1

<LRH>McDonald and Mair

<RRH>Social Capital Across the Life Course

<AT>Social Capital Across the Life Course:Age and Gendered Patterns of Network

Resources1

<AU>Steve McDonald2 and Christine A. Mair2

<ABS>

Despite increasing research interest in network dynamics and cumulative

advantage/disadvantage processes, little remains known about how social capital varies across

the life course. While some researchers suggest that social capital increases with age and others

argue the opposite, this study tests these contradictory assertions by analyzing multiple indicators

of social capital from a nationally representative data set on working-age U.S. respondents. The

findings reveal evidence of both social capital accumulation and decline. Social resources from

occupational contacts tend to increase with age, but eventually level off among older

respondents. Changes in voluntary memberships follow a similar pattern. However, daily social

interaction is negatively associated with age. Overall, the results suggest that social capital

embedded in occupational networks tends to accumulate across the career, even in the face of a

general decline in sociability. The study also uncovers gender differences in these social capital

trajectories that are linked to the distinct life experiences of men and women.

<end ABS>

1Data cited here were collected in the thematic research project, �Social Capital: Its Origins andConsequences� (Grant No: AS-94-TP-Co2; Grant Period: 2004�2007). The project was conducted by theInstitute of Sociology, Academia Sinica, with pilot funding sponsored by the Research Center forHumanities and Social Sciences, Academia Sinica. The principal investigators are Nan Lin, Yang-chih Fu,and Chih-jou Jay Chen. Special thanks to Nan Lin for providing access to the data. An earlier version ofthis article was presented at the International Conference on Social Capital in Taipei, Taiwan. We areindebted to David Warner, Feinian Chen, Glen Elder, Nan Lin, Bonnie Erickson, and Ron Burt for thehelpful comments they provided on an earlier draft of the article. Also, thanks to Karen Cerulo and theanonymous reviewers for their comments and criticisms, which helped to substantially improve this study.

2

<KW>KEY WORDS: biographical and historical context; gender; life course; social capital;

social networks; work.

<H1>INTRODUCTION

Sociologists have recently focused much attention on identifying and explaining patterns

of resource accumulation across the life cycle (for a review, see DiPrete and Eirich, 2006).

Researchers have found, for example, substantial divergence in health (Dupre, 2007; Willson et

al., 2007) and economic resources (Alon and Haberfeld, 2007; Maume, 2004) over the course of

careers. However, little is known about life-course patterns of social capital�that is, resources

embedded within social relationships (Lin, 2001). Insight into trajectories of social capital would

aid in understanding how people navigate the various spheres of their lives (e.g., work, family,

and educational spheres). Furthermore, life-course trajectories of social capital may contribute to

broader patterns of cumulative advantage/disadvantage (O�Rand, 2006), as well as persistent

gender and race inequities, as many have argued (e.g., Bourdieu, 1986).

Despite the increased interest in these processes, few have explored the nuances of social

capital change across the life course; rather, most of the research on social networks has remained

preoccupied with examining how static network configurations and features explain various

outcomes (Smith-Doerr and Powell, 2005). Among the studies that have examined network

change, many focus on examining average changes across history or over time rather than linking

changes in networks to age-related processes. Consequently, such research lacks sensitivity to the

ways that social relationships are structured by age. Research in the social support literature has

examined age variation in social capital (e.g., Cornwell et al., 2008; Kalmijn, 2003), but these

studies examine strong ties to network contacts, which tend to be less salient than weak ties for

understanding attainment processes (Granovetter, 1973). The few studies that focus on change in

work contacts are limited in their generalizability and/or focus on single-item indicators of social

capital. In the end, the findings from the disparate literatures on social capital change over time

have led to a host of contradictory conclusions: some researchers have suggested that social

2Department of Sociology and Anthropology, Box 8107, North Carolina State University, Raleigh, NorthCarolina 27695-8107; e-mail: steve_mcdonald@ncsu.edu.

3

capital tends to accumulate with age (Bridges and Villemez, 1986), others have argued that social

capital tends to depreciate with age (Coleman, 1990; Kalmijn, 2003; Wellman et al., 1997), and

others have suggested both accumulation and decline (Erickson, 2003; Lambert et al., 2006).

Moreover, prior empirical research on variation in occupational contacts has paid relatively little

attention to how patterns of social resource accumulation might vary by gender, despite the

importance of these differences for explaining gender inequality.

The current investigation attempts to overcome the limitations of prior research by using

a nationally representative sample of U.S. residents age 22�65 to examine age variation in social

network resources. Our examination is focused on (but not limited to) occupational network

resources measured via the position generator approach to collecting network data (Lin et al.,

2001; Lin and Dumin, 1986). Prior research indicates that these work-related connections are

highly associated with status attainment (Lin, 1999), whereas emotionally strong connections and

neighborhood contacts can have negligible or even deleterious effects on status attainment

(Elliott, 1999; Smith, 2000). The data analyzed here contain detailed information on the

characteristics of occupational network members, although the cross-sectional nature of the data

limits our ability to distinguish aging trajectories from period and cohort effects. Nonetheless, the

findings are consistent with prior research and robust across various indicators of social capital.

The results suggest that highly valued work-related social resources tend to accumulate over the

course of careers, with diminishing levels of social capital among the most senior respondents.

Voluntary association membership follows an identical pattern. At the same time, the average

amount of daily contact is negatively associated with age. This highlights an important paradox�

that the overall level of social interaction declines across the adult life course, while employment

and work connections expand. The analyses also identify many gender differences in the form of

the relationships between age and social capital, suggesting that gendered careers structure social

capital accumulation processes. The gender differences offer little support for the notion that

women�s work-based social resource trajectories are any more sporadic than men�s, although

patterns of daily contact do appear to be more discontinuous for women than for men.

The article proceeds with a theoretical justification for our analyses. We link the study of

social capital as network resources (Lin, 2001) to a life-course approach aimed at understanding

changing lives within biographical and historical context (Elder and Shanahan, 2006). Next, we

4

describe the empirical research literature on change in social capital and social networks across

time, focusing on the research that has examined age-based social capital trajectories. Based on

the findings from these studies, we develop a set of hypotheses regarding age and gender

variation in social capital, which serves as the basis for our empirical examination.

<H1>SOCIAL CAPITAL AND THE LIFE COURSE

Noting the static character of network studies, Smith-Doerr and Powell (2005) emphasize

the need to incorporate principles of the life course into the study of social networks and

economic life. The life-course perspective is highly compatible with a relational approach to

understanding human behavior (see Emirbayer, 1997). Life-course theory explicitly notes that

lives are lived interdependently (Elder and Shanahan, 2006). An individual�s opportunities are

linked to the fortunes of other people with whom he or she is associated. A life-course

perspective is also useful for understanding how access to social capital is embedded within a

broader set of social and historical processes. Specifically, the theory recognizes that aging is a

lifelong process that is path dependent and cumulative in nature (Elder and Shanahan, 2006;

O�Rand, 2006). People tend to accumulate experiences and resources as they age. Of major

interest then are life-course trajectories�the sequences of related events or experiences in

individual lives (Elder, 1985). Life-course trajectories set people on specific paths in life, helping

individuals to garner experiences, skills, and resources that ultimately structure future

opportunities and decision making. Though path dependent, aging is an agentic process�

individuals are capable of making subtle or even dramatic changes to the paths they are on (Elder

and Shanahan, 2006). Transitions refer to the distinct events in life that denote changes in the

trajectories of social roles. Life-course research is broadly interested in the timing, duration, and

sequencing of trajectories and transitions in people�s lives.

In recent years, life-course research has paid increasing attention to issues related to the

advantages and disadvantages that tend to accumulate as people age (DiPrete and Eirich, 2006).

