due to the high levels of civic engagement of one age cohort, the
“greatest” or “long civic” generation, who reached maturity during the Great
Depression and World War II (Goss, 1999; Putnam 2000).
Most of the initial research relied on data sources that did not include
information on religious affiliation or attendance at religious services. The first
extensive discussion on the connection between religion and volunteering was
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documented in a study of older persons which revealed significant differences
between religious groups, with higher rates of volunteering among Jews and
Protestants than Catholics (Chambré, 1987). Subsequent surveys demonstrate
that religious participation has a strong influence on volunteering, as well as
other types of civic engagement.
When David Horton Smith (1994) surveyed research on volunteering and
voluntary association membership in the early 1990’s, he observed that there
had been little theorizing. While there are significant challenges to creating a
unified theory of volunteering (Hustinx, Cnaan and Handy, 2010), it is fair to say
that this situation has changed and that there are are numerous theories of
volunteering, most of them middle range. We classify them into three theoretical
traditions (Table 1): sociological, prosocial and value orientations, and resource
theories.
Sociological theories
Sociological theories focus on the social and cultural influences that
predispose some individuals to become engaged in volunteer work. Social
context influences volunteering at the macro and meso level, while social
networks and social roles influence volunteering on the micro level.
Macro and Meso Level Theories – Social Context: Volunteering is
conditioned by broad contextual and community level factors. One contextual
factor which may affect volunteering in an unexpected fashion is the state of the
economy. While one might expect volunteering to decrease during periods of
economic decline since people would spend time working or looking for work, this
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has not occurred in the recent economic downturn in 2008 and 2009
(Corporation for National & Community Service, 2010).
Sills’ (1957) study of March of Dimes volunteers was the first empirical
investigation of an important phenomenon in American life: the mobilization of
volunteers in response to the emergence of a new social issue, a series of
events, or a social catastrophe like a natural disaster, where there is a
convergence of social activity both as a way of providing unpaid labor and
channeling individuals’ desire to help and to cope. Perhaps the most extensive
research on this type of volunteering are studies of AIDS volunteers, which
suggest that individuals infected with HIV and their friends, families and lovers
were the earliest volunteers (Chambré, 1991a, 1991b; Omoto and Snyder, 2002).
Once the sense of urgency decreased and more funding became available,
organizations became professionalized and the initial centrality of volunteers
decreased (Chambré, 2006). There are earlier historical precedents to this
pattern, most notably the development of social work which began in the work of
volunteer friendly visitors in the late 19th century (Becker, 1964) as well as the
creation of new occupational roles like firefighting (Ellis and Noyes, 1978).
In addition to broad historical factors, rates of volunteerism are influenced
by meso level factors: the neighborhood, state, province and country where a
person lives are influential (Anheier and Salamon, 1999; Mellor, Hayeshi, et. al.,
2009; Musick and Wilson, 2008; Rotolo and Wilson, n.d.). This is partly due to
cultural traditions and religious differences; for example, the highest rates of
volunteering in the U.S. are in Utah, a largely Mormon state. Wolpert (1989,
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1993) traces these differences to cultures of generosity that are also evident in
various public policies like welfare payment levels. Although there might be
regional differences in philanthropy (Schneider, 1996), empirical research on this
issue has yielded contradictory findings (Caputo, 2009; Kim and Hong; 1998) .
Similarly, the impact of neighborhood effects is unclear (Perkins, 1996;
Wilson, 2000). Although neighborhoods with a relatively higher nonprofit density
might have higher participation (Sampson, McAdam, et. al., 2005), voluntary
association membership tends to be of shorter duration in dense organizational
niches (McPherson and Rotolo, 1996). Racially homogeneous neighborhoods
have higher rates of volunteering (Portney and Berry, 1997), which is consistent
with Putnam’s (2007) observation that racial and ethnic heterogeneity reduce
levels of social capital. Surprisingly, there is only slightly lower participation in
central cities (44%) than in suburbs and rural areas (49%) (Musick and Wilson,
2008).
Social context clearly plays a role in volunteering, both by encouraging a
sense of community and social obligation (Omoto and Snyder, 2002) and by
influencing the opportunities to become involved.
Micro Level Theories – Social Integration and Social Roles
Integration: Volunteering is one facet of a more socially engaged lifestyle,
since there is a strong correlation between volunteering and a host of active
leisure pursuits like interacting with friends, neighbors and family. In contrast,
individuals who do not volunteer spend more time watching television or listening
to the radio, sitting and thinking, or doing nothing (Putnam, 2000; Chambré,
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1987). Individuals who tend to be more outgoing and socially involved also tend
to be more likely to step forward and are more often asked to volunteer (Bekkers,
2005; Okun, Pugliese and Rook, 2007; Rossi, 2001). In addition, volunteering is
a way to cultivate new social relationships (Prouteau and Wolff, 2008).
Smith’s (1994) social integration model points out that participation
becomes an engrained social practice. In fact, Caro and Bass (1995) found that
over forty percent of volunteers worked for two or more organizations and slightly
more than one quarter were involved in three or more groups. People who
express a strong sense of community obligation (Okun and Michel, 2006) and
have strong social and commmunal ties (Jones, 2006) tend to act out those
commitments as volunteers. There is also a great deal of continuity of civic
engagement over the course of people’s lives (McFarland and Thomas, 2006).
People who volunteer tend to be ‘joiners’ (Hausekenecht, 1962), with more
extensive social networks (Apinunmahakul and Devlin, 2008), and are more
involved in a host of social and recreational activities than nonvolunteers
(Chambré, 1987). Having friends who volunteer also has a positive effect on
volunteer engagement (Wymer, 1999).
Social Roles: Other social roles influence volunteering, as volunteering
can compensate for a lack of fulfillment in other roles (Staines, 1980), or act an
extension of work and family obligations (Choi, Burr, et. al., 2007). This spillover
effect is evident in the work-volunteering nexus, both because participation is
expected in some careers, and because the human and cultural capital
associated with a person’s job influence a person’s success in finding and
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succeeding in a volunteer job (Wilson and Musick, 1997b). Having a flexible work
schedule (Freeman, 1997) and a stable work history rather than a “disorderly”
sequence of jobs (Wilensky, 1961b; Rotolo and Wilson, 2003) are both positively
associated with volunteering.
The impact of other social roles as incentives explains the age-related Ushaped distribution of voluntary association membership (Cutler, 1977; Cutler
and Hendricks, 2000) and volunteering. Transitions at different stages of the life
course explain both attachment to the volunteer role and shifting allegiances to
specific volunteer positions, since “certain life stages are times of voluntary
membership mobility,” when “individuals may leave one type of organization and
in turn join others” (Rotolo 2000:1155). Engagement peaks in midlife when
people join organizations and volunteer in conjunction with family- and workrelated obligations (Knoke and Thomson, 1977). People move into and out of
voluntary associations and volunteer work at various stages in their lives in ways
that are connected to other aspects of their lives. Teenagers are involved in
youth organizations, and parents participate in Little League, PTAs and other
child-related activities (Knoke and Thomson, 1977; Rotolo, 2000). Wives appear
to draw their husbands into volunteering, but not vice versa (Rotolo and Wilson,
2006).
Other social roles can serve as constraints on volunteering due to a lack
of time and the prospect of role overload. Thus, when individuals move out of
key social roles like work and active parenthood, the discretionary time they gain
might be spent volunteering. Indeed, individuals are more receptive to the idea of
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volunteering within the first two years after they retire (Caro and Bass, 1997).
Although caregiving activities do not lead to reduced volunteering by midlife and
older persons (Farkas and Himes, 1987; Gallagher, 1994), women reduce their
involvement in volunteering due to family obligations much more than men (Choi,
Burr, et al., 2007).
Thus, the link between volunteering and other social roles is complex and
often contradictory: some social roles provide opportunities, incentives, or even
mandates for participation, but the same roles can also reduce volunteering
because of time constraints.
Prosocial and Value Orientations:
A great deal of research has focused on the importance of individual
characteristics, including personality traits, motivations and values, that
predispose people to engage in a variety of prosocial and altruistic practices,
including volunteer work (Clary, Snyder, et. al., 1998; Mowen and Sujan, 2005).
Some of the earliest psychological research considered whether certain
characteristics were associated with an altruistic personality (Batson et al., 1986;
Penner et al., 2005). Recent research has documented that volunteering is
associated with several personality characteristics including resilience,
extraversion, self efficacy, and low levels of neuroticism (Carlo, Okun, et. al.,
2005; Matsuba, Hart, and Atkins, 2007; Okun, Pugliese, et al., 2007).
A number of value orientations and motivations differentiate between
volunteers and nonvolunteers, including commitment to altruistic values (Wielhe
and Isenhour, 1977), commitment to personal charitable behavior and public
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involvement (Sundeen, 1993), and being more interested in interpersonal
expression and growth than in success (Williams, 1986). Volunteers also express
greater support for norms of obligation (Schwartz, 1997; Schwartz and
Fleishman, 1978). Qualitative interview studies of Holocaust rescuers (Monroe,
1996) and exemplary moral leaders (Colby and Damon, 1992) indicate that
highly altruistic people were propelled by a sense of moral obligation toward
many people, not just friends and family, a characteristic that Samuel and Pearl
Oliner (1988) call “extensivity.” Rossi (2001) and Einolf (2010) found that moral
obligation correlated with prosocial action in the domains of family, volunteering,
and charitable giving, and Einolf (2010) found that people with extensive moral
obligations were more likely to donate time and money to charitable causes.
Perry, Brudney, and colleagues (2008), examined how “public service
motivation,” a composite measure of prosocial motivation in the workplace,
correlates with volunteering.
Clearly, a desire to help others without payment shapes the decision to
volunteer. Yet it is also clear that pure altruism is rare (Andreoni, 1980) and that
a combination of altruism and self interest is common (Gidron, 1983; Van Til,
1985). Midlarsky (1991) observes that individuals themselves derive a number of
benefits in the process of helping other people. This view was commonly
expressed by AIDS volunteers in the late 1980’s and early 1990’s, who often said
that they ‘got’ more than they ‘gave,’ and often found volunteering not only
beneficial but transformative (Chambré, 1995, 2006).
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Religious precepts are also influential. The connection between religiosity
and volunteering is well documented (Becker and Dhingra, 2001; Park and
Smith, 2000; Janoski, Wilson, and Musick, 1998; Musick and Wilson, 2008), and
one explanation of this connection are the moral norms and values which
motivate helping behavior, including volunteering (Lee, Piliavin, and Call, 1999;
Schervish and Havens, 1997, 2002; Schwartz, 1977; Schwartz and Fleishman,
1978; Wilson, 2000).
People who report that they are actively involved in religious organizations
and attend services on a regular basis (Hodgkinson, Weitzman and Kirsch, 1990;
Wilson and Janoski, 1995) more often volunteer. However, Cnaan, Kasternakis
and Wineberg (1993) found that self-defined religiosity did not distinguish
between the motivations of volunteers and nonvolunteers. They also found that
the strength or importance of people’s religious beliefs were less significant
predictors of volunteering than the fact that churchgoers have more extensive
social networks and are more likely that a person will be asked to volunteer
(Becker and Dhingra, 2001; Park and Smith, 2000). However, a recent analysis
using more nuanced measures of subjective religiosity coded from interview data
found a link between religious values and volunteering (Einolf 2011).
Psychologists influenced by Erik Erikson’s (1980) life stage theory of
human development have pointed out that the meaning of volunteering changes
over the course of people’s lives, especially as they reach the generative stage of
life. This stage begins in middle adulthood, and is marked by a turn away from a
focus on one’s self and an increased focus on what one will leave for the next
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generation. Later theorists connected this change in focus with an increased
awareness of mortality, so that people begin to define themselves by what legacy
they will leave for the next generation (Kotre, 1984). Generative concern has
been associated with a variety of helping behaviors, including volunteering (Rossi
2001).