O�Rand (2006) describes four specific types of capitals (human, social, health, and personal) that

accrue to individuals over the life course. She explains that researchers need to learn more about

the trajectories of these life-course capitals and how they are related to wealth, well-being, and

5

mortality. Also, the specific types of trajectories�such as time based or age based�matter a

great deal. To understand inequality, it is necessary to understand how the accumulation,

maintenance, and erosion of resources are linked to age-related processes. Resource accumulation

and decline are associated with aging processes�which result from the consequences of getting

older�as well as the various events and experiences (such as education, employment,

childbearing, and retirement) that do not occur randomly throughout life, but are instead

fundamentally structured by age (cf. Settersten, 2003). Therefore, research on life-course capitals

should examine age-based trajectories of life-course capitals that follow changes in resource

accumulation as people age. Age-based trajectories are distinct from time-based trajectories,

which describe changes in resource levels over a fixed period of time that is not linked to aging

(George, 2009), or from historical trajectories, which assess changes in attributes across calendar

time. Age-based trajectories are useful because they anchor changes in resource levels to the

context of aging, linking the possession of resources to the life stages people experience.

This study explores age-based trajectories of social capital. Consistent with other scholars

(e.g., Flap and Volker, 2001; Lin, 2001), we view social capital as a set of resources accessible

through social networks. People invest in social relationships for both instrumental and

expressive purposes and tend to receive a variety of benefits from developing and maintaining

these relationships. As such, the resources embedded in social networks (such as information,

influence, status, and support) serve as social capital from which the individual may further invest

in or draw on when needed (Lin, 2001). The theory is not explicit about how social capital might

vary across the life course; it only notes that individuals seek to invest in their social relationships

whenever possible. Although interest in social network dynamics has increased in recent years,

relatively few studies to date have empirically examined change in social capital as people age.

Among those studies that have explored changes in social capital, the changes have rarely been

linked to age-related processes. The approaches to studying social capital change are briefly

outlined here.

6

<H1>HISTORICAL AND TIME-BASED TRAJECTORIES OF SOCIAL CAPITAL

Much empirical research has examined how social capital has changed over time. For

example, Putnam�s (2000) contention that social capital is on the decline in the United States has

garnered a great deal of attention in the empirical research literature. This line of research focuses

on trends in social relationships, trust, and voluntary association memberships across historical

time rather than focusing on how social capital changes across the life course (see, e.g., Paxton,

1999). The same can be said for the more recent debate on the extent of social isolation in the

United States, which maps historical changes in social networks across several decades

(McPherson et al., 2006). Consequently, these studies attempt to answer questions about

historical trajectories rather than age-based trajectories of social capital. Other studies have

examined change in occupational networks across historical time. For example, researchers have

examined how the transition from state socialism to a market-based economy influenced the

nature of occupational networks and their effectiveness in Eastern Europe (Angelusz and Tardos,

2001; Yakubovich and Kozina, 2000).

Another line of research examines changes in networks over time, but does not link these

changes to age or to history. Drawing from a sample of investment bankers, Burt (2000) finds that

social ties are subject to a high level of �decay� or disintegration. Newly formed ties and ties that

are weakly embedded in a given network are more likely to decay (or tend to decay at a faster

rate) than other ties (Burt, 2000, 2002). Social capital tends to accumulate for those who start out

with high levels of social capital and decay is less frequent among those with strong social

capital, such as high-performance bankers. Although these studies are the only examples that

describe and explain individual change in occupational networks over time, they do not address

the issue of change in social capital across the life course, as the ages of the workers in the sample

varied widely.

<H1>AGE-BASED TRAJECTORIES OF SOCIAL CAPITAL

Research on friendship and social support networks has focused more attention on life-

course patterns of social capital. Research in this tradition has examined the ways age, family

7

transitions, career transitions, and historical events affect network configurations. The results

show that life-course transitions can result in major network disruption. Friendships are formed

quickly and easily at young ages (Cairns and Leung, 1995). Drastic transitions, such as those

from grade school to junior high, for example, result in a major upheaval of social networks,

leaving some individuals vulnerable to a lack of emotional support (Cantin and Boivin, 2004).

Prior research suggests that friendship networks tend to shrink as people age (Kalmijn,

2003; Wellman et al., 1997). Socioemotional selectivity theory suggests that older individuals

who have accumulated knowledge and information over the life course are less likely to engage

in social interaction for the sake of information sharing as they age (Carstensen, 1995). Labor

force participation results in more homogeneous networks (Bidart and Lavenu, 2005) and greater

network density leads to higher retention of contacts over time (Martin and Yeung, 2006).

Network reduction with age may therefore reflect the reduction of information-based ties, such as

work acquaintances, rather than the loss of emotion-based ties, such as confidant friendships,

which tend to persist across the life course (Fung et al., 2001). Despite a general decline in

contacts across the life course, older individuals tend to experience increases in neighborhood

socializing, religious attendance, and volunteering from age 57 to 85 (Cornwell et al., 2008) and

older individuals demonstrate a high level of flexibility and adaptability in the maintenance of ties

over time (Wenger and Jerrome, 1999).

Friendship network structure and composition also varies by gender. Women tend to have

greater contact with friends and family than do men (Kalmjn, 2003). Friendship networks also

fluctuate across time as people experience more pronounced changes in their relationships

following major life-course transitions, such as marriage and parenthood. In general, cohabitation

and marriage are associated with a net decline in network friends and contacts (Bidart and

Lavenu, 2005; Fischer and Oliker, 1983; Kalmijn, 2003). At the same time, the percentage of

friends and contacts that a couple shares between each other increases over time (Kalmijn, 2003).

For women, compared to men, the transition to parenthood is associated with a lower percentage

of friend overlap with a spouse (Kalmijn, 2003).

Gendered patterns of labor force participation also influence network characteristics

across work careers. Women tend to have more discontinuous work histories than do men (Han

and Moen, 1999), which can result in lower financial returns and poorer occupational mobility

8

(Parks-Yancy et al., 2006). Also, women�s social capital is particularly vulnerable to changes in

socioeconomic status (Ajrouch et al., 2005). Stronger attachment to the workplace among men

affects their participation in volunteer organizations, an important form of social capital (Son and

Lin, 2008). Men�s work histories are strongly tied to their organizational associations (Rotolo and

Wilson, 2003) and men are less likely than women to participate in expressive forms of civic

action, such as community preservation (Son and Lin, 2008). Although women entering the labor

force gain more opposite-sex friends in their networks compared to men (Kalmijn, 2002), women

are significantly less likely than men to obtain a job through a social capital resource (Parks-

Yancy et al., 2006). Women are more likely to be enmeshed in kin-based networks (Fischer and

Oliker, 1983; McPherson et al., 2006) and even professional women are less likely than men to

socialize with co-workers due to kin-related time constraints at home (Walker, 1990). However,

some research suggests that women employed in higher-status positions tend to retreat from kin-

based networks and become more involved in caring for friend-based contacts (Gerstel, 2000),

many of which may have been formed in the workplace. Yet despite this pattern of reduced

proportion of kinship ties, women in high-status jobs may continue to be isolated from elite

networks (Moore, 1988). Qualitative research on the general population shows that men still have

greater access to social capital and reap more benefits from it (Parks-Yancy et al., 2006), an

effect that likely accumulates across the life course.

Other structural changes related to life-course events, such as parenthood or retirement,

may actually benefit female networks. Although children can be a significant drain on financial

capital, they may act as a form of social capital (Schoen and Tufis, 2003), motivating both

married and single individuals to participate in parenthood (Schoen et al., 1997). Parenthood can

facilitate access to new types of social networks via child-care facilities, informal play-groups,

and other volunteer associations. Because of the gendered division of labor within the home,

women may have better access to volunteer memberships associated with parenthood than will

men. Retirement is also an important life-course transition with gendered outcomes, as it brings

an increase in social ties for women as they gain more friendship connections. Since men are

more likely than women to have social networks based primarily in the workplace, they may

experience network reduction in the transition to retirement (Fischer and Oliker, 1983).

9

To summarize, this line of research demonstrates that social support networks (1) tend to

shrink as people age, (2) are sensitive to life-course transitions, and (3) are structured by gender

and family/work career trajectories. There are, however, a number of limitations of this research.

First, these studies have tended to rely on data from age-specific samples (e.g., Cairns and Leung,

1995; Cornwell et al., 2008), which are unable to fully assess age variation in social capital.