Social psychologists as well as sociologists have pointed out that
individuals who actively engage in various forms of prosocial, altruistic or
philanthropic practices develop an image of and expectations for themselves as
donors of time and money. Lee, Piliavin and Call (1999) explain continued
commitment to altruism, blood donation and volunteering in terms of ‘role identity’
theory. A person who volunteers repeatedly may come to think, “I am the kind of
person who volunteers,” and eventually, “Volunteering is an important part of who
I am.” People might begin volunteering for any number of reasons, but may
continue volunteering even after their initial commitment ends because they have
assimilated the role of volunteer into their sense of self.
Several ethnographers and sociologists offer variants of role identity
theory. Allahyari (2000) uses the term ‘moral selving’ to refer to a process in
which individuals construct a self image that both promotes and reinforces their
involvement in charitable work. Schervish and Havens discuss the concept an
“identification theory of care” to describe a moral citizenship and sense of
concern about others (Schervish, 2006; Schervish and Havens, 2002).
Research on prosocial orientations also considers the impact of
socialization into volunteer roles, including parental modeling and participation in
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community service projects during high school or college (Hart, Donnelly, et al.,
2007). Socialization models of volunteering especially focus on the importance
of having parental role models and the intergenerational transmission of
prosocial attitudes (Hodgkinson, 1995). In their study of the antecedents of public
service motivation, Perry, Brudney, and colleagues (2008) found that the most
salient influence was parental volunteering, family orientations toward helping
strangers, and helping behavior within families. While it is clear that family
socialization is critical, the effects of family values appear to be more significant
earlier in people’s lives than at later points (Janoski, Musick and Wilson, 1998).
Based on three waves of data collection from the Youth-Parent Socialization
Panel study, Janoski and Wilson (1995) demonstrate that family orientations
influence whether or not their children are engaged in high school activities, the
impact of parental modeling diminishes over time, and individuals develop their
own values that are only partly influenced by their parents as they grow older.
Resource Theories
One of the earliest theories of volunteering was “dominant status theory.”
Lemon, Palisi and Jacobson’s (1972) study revealed that there was a positive
correlation between the voluntary association membership and the number of
“dominant statuses” people occupied such as being male, having more
education, higher income and more prestigious occupations. This perspective
has now been subsumed under resource theories, which specify that individuals
are more likely to volunteer when they possess the kinds of skills and resources
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that promote their involvement. These resources make them attractive to
organizations, and hence more likely to be recruited (Wilson and Musick, 1997;
Yörük, 2006). In addition, people who have a surplus of the key ingredient
involved in volunteering – time – are also more likely to volunteer.
Conceptualizing the decision to volunteer in terms of rational choice
theory, Musick and Wilson (2008:113) point out that “volunteering is more
attractive to the resource-rich than to the resource-poor. If volunteer work
demands money, the rich will find it easier to do; if it demands knowledge and
‘civic skills,’ the well educated will be less challenged by it… In other words, the
resource-rich are more likely to ‘profit’ from doing volunteer work.”
There is considerable support for this perspective. Numerous studies point
out that human capital variables are powerful predictors of volunteer participation
(Chambré, 1987; Musick and Wilson, 2008). Those with higher levels of human
capital, as measured by education and occupation, are far more likely to
volunteer than those with more modest education and limited occupational skills.
Musick and Wilson (2008) point out, however, that people with higher levels of
human capital are also more likely to volunteer because they are more often
asked to volunteer.
Recent economic research casts some doubt on resource theory. Since
there is a tradeoff between time spent working for pay and time spent working for
free, people with lower wages would be expected to volunteer more than people
who earn higher wages. While economists view this as “standard labor supply
substitution behavior,” it turns out that wages explain “only a minor part of
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differences in volunteer activity among individuals with similar demographic
characteristics” (Freeman, 1997). One exception seems to be new retirees, for
whom volunteering is higher among those with lower preretirement wages (Moen
and Fields, 2002).
While nonvolunteers most often cite a lack of time as the major reason for not
being involved in volunteer work (Sundeen, 2007), there does not appear to be a
tradeoff in time spent working versus time spent volunteering. Among people of
all ages, and particularly among older persons, there is little evidence that when
people reduce their work commitment, they use the free time they gain to
volunteer. Among older persons, who have reduced work and family obligations,
volunteer participation is higher among those who continue to work than for
homemakers and retirees regardless of age (Chambré, 1987).
There is also no support for the idea that volunteering tends to be more
common when people retire and claim that they miss working. Data from a 1981
national survey of older persons revealed that “people who miss the structure
imposed by work, the interpersonal relationships emerging in a work setting, the
feeling of being useful, and the respect derived from working actually volunteered
less often than people who indicated they did not miss these things” (Chambré,
1987:29). Even when age and health are introduced as controls, working less is
not associated with volunteering more. Once a person does volunteer, however,
the amount of time devoted to unpaid work is greater when they work part time or
they are retired than if they are work on a full time basis (Chambré, 1984, 1987;
Musick and Wilson, 2008).
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While volunteer rates are higher among women who are out of the paid
labor force (Rotolo and Wilson, 2007), it is not so clear that this is because they
have free time. Three studies of elite women found no support for the idea that
they viewed volunteer work as an unpaid job, nor are there significant differences
between the motivations expressed by women volunteers who were working and
those who were not (Markham and Bonjean, 1996). Kaminer’s study of Smith
College graduates (1984:197) observed that volunteering was “simply something
‘extra’: it supplements instead of substituting for a paid job.” Daniels (1988)
reached a similar conclusion, noting that “they do the work because they enjoy it,
not because they have to.”
Resource theories point to an important dynamic: the recursive
relationship between volunteers and the organizations they serve. Individuals
bring skills, knowledge and cultural capital to their work, and organizations select
individuals who will be a good “fit.” Individuals with high levels of human capital
may be more attractive especially for human service and religious volunteer
positions. Those with high levels of social capital have the networks which both
inform and recruit them to become involved. Cultural capital is also important
because of the knowledge of the work setting and the norms of dominant social
institutions.