Second, the studies have questionable generalizability for the U.S. context, since the samples are

typically not nationally representative. Third, none of these studies have directly addressed issues

related to changes in occupational networks over time. Rather, these studies focus exclusively on

changes in close friendship ties rather than work-related ties.

The existence and effectiveness of work-related social connections should vary across the

life course, as suggested by research that has examined the importance of timing for conditioning

the effects of social capital. For example, prior research demonstrates that the impact of social

connections on employment outcomes depends on relationship duration (Rhee, 2007), on the age

of the respondent (McDonald and Elder, 2006), and on the interaction between age and

relationship typology (Moerbeek and Flap, 2008).

The number of studies that have examined variation in work-related social resources

across age is quite small, but they all suggest a curvilinear relationship. Research on a sample of

white-collar workers finds that younger and older individuals tended to have the least amount of

diversity in their networks (Lambert et al., 2006), suggesting a curvilinear relationship between

social capital and age. Using data from the Canadian Federal Election Study, Erickson (2003)

also finds that diversity in occupational resources tends to increase with age and then declines

over time. Age-related differences in social capital have also been explored with respect to

voluntary organization memberships, which are often linked to occupational environments and

careers (Rotolo and Wilson, 2003). Putnam�s (2000) analysis of the General Social Survey shows

an inverted U-shaped relationship between age and voluntary association memberships. Popielarz

and McPherson (1995) examine turnover in voluntary group memberships, finding that turnover

decreases with age at a decreasing rate.

These studies are useful because they point to patterns of the accumulation and decay of

work-related contacts over the life course and the contingency of social capital effects on the

timing of employment transitions and relationship formation. However, the evidence is far from

10

conclusive. As with the social support literature, the studies are subject to questionable

generalizability to the U.S. context due to nonrepresentative and/or age-specific sampling. Also,

the analyses tend to rely on singular indicators of social capital, which make the results

vulnerable to idiosyncratic variation in measurement, a serious concern when attempting to

measure a complex and multidimensional concept like social capital. Furthermore, the results

from the two literatures imply that work-related and non-work-related social resources may move

in opposite directions across the life course, which highlights the need to examine both forms of

social capital simultaneously. Finally, more attention needs to be focused on gendered patterns of

network change over time. Given the substantial differences in the life-course experiences of men

and women and the consequences for gender inequality, research should examine how trajectories

of social capital vary for men and women.

<H1>HYPOTHESES

The empirical investigation presented here attempts to build on these prior studies by

utilizing a nationally representative sample of the United States to examine age variation in social

capital and how these life-course patterns vary by gender. We explore a number of

operationalizations of social capital. To begin with, we focus on resources embedded in

occupational networks because (1) relatively little research has examined change in occupational

networks across the life course and (2) these resources are most salient for attainment processes.

In addition to examining the quantity and quality of occupational networks, we also analyze a

number of other features of occupational networks: gender, closeness, density, and trust. The age

patterns for these occupational network resources are also compared to changes in daily contact

and voluntary association membership.

First, we hypothesize that social capital varies across the life course. Based on evidence

from prior empirical investigations, we expect to find a steady accumulation of resources

embedded in occupational networks, though these resources should level out and decline at more

advanced ages (Erickson, 2003; Lambert et al., 2006). Second, research on friendship and social

support networks has found the reverse: declining socialization with age (Kalmijn, 2003;

Wellman et al., 1997). Therefore, we hypothesize that age is negatively associated with daily

11

contact. Finally, prior research suggests that men and women display unique patterns of

socialization over the life course due to gendered careers (Fischer and Oliker, 1983; Han and

Moen, 1999; Kalmijn, 2002). Consequently, we expect to find gender differences in the

relationship between age and social capital. More specifically, the relationship between age and

social capital should be mostly linear for men, whereas the relationship between age and social

capital among women should be more often nonlinear or insignificant, given the greater

discontinuities of women�s work career patterns relative to men. These hypotheses are formalized

below.

<H>

H1: On average, the relationship between age and work-related social capital should be

curvilinear, with social capital increasing across the career followed by leveling off and declining

at advanced working ages.

H2: On average, the relationship between age and daily contact should be negative.

H3: Men and women should experience distinct age-based social capital trajectories, with

men experiencing linear patterns of social capital accumulation/decline and women experiencing

nonlinear patterns of social capital accumulation/decline.

<end H>

<H1>DATA AND METHODOLOGY

We use data from the Social Capital-USA (SC-USA) survey. SC-USA is a national,

random-digit-dialed (RDD) telephone survey of adults in the United States age 22�65 who were

currently or previously employed. The survey was specifically designed to examine social capital

and was part of a three-society (the United States, China, and Taiwan) study, jointly sponsored by

Academia Sinica, Taiwan, and Duke University. The survey was administered from November

2004 to March 2005, averaged 35 minutes to complete, and resulted in 3,000 completed

interviews. The response rate (43%) is comparable to other recent national RDD surveys (see

Groves et al., 2004). To assess the potential bias due to nonresponse, parameter estimates from

12

SC-USA were compared to those obtained from the March 2005 Current Population Survey

(limited to respondents age 22�65). Few significant differences were found, although SC-USA

respondents are more highly educated than the CPS respondents. Therefore, sample weights were

constructed (using the rake procedure in Stata) to match the gender, race, age, marital status, and

education characteristics of SC-USA to the CPS estimates. The sample weights are employed in

the analyses presented here to ensure generalizability. Five cases do not have valid weights due to

missing values and are therefore excluded from the analyses.

<H2>Measurement

The weighted means for the variables used in this analysis are presented in Table I. This

study focuses on age and gendered patterns of social capital. Age is calculated based on the self-

reported year of birth. The sample ranges from age 22 to age 65. We compute squared and cubic

age terms to capture curvilinear variation in the dependent variables across age. Gender also

serves as an independent variable in the analysis and is measured as a dummy variable (female =

1, male = 0).

[NOTE: Place Table I near here.]

The remaining eight variables are measures of social capital and serve as the dependent

variables for the analyses. Most of these variables are drawn from the position generator

methodology, which measures the characteristics of occupational networks (Lin and Dumin,

1986; Lin et al., 2001). The position generator measures resources embedded in occupational

social networks by identifying access to contacts in a list of 22 occupations. Each respondent is

asked: �Do you know anyone among your relatives, friends, or acquaintances that has one of the

following jobs? (�Knowing� means that you and the person can recognize and greet each other. If

you know several persons that have a particular job, please name the person that comes to mind

first.)� The list of jobs is designed to capture the scope of positions in the occupational hierarchy:

from janitors and hairdressers to lawyers and CEOs. Upon identifying a contact with a particular

13

job, respondents are asked a series of follow-up questions about the characteristics of the position

occupant.

We calculate a number of variables based on these questions. First, we calculate a

measure of social resources that taps into the quantity and quality of positions accessed through

occupational networks. Extensity is a measure of the total number of positions accessed by

respondents. Quality is measured by upper reachability of network contacts�that is, the highest

prestige score of the occupations to which respondents had access (using the Standard

International Occupational Prestige Scale). Extensity and upper reachability are highly correlated

(r = .62) and are therefore combined into a single social resource index using principal

component factor analysis with varimax rotation. The factor score was recoded such that values

range from 0 to 5.96, with a mean value of 3.36.

We also include a measure of the proportion of contacts mentioned in the position

generator. The variable ranges from 0 to 1 and is equal to the number of male contacts mentioned

divided by extensity. Male contacts serve as an important source of social capital in the work

sphere given persistent occupational sex segregation (Tomaskovic-Devey et al., 2006) and the

relatively low authority levels maintained by women in the workplace (Smith, 2002).

Consequently, male contacts are more likely to provide access to high-wage jobs (Smith, 2000).

Respondents were also asked about how close they are to each person they mentioned. The

closeness variable was recoded (such that 1 = not close at all, 2 = not close, 3 = so, so, 4 = close,

and 5 = very close) and the average closeness of all contacts mentioned in the position generator

was calculated. The resulting continuous variable ranges from 0 (for individuals with access to

zero positions in the generator) to 5 (for people who reported only very close occupational

contacts).

Density of occupational networks is gleaned from a question asking: �Among the above

people you mentioned [in the position generator], how many know each other?� Higher values on

the density variable indicate a greater degree closure among network contacts. This ordinal-level

variable ranges from a value of 0 (no contacts know each other) to 4 (all contacts know each

other). Trust is measured in a similar way. Respondents were asked: �Among the above people

you mentioned [in the position generator], how many can be trusted?� The values on the trust

variable range from 0 (none can be trusted) to 4 (all can be trusted).

14

We also examine a question on daily contact, which identifies the number of people that

each respondent interacts with in a typical day. The variable spans from a value of 1 (0�4 people)

to 6 (more than 100) and has been shown to be related to expressive and instrumental network

dimensions of other social capital indicators (Fu, 2005). A follow up question asked: �Among

those [daily contacts], how many of those do you make contact with because of work?�

Responses vary from �almost none� (0) to �almost all� (4). This variable serves as an indicator of

work-related contacts. Finally, we include a measure of the number of memberships in voluntary

organizations, which is a count of up to 10 different types of organizations. These include

memberships in political parties, labor unions, professional organizations, religious groups,

leisure/sports/cultural groups, charities, neighborhood organizations, school/PTA organizations,

ethnic/civil rights organizations, or other voluntary organizations.

In the subsequent analyses, the sample size depends on the missing values on the

dependent variables, since the independent variables (age and gender) contain no missing values.

The only variable with substantial missing data (i.e., greater than 7%) is the work-related contact

variable, which is explained by the fact that responses from nonemployed individuals were coded

as �not applicable.� Therefore, the results for work-related contact refer only to currently

employed individuals.

<H2>Analysis

The analysis is divided into three steps. First, the social capital trajectories are presented

graphically to provide a visual representation of how social capital varies across the life course.

Second, the form of the relationship between age and social capital is modeled using ordinary

least squares regression (for continuous dependent variables) and ordinal logistic regression (for

ordinal dependent variables). Third, analyses are split by gender to identify similarity/difference

in the form of the relationships between age and social capital. The figures and tables are

organized according to these three steps, but we describe all three steps for each dependent

variable before moving to the next variable. The dependent variables derived from the position

generator questions are presented first (social resources, proportion male contacts, closeness,

density, and trust). These variables refer specifically to social capital embedded in the

15

occupational networks of respondents. The remaining dependent variables refer to a broader set

of social interactions (amount of daily contact, proportion of daily contact that involves work) as

well as group membership in voluntary organizations. To reiterate, these variables are used to

assess the extent to which: (1) work-based social capital tends to accumulate with age and then

decline among older individuals; (2) daily contact tends to decline across the life course; and (3)

women experience greater discontinuity than do men in their patterns of social capital

accumulation.

<H1>RESULTS

Figures 1 and 2 present visual evidence of how social capital varies across different age

groups. The first figure (Fig. 1A) [AUTHOR: Please note that Fig. 1 is separated into only 1B

and 1D.] graphs the relationship between age and the social resources factor score. Social

resources increase steadily with age, though additional returns to age tend to diminish to the point

where social resources peak (age 46�50) and then drop off among the 50+ population. This

pattern is consistent with our expectations. Interestingly, there is a slight uptick in social

resources among the oldest age group. The functional form of the relationship is modeled using

ordinary least squares regression (see Table II). The best-fitting model includes both a linear age

term and a squared age term. The significant positive age effect captures the pattern of social

resource accumulation with age, while the significant negative age squared effect highlights the

diminishing returns to age among the older respondents. The summary from Table III shows that

this pattern holds for both males and females in the sample. This is illustrated by the positive

linear age term, negative squared age term, and insignificant cubic age term.

[NOTE: Place Figs. 1 and 2 and Tables II and III near here.]

The relationship between age and the proportion of male contacts is displayed in Fig. 1B.

Again, the results reveal a curvilinear relationship: initial steep decline in male contacts followed

by a steady increase to the oldest age group. Overall, this evidence is consistent with prior

expectations. The best-fitting form of this relationship is a positive linear relationship that

16

captures the steady increase in male contacts with age. The results in Table III, however, show

that this relationship is significant for women only. For men, the relationship between age and

proportion of male contacts is statistically insignificant.

The average closeness of occupational networks tends to diminish over time (see Fig.

1C). [AUTHOR: Please note that Fig. 1 is separated into only 1B and 1D.] After an initial rise

from age 22�26 to 27�30, closeness declines and tends to level off around age 46�50. The

significant negative linear age effect best captures this pattern of declining closeness across age

groups (see Table II). The relationship between age and closeness, however, is conditioned by

gender. Only men experience this significant decline in closeness across the life course. Patterns

of network density across age are similar to closeness. Figure 1D shows a general decline in

density, with a steep drop between ages 27 to 40. The decline in closeness and network density

across the life course provides further evidence of the accumulation of social capital in the form

of weakly tied and loosely connected network contacts. Using ordinal logistic regression, we find

that the best fit for the form of the relationship between age and density uses both linear and

squared age variables. The significant negative effect captures the rapid decline in density in the

first half of the work career, whereas the positive squared term illustrates the leveling off of the

effects during the second half of the work career. The gender-specific models (see Table IV)

[AUTHOR: Note that no Table IV was included in my copy of the manuscript.] indicate that

the curvilinear relationship is best for men only. For women, density declines across the career

with less leveling at the end of the career than for men.

[NOTE: Place Table IV near here.]

[AUTHOR: Note that no Table IV was included in my copy of the manuscript.]

Figure 2A [AUTHOR: Please note that Fig. 2 is separated into only 2B and 2D.]

shows that, in general, older respondents are more likely than younger respondents to perceive

their occupational contacts as trustworthy. However, the increase in trust is not consistent across

all age groups. There is an early rise in trust from age 27 to 45, followed by a sharp drop in trust

for the 46�50 age group, and then a steady rise in trust again among the oldest age groups. The

linear pattern of increasing trust across age is confirmed with ordinal logistic regression (see

17

Table III). The results show that, on average, each additional year is associated with higher levels

of trust in occupational networks. Splitting out the results by gender, however, shows that

increasing trust with age is significant for women, but not for men.

The results also show a general decline in the amount of daily contact in which people

engage, though again the pattern is not entirely linear (see Fig. 2B). The highest levels of daily

contact are among the youngest age group. Daily contact dips among 27 to 35 year-olds, then

rises among 36 to 40 year-olds, and steadily declines thereafter. Using OLS regression, the age

pattern of daily contact is best captured by a negative linear term for age (see Table III). Further

analyses summarized in Table IV [AUTHOR: Note that no Table IV was included in my copy

of the manuscript.] reveal gender differences in the form of the relationship between age and

daily contact. For men, the daily contact relationship is best described as linear. For women, the

best-fitting model contains a negative coefficient for the linear age variable, a positive coefficient

for the age * age variable, and a negative coefficient for the age * age * age variable (all of which

are statistically significant). In short, this model predicts an S-curve characterized by an initial

decline, then increase, and finally a decline in daily contact across age. This suggests greater

fluctuation in the daily contact of women across the life course.

Age patterns of daily social interaction are distinct from changes in work-based

interaction. Figure 2C [AUTHOR: Please note that Fig. 2 is separated into only 2B and 2D.]

tracks the proportion of daily contact that is work related and shows a pattern that is more

consistent with what was found for the variables constructed from the position generator. That is,

the proportion of work-related contact tends to increase with age and levels off around age 46. In

spite of the diminishing returns, the ordinal logistic regression model with a single linear age

variable produced the only statistically significant coefficient. Both men and women display

similar linear increases in work-related contact across age.

Finally, we examine age variation in the number of voluntary organization memberships.