Models of Volunteering
Several teams of scholars have developed empirical models of
volunteering that combine several sets of causal factors to examine their
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interrelationship and relative influence on volunteering. The earliest of these
(Chambré, 1987) indicates that social and demographic characteristics (race,
religion, gender, marital status and age) influence two measures of resources
(income and education) as well as health. These variables influence life
satisfaction and an overall level of social engagement, which are the most salient
direct influences on whether or not older persons in the sample volunteered.
A second model, based on a sample of older persons (Herzog and
Morgan, 1993), analyzes the influence of a series of demographic, socioeconomic, contextual, social role, social engagement and personality variables.
They build a conceptual model of volunteering which includes three sets of
exogenous factors (environmental, social structural and personality), which
influence social roles, resources and lifestyle measures, which directly influence
volunteer status.
In a series of articles with a number of different collaborators, John Wilson
draws on several different surveys, including Americans’ Changing Lives and the
Youth-Parent Socialization Study, to develop models of involvement in volunteer
work and attachment to volunteering. Using the study of youth and their parents,
Janoski, Musick and Wilson (1998) found support for Smith’s General Activity
model in the longitudinal Youth-Parent Socialization Study, as the strongest
direct influence on whether or not a person volunteered in 1982 was whether or
not they volunteered in 1973, which in turn was most influenced by whether they
volunteered in 1965. While attitudes about tolerance, passive or active
citizenship, and political efficacy in 1965 had a major impact on whether or not
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they volunteered in 1973, the link between attitudes held in 1973 and volunteer
status in 1982 was significant but weak.
Wilson and Musick’s (1997) article “Who Cares? Toward an Integrated
Theory of Volunteer Work” develops an empirical model of volunteering that
includes a relatively broad range of variables. These include background
demographic variables (gender, race and age), measures of socioeconomic
status (income and education), and measures of resources, religiosity, human
capital, social capital and cultural capital. The strongest direct influences on
volunteering were religiosity and number of children. The model explains a
substantial amount of the variance in volunteering, with an R-squared value of
.426. However, the measures of various forms of capital diverge from common
definitions: income and health are used as measures of human capital, number
of children is a proxy for social capital, and church attendance, attitudes toward
helping and the use of prayer are measures of cultural capital.
Drawing on 1995 data from the Midlife in the United States study,
Matsuba, Hart and Atkins (2007) develop a three stage path model that includes
personality factors, gender, age, education and income as exogenous variables,
which influence civic obligation, helping identity, and opportunity to volunteer.
The major factor influencing volunteering is opportunity (which includes
institutions and relationships) and a measure of identity.
A structural model, based on a study of exemplary volunteers who won
Points of Light awards, includes formal volunteering as part of a model of
interrelated factors rather than the dependent variable, since the focus was to
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explain public service motivation (Perry, Brudney, et al., 2008). The model
indicates that the strongest influences on formal volunteering are religious
activity, youth volunteering and being female. Other variables, including family
socialization, income and education, were not significant.
With the exception of this last model, all of them are multi-stage path
models where measures of social context, demographic, socio-economic status,
personality and values are exogeneous variables, which have an indirect effect
via mediating factors such as social integration, social capital and social roles.
Thus, for example, a study of extraversion and volunteering among older persons
reaches the conclusion that extraverted people volunteer because they are more
actively engaged in a host of social activities (Okun, Pugliese and Rook, 2007).
However, most of these studies fail to include the R-squared value of the model,
and hence provide no sense of their models’ explanatory power.
Finally, Bekkers and Wiepking’s (2011) literature review on charitable
giving is similar to the current article, as it divides causes into broad theoretical
categories. Bekkers and Wiepking identify eight causal mechanisms for
charitable giving: awareness of need, solicitation, costs and benefits, altruism,
reputation, psychological benefits, values, and efficacy. Our category of
motivations and prosocial value orientations corresponds to Bekkers and
Wiepking’s categories of altruism, psychological benefits, and values. Our
category of resources relates to the authors’ discussion of the material costs and
benefits of giving. Social context, roles, and networks increase awareness of
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need, and provide the context within which volunteers attempt to enhance their
reputations.
In conclusion, fifty years of research on volunteering have provided
researchers with a wealth of empirical data and a number of theories to make
sense of these data. However, most scholars work within a single academic
discipline and apply only a few theories from their own discipline. Our paper
brings these theories together under three categories: social context, roles, and
networks, prosocial and value orientations, and resources. The next section
describes how these three traditions can be combined into a single hybrid model
and tested empirically.
Toward a Hybrid Theory of Volunteering
There are several ways to empirically test these three theories and
construct a hybrid theory. . One way is to consider the three traditions as
alternative explanations, which leads to our first research question: Which of the
three theories is the most explanatory? A second approach is to view them as
complementary, providing different pathways to volunteering. Adopting this
approach, we ask which combination of variables best predicts volunteering. A
final question examines the relationship between theories and variables. Many
variables commonly studied as predictors of volunteering actually measure
concepts from more than one theory, leading to unexpected empirical results. By
carefully mapping variables to theories, and accounting for when theories work
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for or against one another, we can clarify some confusing but often observed
empirical results.
Where variables measure concepts from more than one theory, the
theories may either work together to encourage volunteering, or against each
other to produce little net result. Education is a good example of how a single
variable can measure concepts from more than one theoretical tradition, resulting
in very strong net effects. One can view education as a resource, giving people
skills for volunteering (Wilson and Musick, 1997). But education also teaches
civic values, making it a type of prosocial value orientation; education brings
people into social networks, and educated people are more likely to take on
professional roles where volunteer work is expected. As education works across
all three theoretical traditions, it is not surprising that studies consistently find it to
be a very strong predictor of volunteering.