Here, the figure shows steady increases in memberships early in the life course, diminishing

returns and a reversal of the trend during the middle years, followed by a slight uptick in

memberships among the oldest age group. This pattern is strikingly similar to what was found for

the social resource factor score. Results from OLS regression show that the relationship is best

described with a positive linear age term and a negative squared age term, which captures the

18

parabolic form of the relationship between age and memberships. Further elaboration by gender

in Table IV [AUTHOR: Note that no Table IV was included in my copy of the manuscript.]

shows a similar relationship for both men and women.

<H1>DISCUSSION

The current study investigates age variation in social capital and the extent to which these

patterns may be conditioned by gender. The analyses are admittedly more descriptive than

explanatory. However, unlike any previous research, our study maps the broad patterns of social

capital variation across the life cycle in a sample nationally representative of the United States.

Furthermore, we use a variety of different social capital indicators and ultimately identify the

general features of social capital accumulation and decline. The results are generally consistent

with our hypotheses. First, social capital embedded in occupational networks tends to accumulate

with age, but eventually levels off (and may also decline) in later ages. Second, daily contact

tends to decline with age. Third, the social capital trajectories generally display distinct patterns

by gender, although the trajectories of work-related social capital of women appear to be no more

discontinuous than those of men. We offer a more explicit interpretation of the results below.

<H2>Age Variation in Social Capital

Both the quantity and quality of occupational networks (as measured by the social

resource factor score) tend to increase with age. Therefore, this finding suggests that social capital

tends to accumulate across the life course. These findings are consistent with prior research

suggesting that work-based social capital increases with experience (Lambert et al., 2006).

However, social resources also appear to level off or decline in later ages (Erickson, 2003). The

age-based trajectory for the social resources factor score is virtually identical to what was found

for voluntary organization memberships. Recent research has identified voluntary organizations

as useful sources of social capital. Extensive work histories and attachment to the labor force are

associated with participation in volunteer organizations (Rotolo and Wilson, 2003; Son and Lin,

19

2008). Voluntary organizations and associations provide opportunities for developing diverse

occupational contacts.

At the same time, the results for social resources and for voluntary memberships reveal a

slight uptick in social capital among the 56�65 age group. This finding is consistent with recent

evidence from the National Social Life, Health and Aging Project (NSHAP), which show an

increase in volunteerism among recent retirees (Cornwell et al., 2008). Also, Moen (2005) notes

that work careers often involve �second acts,� during which people retire from high-pressure jobs

to take on more flexible, enriching work roles. The engagement in �second acts��whether this

takes the form of new work roles or increasing group membership�often results in the making of

new friends and acquaintances. As such, the later life increases in social resources and

organizational memberships are likely explained by life-course patterns of career renewal or

retirement-related transitions. Alternatively, the increase in social resources and volunteer

memberships among the older respondents may also reflect generational differences, resulting

from greater overall civic involvement of the World War II �greatest generation� (Mettler, 2007).

The positive association between age and access to male occupational contacts provides

further support that social capital tends to accumulate across the work career. Female-dominated

occupations (e.g., service and clerical) are common in the early part of careers, while increasing

occupational attainment may lead individuals to male-dominated workplaces, which offer greater

wages and benefits. The steady accumulation of male contacts after age 30 could indicate

movement from female-dominated job types to male-dominated job types with age. Alternatively,

this pattern may reflect changing labor market conditions over time. For example, the decline in

male-dominated manufacturing jobs, the rise in the female-dominated service sector, and

increasing female labor force participation could all contribute to lower proportions of male

contacts in occupational networks among younger cohorts. The relatively high proportion of male

contacts among young adults age 22�26 is more difficult to explain. This may be related to

gender differences in labor force participation or networking behavior among this age group.

Closeness and density are negatively associated with age, further suggesting social capital

accumulation. Both factors are empirically correlated and strength of ties has often been used as a

proxy measure of network density (Burt, 1992). Decline in closeness suggests a general

weakening of ties to occupational contacts across the life course. Weak ties are useful in labor

20

market contexts since they are more likely to provide access to nonredundant information about

employment opportunities (Granovetter, 1973). Further, less dense networks contain structural

holes (Burt, 1992), which are useful for brokering exchanges. The findings suggest that as

individuals gain greater work experience, their occupational networks expand and diversify,

resulting in broader yet more weakly tied networks. Although closeness and density tend to

decline with age, there is evidence of a closeness peak during the late 20s. This early peak may

reflect a hold-over effect from close school-related friendships that later diminish (Cantin and

Boivin, 2004).

Trust in occupational contacts is positively associated with age, which could be

interpreted two ways. Individuals could develop greater trust in their contacts over time or they

could eventually rid their networks of untrustworthy contacts. Both processes (which are not by

necessity mutually exclusive) would result in an increase in average network trust. Regardless of

the process involved, increases in trusting relationships can provide greater access to social

capital, as trust often facilitates the receipt of instrumental support and career sponsorship

(Seibert et al., 2001; Smith, 2005). The findings from the SC-USA data also show a drop in trust

around age 46, which might reflect a historical shift in trust. However, the trend does not mesh

with prior assertions that trust in others has declined in successive cohorts (Putnam, 2000), which

would predict that the later cohorts would have substantially higher levels of trust in their

network contacts than earlier contacts.

Whereas most of the indicators reveal a pattern of social capital accumulation with age,

daily contact is negatively associated with age. This finding is consistent with prior research that

also suggests that social interaction tends to decline with age (Kalmijn, 2003). At the same time,

daily contact that is directly related to work activities increases over time and levels off around

middle age. When taken together, this reveals an interesting paradox: that social interaction

generally declines, but work contact and voluntary organization membership tend to increase

across the life course. This may seem counterintuitive at first glance. However, engaging in social

interaction is quite different from knowing people and being affiliated with groups. The results

presented here suggest that while social networks and organizational affiliations tend to expand

across the life course, people tend to interact with other individuals less frequently as they age.

21

When taken together, the results offer strong and consistent evidence of age-based

structuring of social capital. While we do not deny that generational differences influence life-

course patterns of social capital, Putnam�s (2000) generational hypothesis is unable to fully

explain the patterns identified here due to several inconsistencies. First, the negative association

between age and daily contact runs directly counter to the idea that earlier generations engage in

more social interaction that younger cohorts; rather, these findings are more consistent with the

results from research on age-related patterns of social support. Second, the timing of generational

changes does not match the findings for the other dependent variables. Putnam (2000) argues that

the Boomer generation, born beginning in 1946, represents the beginning of the decline in social

capital and finds that successive generations report lower levels of social capital. Yet the results

presented here (and in subsequent analyses) show that social capital does not always increase

steadily from younger ages to age 59 (the edge of the Boomer generation). Specifically, the

unexpectedly low levels of social capital observed among the 51�55 age group for the social

resources, density, and voluntary organizations variables seems inconsistent with the generational

hypothesis, as does the low level of social capital among the 46�50 year-old group on the trust

variable. Furthermore, our results are strikingly similar to Putnam�s own analysis of the 1972�

1994 GSS data, which reveal a similar pattern of increase in voluntary organization memberships

with age, followed by decline and slight uptick immediately prior to retirement (Putnam,

2000:249). The consistency between the results from the GSS and the SC-USA data highlights

the importance of age-based explanations for social capital change across the life course.

<H2>Gendered Trajectories of Social Capital

The relationship between social capital and age is moderated by gender and may be

explained by the differential life-course experiences of men and women. While there is evidence

of gender consistency in the relationships between age and our social network measures, gender

divergence in these relationships is more common. For example, we find that the proportion of

male contacts increases with age for women but the effect for men is not statistically significant.

This finding helps clarify how social resources accrue differently to men and women by virtue of

their distinctive patterns of social participation in occupational environments across the work

22

career. Women begin with a deficit in male occupational contacts relative to men, but the gap

closes as they age. The early career gap in male occupational contacts may help explain the

slower rates of early promotion among women. The results presented here are consistent with

prior research. As noted earlier, Kalmijn (2002) has found that labor force attachment increases

the number of opposite-sex friends for women, but has no impact on the networks of men. Prior

research also indicates that women often move from female-dominated into male-dominated

employment, especially after experiencing life-course transitions such as having children or

exiting the work force due to family care-giving responsibilities (Rosenfeld and Spenner, 1992).