Other variables measure a positive effect of one theoretical tradition but a
negative effect of another. For example, when people retire from full time work,
they gain in the resource of free time, but lose the social roles and networks that
come with full-time employment. Similarly, the arrival of children brings parents
into new social networks and creates social role expectations, but also reduces
their amount of free time. One would therefore expect the net effect of retirement
and having young children on volunteering to be small, and it would not be clear
whether the direction would be positive or negative.
This study does not attempt to create a path model, as Chambre (1987)
and Wilson and Musick (1997) did in earlier work, as the causal relationships
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among variables in each category are probably reciprocal. For example,
education (a resource) may draw an individual into a particular profession (social
role), but work in that profession may in turn develop skills valued in volunteering
(resource). Prosocial values may encourage a person to attend religious
services, which in turn may reinforce prosocial values. Accurately tracking these
causal relationships would not be possible with the data used in this study.
This study offers a preliminary test of the three theories by
operationalizing each theory with several variables, and testing the ability of
these variables to predict variation in volunteering. It asks whether we can
combine elements from all three traditions to formulate a single predictive model.
DATA AND METHODS
To test our hybrid theory, we used data from the 1995 wave of the Midlife
in the United States (MIDUS) survey. This survey is random and nationally
representative, uses a large sample, studies individuals in a broad age range,
and includes variables that measure concepts from all three theoretical traditions.
The MIDUS study used written surveys and telephone interviews with a
nationally representative random-digit dialing sample of 3,032 noninstitutionalized, English-speaking adults, born between 1920 and 1970. The
estimated overall response rate to the first wave was 60.8% and the data are
weighted to adjust for nonresponse.1
Dependent variable: Volunteer status
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The MIDUS survey asked respondents to write in how many hours they
spend volunteering each month. While the survey does not define volunteering, it
prompts respondents with questions about volunteering for health, medical,
youth, and educational organizations. About a third of respondents, or 36.8%,
stated that they volunteered in a typical month. Among those who did volunteer,
the mean hours per month spent volunteering were 14.4, the median hours were
8.0, and the standard deviation was 20.5 (Table 2). The variable was skewed to
the right, with a small number of respondents contributing a large amount of
volunteer time.
[Table 2 here]
Independent variables
Demographic variables: The MIDUS sample was 43.5% male, 11.2%
African-American, 1.0% Asian-American, and 2.6% Latino. The average age of
the respondents was 45.3 years old. We use a quadratic term for age in
regression analysis, as prior studies of volunteering find that age has a
curvilinear relationship with volunteering, increasing through middle age and
declining late in life.
Social context: To measure social context properly, one would want
information about levels of volunteering, social capital, and nonprofit activity in
each respondent’s neighborhood, city, and state. The MIDUS survey does not
have such data, meaning that the data do not allow us to measure social context
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directly. However, the survey does have a measure of neighborhood safety and
quality originally designed by Keyes (1998; Keyes and Shapiro, 2004). This scale
consists of four statements, “I feel safe being out alone in my neighborhood
during the daytime,” “I feel safe being out alone in my neighborhood at night,” “I
could call on a neighbor for help if I needed it,” and “People in my neighborhood
trust each other.”
Social roles: Labor force status, family status, and participation in religious
institutions and voluntary associations bring with them social roles that
encourage volunteering, but also increase social integration, making it difficult to
disaggregate the two effects. We consider marital status to be an exception –
being married is a social role that
Social integration: The MIDUS survey uses measures of social integration
and social contribution originally designed by Keyes (1998; Keyes and Shapiro,
2004). The social integration measure consists of three agree/disagree
statements, “I don’t feel I belong to anything I’d call a community” (reverse
coded), “I feel close to other people in my community,” and “my community is a
source of comfort.” The social contribution measure is also three statements, “I
have something valuable to give to the world,” “My daily activities do not create
anything worthwhile for my community” (reverse coded), and “I have nothing
important to contribute to society” (reverse coded). Finally, MIDUS had two
questions about social interactions with neighbors, one that asked how often they
had any contact, “even as simple as saying hello,” and another that asked how
often they “have a real conversation or get together socially.”
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Prosocial and value orientations: The MIDUS survey contains many
psychological scales measuring prosocial personality traits and value
orientations. Of these, this study used measures of generative concern, moral
obligation, subjective religiosity, and prosocial role identity.
MIDUS incorporated six items from the Loyola Generativity Scale
(McAdams and de St. Aubin, 1992) which measures a respondent’s level of
concern for the next generation. The survey asked respondents to describe how
much six statements apply to them, such as “Many people come to you for
advice,” and “You have had a good influence on the lives of many people.”
MIDUS has nineteen questions that asked respondents to rate how obligated
they would feel to help individuals or perform a task for the benefit of the
community. Examples include how obligated the respondent would feel “to drop
plans when your children seem very troubled,” “to do more than most people
would do on your kind of job,” and “to volunteer time or money to social causes
you support.” We used both the single-item measure of obligation to volunteer
time and moneyand the average of all nineteen questions.
We used four questions about how religious and how spiritual the
respondents are, and how important religion and spirituality are in their lives, to
make a single measure of subjective religiosity. We measured prosocial role
identity through a question that asked respondents to “rate your contribution to
the welfare and well-being of other people” over the course of their lives. We
measured volunteer role identity through a question that asked whether the
respondent expected to volunteer 15 or more hours per week 10 years from now.
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Resources: Measures of work skills include the prestige of respondents’
occupations and household income. Education is also a measure of skills, and
labor force status (full-time, part-time, or not working) and a dummy variable
measuring the presence of minor children serve as measures of free time.
Education, labor force participation, and the presence of children embody other
paradigms as well, and are discussed in the next section.
Multiple paradigms: Ten variables measure concepts related to more than
one paradigm, with four measuring concepts that work together and six
measuring concepts that work at cross-purposes. Education is a resource and
also facilitates participation in social networks. Religious services attendance,
religious meeting attendance, and attendance at meetings of voluntary
associations build social networks and provide opportunities to take on prosocial
roles, and also encourage prosocial motives through the internalization of
external norms.
Three variables related to employment status and three related to children
measured paradigms that affected volunteering in conflicting ways. Employment
can promote volunteering through the social networks and skills that come with it,
but can also discourage it due to the demands paid work places on individuals’
time. The presence of children in the household can encourage volunteering
through increased social networks and the adoption of the social role of parent,
but can also discourage volunteering through the demands children make on
parents’ free time.
Method of Analysis
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Because the dependent variable (hours volunteered) is not normally
distributed, we used logistic regression in our analysis, and converted the
volunteering measure into a binary variable with 0 = no volunteering and 1 = any
volunteering. In reporting the logistic results, we give the log of the odds ratio for
each independent variable rather than the slope coefficient. Estimates of the
predictive power of each regression model are estimated using the Nagelkerke
calculation of pseudo-R squared (Nagelkerke, 1991).
FINDINGS:
Multivariate regression (Table 3) provides the answer to our first research
question, which theoretical tradition best contributes to our ability to predict
participation in volunteering. Demographic controls alone (Model 1) explained
very little of the variation in decisions to the likelihood of being a volunteer
(Nagelkerke pseudo R-squared = .013). The resource variables (Model 2)
explained 13.6% of the variation in decisions to volunteer, prosocial and value
orientation measures (Model 3) explained 28.4% of the variation, and the social
context, network, and roles variables (Model 4) explained the highest proportion
of variation, 34.9%.
The second research question, how well a model combining all three
traditions would predict volunteering, is answered by Models 5 and 6. Model 5
presents the results of a regression equation containing all the variables in the
study, with a pseudo R-squared value of .407. We then removed variables one at
a time, starting with the ones that were non-significant in the full model, to obtain
a parsimonious model. Model 6 contains only eight variables, obligation to
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volunteer, volunteer role identity, education, religious services attendance,
voluntary association attendance, full-time employment, children aged 7-13, and
children aged 14-17, but predicts 36.1% of the variation in participation in
volunteering.
Bivariate regression (Table 4) answers our third question, whether
variables that measure concepts from more than one theory will have greater or
lesser explanatory power. Mixed support was found for the idea that variables
that measured concepts from more than one theoretical tradition would better
predict volunteering than variables that measured only one. If one adopts a
Nagelkerke pseudo R squared value of .05 as a rough measure of strong
association, education (pseudo- R2 = .072), religious services attendance (R2 =
.076), religious meeting attendance (R2 = .066) were over this threshold.
However, a number of variables measuring concepts from only one theoretical
tradition were also over this threshold, including generative concern (R2 = .067),
expected future volunteering (R2 = .081), social contribution (R2 = .113), social
integration (R2 = .089), and voluntary association attendance (R2 = .126).
Stronger support was found for the hypothesis that competing effects
measured in a single variable might cancel each other out. Full-time employment
had only a marginal negative effect on volunteering (R2 = .002), and retirement
had no significant positive effect. As expected, the negative effect of reduced free
time for full-time workers was counteracted by the positive effect of social
networks that come with employment, and the reverse seems to be true of
retirement. Part-time workers, who possess both time and work-based social
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networks, volunteer significantly more than either group (R2 = .008). While the
presence of children in elementary and middle school (R2 = .025) and high
school (R2 =.009) made it more likely that people would volunteer, having
preschool aged children had no effect on the likelihood of volunteering. In this
case, the positive effect of the social networks that come with having children
seems to be canceled out by the extensive demands that young children place
on parents’ time.
DISCUSSION AND CONCLUSION
This paper classified existing theories of volunteering into three traditions:
social context, roles, and integration, prosocial value orientations, and resources.
We used bivariate and multivariate regression analysis to find that all variables
from each tradition predicted volunteering, and we combined variables from the
all three theoretical traditions to construct a hybrid model. The most predictive
variables were those measuring social context, roles, and integration, followed by
measures of values, and then measures of resources. Combining variables from
all three traditions with demographic controls predicted 40.7% of the variation in
volunteering. A parsimonious model with eight variables from the three traditions
predicted 36.1% of the variation in volunteering.
We predicted that variables measuring concepts from more than one
theoretical tradition would be particularly powerful predictors of volunteering.
These variables were education (resources, value orientations, and social roles
and networks), and attendance at religious services and meetings (value
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orientations and social networks). While such variables were strong predictors of
volunteering, they were no stronger than some of the variables that drew upon
only one theoretical tradition.
On the other hand, variables that measured positive effects from one
theory and negative effects from another did have small or zero effects, as
predicted. As predicted, full-time employment had no significant effect on
volunteering, as it corresponds to two paradigms which cancel one another out:
the positive effect of social roles and integration that come with employment, and
the negative effect of scarcity in the resource of free time. Similar results were
found for retirement (increased resource of free time, but decrease in social roles
and integration), and having pre-school children (increase in social roles and
integration, but decrease in free time).
Some limitations in the data made a full test of the three traditions theory
impossible. The MIDUS data set only had one variable for social context, which
measured respondents’ subjective opinions of neighborhoods, thus conflating
respondents’ psychological characteristics and the actual characteristics of their
neighborhoods. Future research should use data sets that include measures of
volunteer participation, nonprofit density, and social capital in the community
where the respondent resides. Another limitation exists with the measures of
resources and social integration. Most variables that measured one of these
theoretical traditions also measured one or more other traditions, making it
difficult to disaggregate the effect of each separate tradition. Future studies could
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avoid these problems through careful survey design, and some suggestions for
such design are included below.
CONCLUSION
The hybrid three traditions theory expands both theoretically and
methodologically upon earlier comprehensive theories of volunteering. The three
traditions paradigm goes beyond Wilson and Musick’s (1997) division of the
causes of volunteering into human capital, social capital, and cultural capital, by
disaggregating social capital into social context, social roles, and social
integration. The model helps researchers separate variables from theories, and
predicts that variables that simultaneously measure concepts from multiple
theories may have larger or smaller than expected effects on volunteering. Space
considerations make it impossible to fully test this implication of the theories, but
these implications can be tested in future research. Methodologically, the test of
the three traditions model goes beyond earlier tests by using a broader array of
variables, and by clarifying how variables can correspond to more than one
theory.