As women garner more work experience and climb the occupational hierarchy, they are also

likely to have more frequent interaction with male contacts. In the end, the increasing number of

male contacts among women could be due to changes in occupational environments (such as

women moving into male-dominated work environments) or to differential patterns of social

interaction (whereby women garner greater social legitimacy among their male co-workers) (see

Burt, 1998). Or both processes might operate simultaneously. Of course, these prior studies are

merely suggestive of the mechanisms driving these findings; panel analyses would be needed to

provide a more definitive explanation.

Feelings of closeness with occupational contacts decline with age, but only for men.

Trust in network contacts increases with age for women, while trust is unrelated to age for men.

The closeness and trust findings highlight the relative strength of ties between women and their

contacts (Fischer and Oliker, 1983; McPherson et al., 2006). However, these results also indicate

that the overall greater feelings of closeness and trustworthiness among women may be linked to

network changes occurring over the life course. Women may be more likely than men to maintain

their close connections to work contacts and to construct occupational networks of highly trusted

contacts. Alternatively, men may be more likely than women to develop brokerage opportunities

by garnering weak occupational connections. As for network density, both men and women

experience a general decline as they age, though men experience a small increase in network

density during the latter part of careers. This may serve as an indication of the formation of �old

boy� networks among men in later life�that is, dense male networks of influence that control

access to high-status positions within society (Saloner, 1985). Unfortunately, the nature of the

data makes it impossible to identify whether these patterns reflect gender differences in the

23

maintenance of existing relationships or in the development of new relationships. Such issues

would be important to explore in future research.

The above results indicate that men and women experience differing trajectories of social

capital; however, several other social capital indicators show no differences in the patterns for

men and women: social resources, work-based daily contact, and the number of organizational

memberships. Moreover, there is little evidence of greater discontinuity in women�s work-based

social capital trajectories relative to those of men. However, the findings indicate that while daily

contact declines with age at a linear rate for men, women experience an increase in daily contact

in mid-life. This is likely due to the mid-career transitions more commonly experienced by

women�such as childbearing and changes in career paths�which lead women into new social

milieus and have the potential for fostering an increase in social interaction (cf. Schoen and Tufis,

2003). Women often experience more discontinuities in their occupational trajectories compared

to men (Campbell, 1988), which leads to gendered patterns of social capital accumulation.

<H2>Future Directions

The findings presented here provide many opportunities for further elaboration. First, the

paradox of social resource accumulation coupled with the decline in daily social interaction

should be explored in greater detail. Holistic network data might be used to examine life-course

changes in social network structure and assess whether network expansion is concomitant with

peripheralization within networks as people age. Moreover, qualitative research could explore the

changing nature of social resource access and daily interaction within organizational

environments to determine the processes by which these changes occur. Also, it is not merely the

process of aging per se that is implicated here, but also the ways that life-course experiences are

structured by age (Settersten, 2003). In other words, it is not only �getting old� that matters, but

also the kinds of contexts that young, middle-aged, and elderly individuals take part in that are

consequential for the accumulation and decline of social capital. Specifying the events and

experiences that contribute to this paradox offers both challenge and promise for future research.

Second, additional analyses indicate that these social capital trajectories are more orderly

and consistent during the middle years and display greater variance earlier and later in adult lives.

24

This presumably reflects the transitional character of these times in people�s lives. The youth

labor market involves considerable �churning� or �milling about� (Gardecki and Neumark, 1998)

as individuals complete education and attempt to settle on a career. The late career period also

contains numerous transitions out of primary careers and into retirement, secondary careers, and

volunteering (Moen, 2005). Little is known about the role that social capital plays during these

particular transitions.

Third, the findings presented here reveal a striking congruence between social resource

trajectories and those of voluntary association memberships. This finding seems to suggest not

only that retirement transitions may affect both types of associations but also that participation in

voluntary organizations may be causally linked to the expansion of occupational networks

(Rotolo and Wilson, 2003). The link between voluntary organization memberships and

occupational network expansion may reflect unique mechanisms of social capital accumulation.

Also, future research should examine in greater detail how different types of organization

memberships affect social resources across the life course, as prior research has indicated that

various organizations can have distinct consequences for the density and diversity of social

relationships (Glanville, 2004). Future research should also explore the timing and implications of

network decay due to separating from organizations (Popielarz and McPherson, 1995).

Fourth, the explanations we provide for the gender differences in age-related patterns of

social capital identified in this article are largely speculative at this point and should be elaborated

on in future investigations. In particular, researchers should examine how family and work

events/histories contribute to gender-specific social capital trajectories and clarify not only how

social capital accumulates across the life course, but also how these patterns are associated with

other forms of resource accumulation (economic, educational, health, etc.) (O�Rand, 2006).

Divergence and convergence in social capital trajectories likely exists across other social groups

as well (e.g., race/ethnicity and social class) and may serve as a mechanism to reproduce societal

inequalities across the life course.

Our analysis was designed to overcome the limitations of previous research literature on

network dynamism, but retains a number of limitations. Most importantly, this study relies on

cross-sectional data, which does not allow for the separation of age, period, and cohort effects

(Glenn, 2003). Our interpretation of the relationships emphasizes age-related processes by which

25

social capital tends to accumulate/dissolve, but alternative interpretations cannot be ruled out.

Cohort/generational differences and period effects may also influence social capital trajectories.

The greatest impediment to understanding variation in social capital across the life course is a

lack of panel data on social networks. As panel data sets become available, researchers should

attempt to distinguish age-related processes from changes in historical circumstances and enhance

understanding of these life-course processes. Also, the scope of this study does not encompass the

entire life course, as the networks of young people and those over age 65 could not be analyzed

with the data. Nonetheless, the results offer new insights into the ways that social capital is

structured by age. The analysis offers a baseline for further investigation of social capital

accumulation and encourages researchers to consider life-course factors when examining age-

based and gender-based patterns. Moreover, the patterns of social capital accumulation and

decline identified here are likely to vary on a host of additional social factors, including race,

social class, parenthood, and so forth. Future investigations of age-based differences in social

capital have the potential for enhancing knowledge of how social capital contributes to broader

processes of cumulative advantage and disadvantage for diverse populations across the life

course.

<H1>REFERENCES

Ajrouch, Kristine J., Alysia Y. Blandon, and Toni C. Antonucci. 2005. �Social Networks Among

Men and Women: The Effects of Age and Socioeconomic Status,� Journal of Gerontology 60B:

6: S311�S317.

Alon, Sigal, and Yitchak Haberfeld. 2007. �Labor Force Attachment and the Evolving Wage Gap

Between White, Black, and Hispanic Young Women,� Work and Occupations 34: 4: 369�398.

Angelusz, Róbert, and Róbert Tardos. 2001. �Change and Stability in Social Network Resources:

The Case of Hungary Under Transformation,� In Nan Lin, Karen Cook, and Ronald S. Burt

(eds.), Social Capital: Theory and Research: pp. 291�323. New York: DeGruyter.

Bidart, Claire, and Daniel Lavenu. 2005. �Evolutions of Personal Networks and Life Events,�

Social Networks 27: 4: 359�376.

26

Bourdieu, Pierre. 1986. �The Forms of Capital,� In John G. Richardson (ed.), Handbook of

Theory and Research for the Sociology of Education: pp. 241�258. Westport, CT: Greenwood.

Bridges, William P., and Wayne J. Villemez. 1986. �Informal Hiring and Income in the Labor

Market,� American Sociological Review 51: 574�582.

Burt, Ronald S. 1992. Structural Holes. Cambridge, MA: Harvard University Press.

Burt, Ronald S. 1998. �The Gender of Social Capital,� Rationality and Society 10: 1: 5�46.

Burt, Ronald S. 2000. �Decay Functions,� Social Networks 22: 1: 1�28.

Burt, Ronald S. 2002. �Bridge Decay,� Social Networks 24: 4: 333�363.

Cairns, Robert B., and Man-Chi Leung. 1995. �Friendships and Social Networks in Childhood

and Adolescence: Fluidity, Reliability, and Interrelations,� Child Development 66: 5: 1330�1345.