For researchers, the value of the three traditions model is that it helps
clarify some longstanding empirical findings on the correlates of volunteering. For
example, researchers have long known that education is a powerful predictor of
volunteering. The five paradigms model shows that the reason for its strength is
the fact that the education variable measures concepts corresponding to all three
traditions: values, resources, and social roles and integration. Similarly,
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researchers who think of retirement as a transition that only adds to people’s free
time neglect the fact that withdrawal from the labor force reduces the social roles
and integration that encourage volunteering. Viewing retirement through the lens
of multiple theories explains why retirement has little overall effect on volunteer
participation.
The three traditions theory suggests several avenues for future research.
The first avenue is methodological, as the theory suggests that many of the
variables commonly used in surveys actually measure multiple theories and
should be disaggregated for better accuracy. For example, education is often
taken as a measure of skills, but education is also a proxy measure for social
integration and prosocial values. Future surveys should find a way to separate
these three effects. For social integration and social roles, future surveys should
distinguish between activities that merely bring people into contact with others
from activities that also assign roles to individuals where volunteering is
expected. For example, all voluntary associations create situations where
members encounter other people, but only some associations also expect their
members to volunteer. The three traditions model would predict that the latter
type of organization would have a more powerful association with volunteering
than the former.
In addition to its value to research, the three traditions model has practical
implications for policy makers and volunteer managers. Our comparative test of
the three traditions shows that social context, roles, and integration are the best
predictor of volunteering, with a higher R-squared than prosocial value
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orientations and a much higher R-squared than resources. This is good news for
volunteer managers. They will not have to read the minds of potential volunteers
to look for prosocial values, or read their bank statements to ascertain their
income. Easily observable social behaviors, such as religious participation, social
interaction, and voluntary association membership, are the best predictors of
willingness to volunteer.
A second use of the three traditions theory relates to encouraging
volunteer work among retirees. People gain free time when they retire, but lose
the social roles and networks that come with employment, so that retirement has
little net effect on volunteering. Volunteer managers can concentrate on other
social networks to recruit retired volunteers, and emphasize the meaningful and
productive nature of volunteering to create a replacement social role for people
who have lost their roles as paid workers. Retirees also lose the social networks
that came with employment, and retirees who move to a new home may lose all
of their social networks as well as the social context that originally encouraged
them to volunteer. Taking advantage of the social context of retirement
communities to encourage networks that hold norms of volunteering may help
compensate for this loss of networks and context..
Of particular use to program managers and policy makers may be the
division of Wilson and Musick’s social capital category into social context, roles,
and networks, as managers can use all three to promote volunteering. Any type
of social contact increases the likelihood of volunteering, as each additional
friend or acquaintance is an additional person who may ask an individual to
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volunteer. However, some types of social contact also include social roles and
contexts that encourage volunteering. By adding pro-volunteering roles and
contexts to existing social networks, programs may encourage volunteering
among people who had not previously considered it. A performing arts company,
for example, may encourage regular attendees at performances to change the
conception of their own role from passive onlooker to volunteer, by calling them
“members” or “patrons” and establishing normative expectations for them. As this
idea takes hold, existing patrons may encourage new attendees to also take on
the role of volunteer, turning the performing arts organization into a context that
encourages volunteering. Finally, volunteers encourage other members from
their social network to join, first as audience members and then as patrons and
volunteers.
In conclusion, we have constructed an integrated theory that explains why
some individuals engage in volunteering and others do not. Our data indicate that
demographic characteristics alone have little far less influence than values and
social integration. Having surplus resources, especially time, is significant but
less important than other factors. Our theory specifies that volunteering is
positively influenced by a combination of cultural and social components. In
contrast to those who do not participate, volunteers are influenced by prosocial,
especially religious values, and are more socially integrated.
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Table 1: Summary of Three Theoretical Traditions
Name
Theory
Empirical Evidence
Social context
Volunteers mobilize in
March of Dimes volunteers
response to emergencies
19th century Friendly Visitors
and salient social issues
AIDS volunteers
and are affected by
Variations by state, province or
neighborhoods and
county
cultures of generosity
Nonprofit density increases
volunteering
Racial and ethnic diversity
decrease voluntary association
Social
Volunteers are more
integration
involved in numerous
membership
social and organizational
Volunteers have higher levels of
activities
engagement in numerous social,
voluntary and leisure activities
Levels of social capital are the
strongest direct predictors of
Social roles
volunteering
Other social roles are
pathways into and
Volunteering is a spillover of other
constraints on volunteering social roles: age and lifecycle
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effects
Contradictory findings regarding
volunteering and other role
obligations
Prosocial and
Motivations, values and
Personality: agreeableness,
value
personality characteristics
agency, empathic concern,
orientations
influence volunteering.