Campbell, Karen E. 1988. �Gender Differences in Job-Related Networks,� Work and

Occupations 15: 179�200.

Cantin, Stéphane, and Michel Boivin. 2004. �Change and Stability in Children�s Social Network

and Self-Perceptions During Transition from Elementary to Junior High School,� International

Journal of Behavioral Development 28: 6: 561�570.

Carstensen, Laura L. 1995. �Evidence for a Life-Span Theory of Socioemotional Selectivity,�

Current Directions in Psychological Science 4: 5: 151�156.

Coleman, James S. 1990. Foundations of Social Theory. Cambridge: Harvard University Press.

Cornwell, Benjamin, Edward O. Laumann, and L. Phillip Schumm. 2008. �The Social

Connectedness of Older Adults: A National Profile,� American Sociological Review 73: 185�203.

DiPrete, Thomas A., and Gregory M. Eirich. 2006. �Cumulative Advantage as a Mechanism for

Inequality: A Review of Theoretical and Empirical Developments,� Annual Review of Sociology

32: 271�297.

Dupre, Matthew E. 2007. � Educational Differences in Age-Related Patterns of Disease:

Reconsidering the Cumulative Disadvantage and Age-as-Leveler Hypotheses,� Journal of Health

and Social Behavior 48: 1: 1�15.

Elder, Glen H., Jr. 1985. �Perspectives on the Life Course,� In Glen H. Elder, Jr. (ed.), Life

Course Dynamics: pp. 23�49. Ithaca, NY: Cornell University Press.

27

Elder, Glen H., Jr., and Michael J. Shanahan. 2006. �The Life Course and Human Development,�

In William Damon and Richard M. Lerner (eds.), The Handbook of Child Psychology: pp. 665�

715. New York: Wiley.

Elliott, James R. 1999. �Social Isolation and Labor Market Insulation: Network and

Neighborhood Effects on Less-Educated Urban Workers,� Sociological Quarterly 40: 2: 199�

216.

Emirbayer, Mustafa. 1997. �Manifesto for Relational Sociology,� American Journal of Sociology

103: 2: 281�317.

Erickson, Bonnie H. 2003. �The Distribution of Gendered Social Capital in Canada,� In Henk

Flap (ed.), Creation and Returns to Social Capital: pp. 27�50. Routledge. [AUTHOR: Where

was this published?]

Fischer, Claude S., and Stacey J. Oliker. 1983. �A Research Note on Friendship, Gender, and the

Life Cycle,� Social Forces 62: 124�132.

Flap, H., and B. Volker. 2001. �Goal Specific Social Capital and Job Satisfaction: Effects of

Different Types of Networks on Instrumental and Social Aspects of Work,� Social Networks 23:

4: 297�320.

Fu, Yang-chih. 2005. �Measuring Personal Networks with Daily Contacts: A Single-Item Survey

Question and the Contact Diary,� Social Networks 27: 3: 169�186.

Fung, Helene H., Laura L. Carstensen, and Frieder R. Lang. 2001. �Age-Related Patterns in

Social Networks Among European Americans and African Americans: Implications for

Socioemotional Selectivity Across the Lifespan,� International Journal of Aging and Human

Development 52: 3: 185�206.

Gardecki, Rosella, and David Neumark. 1998. �Order from Chaos? The Effects of Early Labor

Market Experiences on Adult Labor Market Outcomes,� Industrial and Labor Relations Review

51: 299�322.

George, Linda K. 2009. �Conceptualizing and Measuring Trajectories,� In Janet Z. Giele and

Glen H. Elder, Jr. (eds.), Methods of Life Course Research: Qualitative and Quantitative

Approaches: pp. 163�186. Thousand Oaks, CA: Sage.

Gerstel, Naomi. 2000. �The Third Shift: Gender and Care Work Outside the Home,� Qualitative

Sociology 23: 467�483.

28

Glanville, Jennifer L. 2004. �Voluntary Associations and Social Network Structure: Why

Organizational Location and Type Are Important,� Sociological Forum 19: 3: 465�491.

Glenn, Norval D. 2003. �Distinguishing Age, Period, and Cohort Effects,� In Jeylan T. Mortimer

and Michael J. Shanahan (eds.), Handbook of the Life Course: pp. 465�476. New York: Kluwer

Academic/Plenum Publishers.

Granovetter, Mark S. 1973. �The Strength of Weak Ties,� American Journal of Sociology 78:

1360�1380.

Groves, Robert M., Floyd Fowler Jr., Mick Couper, James Lepkowski, Eleanor Singer, and Roger

Tourangeau. 2004. Survey Methodology. New York: Wiley & Sons.

Han, Shin-Kap, and Phyllis Moen. 1999. �Work and Family Over Time: A Life Course

Approach,� Annals of the American Academy of Political and Social Science 562: 98�110.

Kalmijn, Matthijs. 2002. �Sex Segregation of Friendship Networks: Individual and Structural

Determinants of Having Cross-Sex Friends,� European Sociological Review 18: 1: 101�117.

Kalmijn, Matthijs. 2003. �Shared Friendship Networks and the Life Course: An Analysis of

Survey Data on Married and Cohabiting Couples,� Social Networks 25: 3: 231�249.

Lambert, Tracy A., Lillian T. Eby, and Melissa P. Reeves. 2006. �Predictors of Networking

Intensity and Network Quality Among White-Collar Job Seekers,� Journal of Career

Development 32: 4: 351�365.

Lin, Nan. 1999. �Social Networks and Status Attainment,� Annual Review of Sociology 25: 467�

487.

Lin, Nan. 2001. Social Capital: A Theory of Social Structure and Action. Cambridge: Cambridge

University Press.

Lin, Nan, and Mary Dumin. 1986. �Access to Occupations Through Social Ties,� Social

Networks 8: 365�385.

Lin, Nan, Yang-chih Fu, and Ray-May Hsung. 2001. �The Position Generator: Measurement

Techniques for Investigations of Social Capital,� In Nan Lin, Karen Cook, and Ronald S. Burt

(eds.), Social Capital: Theory and Research: pp. 57�81. New York: De Gruyter.

Martin, John Levi, and King-To Yeung. 2006. �Persistence of Close Personal Ties over a 12-Year

Period,� Social Networks 28: 4: 331�362.

29

Maume, David J. Jr. 2004. �Wage Discrimination Over the Life Course: A Comparison of

Explanations,� Social Problems 51: 4: 505�527.

McDonald, Steve, and Glen H. Elder, Jr. 2006. �When Does Social Capital Matter? Non-

Searching for Jobs Across the Life Course,� Social Forces 85: 1: 521�550.

McPherson, Miller, Lynn Smith-Lovin, and Matthew E. Brashears. 2006. �Social Isolation in

America: Changes in Core Discussion Networks Over Two Decades,� American Sociological

Review 71: 3: 353�375.

Mettler, Suzanne. 2007. Soldiers to Citizens: The G.I. Bill and the Making of the Greatest

Generation. Oxford University Press. [AUTHOR: Where was this published?]

Moen, Phyllis. 2005. �Beyond the Career Mystique: �Time In,� �Time Out,� and �Second Acts�,�

Sociological Forum 20: 2: 189�208.

Moerbeek, Hester, and Henk Flap. 2008. �Social Resources and Their Effect on Occupational

Attainment Through the Life Course,� In Nan Lin and Erickson Bonnie (eds.), Social Capital: An

International Research Program. Oxford: Oxford University Press.

Moore, Gwen. 1988. �Women in Elite Positions: Insiders or Outsiders?� Sociological Forum 3: 4:

566�585.

O�Rand, Angela M. 2006. �Stratification and the Life Course: Life Course Capital, Life Course

Risks, and Social Inequality,� In Robert H. Binstock, Linda K. George, Stephen J. Cutler, Jon

Hendricks, and James H. Schultz (eds.), Handbook of Aging and the Social Sciences: pp. 146�

162. Burlington, MA: Academic Press.

Parks-Yancy, R., N. DiTomaso, and C. Post. 2006. �The Social Capital Resources of Gender and

Class Groups,� Sociological Spectrum 26: 1: 85�113.

Paxton, Pamela. 1999. �Is Social Capital Declining in the United States? A Multiple Indicator

Assessment,� American Journal of Sociology 105: 1: 88�127.

Popielarz, Pamela A., and J. Miller McPherson. 1995. �On the Edge or in Between: Niche

Position, Niche Overlap, and the Duration of Voluntary Association Memberships,� American

Journal of Sociology 101: 3: 698�720.

Putnam, Robert. 2000. Bowling Alone. New York: Simon & Schuster.

Rhee, Mooweon. 2007. �The Time Relevance of Social Capital,� Rationality and Society 19: 3:

367�389.

30

Rosenfeld, Rachel A., and Kenneth I. Spenner. 1992. �Occupational Sex Segregation and

Women�s Early Career Job Shifts,� Work and Occupations 19: 4: 424�449.

Rotolo, Thomas, and John Wilson. 2003. �Work Histories and Voluntary Association

Memberships,� Sociological Forum 18: 4: 603�619.

Saloner, Garth. 1985. �Old Boy Networks as Screening Mechanisms,� Journal of Labor

Economics 3: 3: 255�267.

Schoen, Robert, Young J. Kim, Constance A. Nathanson, Jason Fields, and Nan Marie Astone.

1997. �Why Do Americans Want Children?� Population and Development Review 23: 2: 333�

358.

Schoen, Robert, and Paula Tufis. 2003. �Precursors of Nonmarital Fertility in the United States,�

Journal of Marriage and the Family 65: 4: 1030�1040.

Seibert, Scott E., Maria L. Kraimer, and Robert C. Liden. 2001. �A Social Capital Theory of

Career Success,� Academy of Management Journal 44: 2: 219�237.

Settersten, Richard A., Jr. 2003. �Age Structuring and the Rhythm of the Life Course,� In Jeylan

T. Mortimer and Michael J. Shanahan (eds.), Handbook of the Life Course: pp. 81�98. New

York: Kluwer Academic/Plenum.

Smith, Ryan A. 2002. �Race, Gender, and Authority in the Workplace: Theory and Research,�

Annual Review of Sociology 28: 509�542.

Smith, Sandra S. 2000. �Mobilizing Social Resources: Race, Ethnic, and Gender Differences in

Social Capital and Persisting Wage Inequalities,� Sociological Quarterly 41: 509�537.

Smith, Sandra S. 2005. ��Don�t Put My Name on It�: Social Capital Activation and Job-Finding

Assistance Among the Black Urban Poor,� American Journal of Sociology 111: 1: 1�57.

Smith-Doerr, Laurel, and Walter W. Powell. 2005. �Networks and Economic Life,� In Neil J.

Smelser and Richard Swedberg (eds.), Handbook of Economic Sociology: pp. 379�402. Thousand

Oaks, CA: Sage.

Son, Joonmo, and Nan Lin. 2008. �Social Capital and Civic Action: A Network-Based

Approach,� Social Science Research 37: 1: 330�349.

Tomaskovic-Devey, Donald, Catherine Zimmer, Kevin Stainback, Corre Robinson, Tiffany

Taylor, and Tricia McTague. 2006. �Documenting Desegregation: Segregation in American

31

Workplaces by Race, Ethnicity, and Sex, 1966�2003,� American Sociological Review 71: 4: 565�

588.

Walker, Karen. 1990. �Class, Work and Family in Women�s Lives,� Qualitative Sociology 13:

297�320.

Wellman, Barry, Renita Yuk-lin Wong, David Tindall, and Nancy Nazer. 1997. �A Decade of

Network Change: Turnover, Persistence and Stability in Personal Communities,� Social Networks

19: 27�50.

Wenger, G. Clare, and Dorothy Jerrome. 1999. �Change and Stability in Confidant Relationships:

Findings from the Bangor Longitudinal Study of Ageing,� Journal of Aging Studies 13: 3: 269�

294.

Willson, Andrea E., Kim M. Shuey, and Glen H. Elder, Jr. 2007. �Cumulative Advantage

Processes as Mechanisms of Inequality in Life Course Health,� American Journal of Sociology

112: 6: 1886�1924.

Yakubovich, Valery, and Irina Kozina. 2000. �The Changing Significance of Ties: An

Exploration of the Hiring Channels in the Russian Transitional Labor Market,� International

Sociology 15: 3: 479�500.

<end REFS>

32

Table I. Descriptive Statistics

Variable Description Mean Range N

Age In years 42.38 22�65 2,995

Gender Female = 1, male = 0 .51 0�1 2,995

Social resources Factor score combining number of contacts with

highest prestige from position generator (PG)

3.36 0�6 2,995

Male contacts Proportion of male contacts in PG .49 0�1 2,995

Closeness Closeness of contacts in PG 3.43 0�5 2,995

Density Proportion of contacts in PG that know each other 1.82 0�4 2,812

0 = None know each other

1 = Only a few know each other

2 = About half know each other

3 = Most know each other

4 = All know each other

Trust Trust in contacts in PG 3.23 0�4 2,904

0 = None can be trusted

1 = Most can�t be trusted

2 = Some can be trusted

3 = Most can be trusted

4 = All can be trusted

Daily contact Number of people R makes contact with each day 2.30 0�5 2,995

33

0 = 0�4

1 = 5�9

2 = 10�19

3 = 20�49

4 = 50�99

5 = 100+

Work contact Proportion of daily contact that is work related 2.74 0�4 2,652

0 = Almost none

1 = Few

2 = About half

3 = Most

4 = Almost all

Organizational

memberships

Number of organizational memberships 1.73 0�10 2,995

34

Table II. Multiple Regression of Social Capital Indicators on Age

Social Resources Male Contacts Closeness Density

OLS OLS OLS Ordinal Logistic

Age .078 *** .001 * �.006 *** �.080 **

(.016) (.001) (.002) (.029)

Age squared �.0008 *** .0008 *

(.0002) (.0003)

N 2,995 2,995 2,995 2,812

R2 .021 .003 .006 .004

Trust Daily Contact Work Contact Org. Memberships

Ordinal Logistic OLS Ordinal Logistic OLS

Age .008 * �.008 ** .018 *** .123 ***

(.004) (.003) (.004) (.023)

Age squared �.0013 ***

(.0003)

N 2,904 2,995 2,652 2,995

R2 .001 .004 .004 .014

*p < 0.05; **p < .01; ***p < .001; constants are suppressed to save space.

35

Table III. Summary of Gender-Specific Regressions of Social Capital on Age

Social Resources Male Contacts Closeness Density

Male Female Male Female Male Female Male Female

Age + + n.s. + � n.s. � �

Age squared � � n.s. n.s. n.s. n.s. + n.s.

Age cubed n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

Trust Daily Contact Work Contact Org. Memberships

Male Female Male Female Male Female Male Female

Age n.s. + � � + + + +

Age squared n.s. n.s. n.s. + n.s. n.s. � �

Age cubed n.s. n.s. n.s. � n.s. n.s. n.s. n.s.

36

Fig. 1. Social resources, male contact, closeness, and density by age.

2.8

3.0

3.2

3.4

3.6

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Soci

al re

sour

ce

.44

.46

.48

.50

.52

.54

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Mal

e co

ntac

t

1A1B

37

3.3

3.4

3.5

3.6

3.7

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Clo

sene

s

1.6

1.7

1.8

1.9

2.0

2.1

2.2

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Den

sity

1C1D

38

Fig. 2. Trust, daily contact, work-related contact, and organizational membership by age.

3.1

3.2

3.3

3.4

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Trus

t

2.0

2.1

2.2

2.3

2.4

2.5

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Dai

ly c

onta

ct

2A2B

39

2.32.42.52.62.72.82.93.0

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Wor

k-re

late

d co

ntac

t

1

1.2

1.4

1.6

1.8

2

22-26

27-30

31-35

36-40

41-45

46-50

51-55

56-65

Org

. mem

bers

hi

2C2D