extraversion, conscientiousness,
low neuroticism
Values: altruism, obligation,
religiosity, generative concern
Salient role identity
Family socialization
Resource
Individuals with higher
Education, skills, and free time
resources are more likely
to volunteer and more
likely to be recruited to
volunteer
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Table 2. Descriptive statistics
Variable
Range
Mean or %
Standard
Cronbach’s
yes
deviation
alpha
Dependent variable:
Volunteering (yes/no)
0-1
36.8%
n/a
n/a
Volunteering (hours/month,
0-120
14.4
20.5
n/a
Male
0-1
43.5%
n/a
n/a
Black
0-1
11.2%
n/a
n/a
Asian
0-1
1.0%
n/a
n/a
Latino
0-1
2.6%
n/a
n/a
Age
25-74
45.3
13.5
n/a
1-4
3.4
0.6
.68
1-4
2.8
0.6
.84
among those who do
volunteer)
Demographics:
Social context:
Neighborhood safety and
quality
Prosocial and value
orientation:
Generative concern
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Obligation to volunteer
0-10
6.3
2.6
n/a
Obligation overall
0-10
7.2
1.4
.88
Subjective religiosity
1-4
2.8
0.7
.87
Role identity (lifetime
1-5
3.8
0.9
n/a
0-1
27.5%
n/a
n/a
Occupational prestige
8-90
36.7
13.6
n/a
Income (in thousands)
0-250
0-1
68.2%
n/a
n/a
Social contribution
3-21
15.2
3.8
.67
Social integration
3-21
13.8
4.3
.73
Informal socializing with
1-6
4.0
1.3
n/a
contribution to others)
Role identity (expectation of
high current volunteering)
Resources:
Social role:
Marriage
Social integration:
neighbors
Multiple paradigms – all
positive predicted effects
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Education
1-12
6.2
2.4
n/a
Religious services attendance
0-15
2.3
3.1
n/a
Religious meeting attendance
0-10
0.8
1.8
n/a
Voluntary association
0-15
2.1
3.5
n/a
Part-time employed
0-1
13.5%
n/a
n/a
Full-time employed
0-1
60.7%
n/a
n/a
Retired
0-1
14.1%
n/a
n/a
Children aged 0-5
0-1
19.4%
n/a
n/a
Children aged 6-12
0-1
25.6%
n/a
n/a
Children aged 13-17
0-1
18.2%
n/a
n/a
attendance
Multiple paradigms – positive
and negative predicted effects
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Table 3: Multivariate logistic regression results
Variable
Model 1 Model 2
Model 3 Model 4 Model 5 Model 6
Demographics:
Demogr Resource Values
Social
Full
Parsim
aphics
measur
model
on-ious
s
only
Male
.820*
es
.746***
.959
.733**
model
.848
--
.547**
--
*
Black
.843
.960
.511**
.681*
*
*
Asian
.980
.769
,730
.660
.610
--
Latino
.539*
.554*
.407**
.496*
.415**
--
Age
1.078*
1.049^
1.067*
1.063*
1.059^
--
.999*
.999^
--
**
Age2
.999**
*
1.000
.999**
*
Resources:
Occupational prestige
1.013***
1.010*
--
Income
1.001
.999
--
1.163
--
Prosocial value
orientation:
Generative concern
1.405*
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**
Obligation to
1.255*
1.228*
1.280*
volunteer
**
**
**
Subjective religiosity
.935
.981
--
Role identity (lifetime
1.048
1.011
--
Role identity (high
2.111*
2.106*
2.080*
future volunteering)
**
**
**
1.236*
1.203^
--
1.162
1.317*
--
1.102*
1.047*
--
**
*
1.052*
1.041*
**
*
Informal socializing
1.119*
1.083^
with neighbors
*
contribution)
Social context:
Neighborhood safety
and quality
Social role:
Marriage
Social integration:
Social contribution
Social integration
--
--
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Multiple paradigms –
positive predicted
effects
Education
1.206***
1.169**
1.180**
1.148*
1.213*
*
*
**
**
Religious services
1.116*
1.099*
1.087*
1.142*
attendance
**
**
**
**
Religious meeting
1.134*
1.168*
1.129*
--
attendance
**
**
**
Voluntary association
1.181*
1.174*
1.199*
attendance
**
**
**
Multiple paradigms –
positive and negative
predicted effects
Part-time employed
1.359*
1.293
1.380^
--
Full-time employed
.811^
.713*
.717*
.580**
*
Retired
1.462*
1.343
1.293
--
Children 0-6
1.036
.983
1.080
--
Children 7-13
2.151***
2.352*
2.541*
2.388*
**
**
**
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Children 14-17
Constant
1.484***
.135**
.029***
*
1.372*
1.524*
1.496*
**
**
.003**
.002**
.000**
.013**
*
*
*
*
N
3004
3002
2930
2939
2911
3029
% Predicted (base)
63.1
63.1
62.5
62.3
62.2
63.2
% Predicted (model)
63.1
67.5
72.6
74.9
77.1
75.2
- 2 Log Likelihood
3928.9
3639.6
3201.3
3027.6
2830.1
3058.9
Total R2:
.013
.136
.284
.349
.407
.361
^ p ≤ .10
* p ≤ .05
** p ≤ .01
*** p ≤ .001
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Table 4: Bivariate logistic regression results
Volunteering
Exp(B)
R2
Male
.836*
.002
Black
.857
.001
Asian
.985
.000
Latino
.531*
.003
Age
.997
.000
Age +
1.077**
.006
Age2
*
Variable
Demographics:
.999***
Social context:
Neighborhood safety and quality
1.836**
.033
*
Prosocial and value orientation:
Generative concern
2.176**
.067
*
Obligation to volunteer
1.334**
.131
*
Subjective religiosity
1.624**
.037
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*
Role identity (lifetime contribution to
1.305**
others)
*
Role identity (expected future
3.101**
volunteering)
*
.017
.081
Resources:
Occupational prestige
1.028**
.044
*
Income
1.004**
.014
*
Social role:
Marriage
1.472**
.010
*
Social integration:
Social contribution
1.188**
.113
*
Social integration
1.141**
.089
*
Informal socializing with neighbors
1.288**
.031
*
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Voluntary association attendance
1.216**
.126
*
Multiple paradigms - complementary
effects
Education
1.225**
.072
*
Religious services attendance
1.177**
.076
*
Religious meeting attendance
1.300**
.066
*
Multiple paradigms – competing effects
Part-time employed
1.567**
.008
*
Full-time employed
.865^
.002
Retired
1.032
.000
Children 0-6
1.061
.000
Children 7-13
1.875**
.025
*
Children 14-17
1.526**
.009
*
^ p ≤ .10
* p ≤ .05
** p ≤ .01
*** p ≤ .001
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1
Full information about the sample, response rate, weighting, and survey design are
contained in the MIDUS codebook, available from the MIDUS website at
midmac.med.harvard.edu/research.html.
65