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RESEARCH ARTICLE |
a Department of Sociology, Anthropology, and Criminology, Eastern Michigan University, Ypsilanti
b Department of Psychology and Life Course Development Program, University of Michigan, Ann Arbor
c School of Public Health and Life Course Development Program, University of Michigan, Ann Arbor
Kristine J. Ajrouch, Department of Sociology, Anthropology, and Criminology, Eastern Michigan University, 712 Pray-Harrold, Ypsilanti, MI 48197 E-mail: kajrouch{at}online.emich.edu.
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Abstract |
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 TOP Abstract The Life Course Perspective...Social Networks and AgeSocial Networks and RaceSocial Networks and the...MethodsResultsDiscussionReferences |
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Methods. Respondents were drawn from a stratified probability sample of people aged 20–93 in the greater Detroit metropolitan area. Approximately 30% of the sample were African American, and people aged 60 and older were oversampled (n = 1,382). The authors used hierarchical regression analysis to estimate the influence of race and age on each component of social network, controlling for marital status, gender, and education. An interaction term (Race x Age) was added to explore the extent to which age moderates any detected race differences.
Results. Older age was associated with smaller, less frequently seen, and less proximal networks that had a higher proportion of kin. Blacks and Whites were similar with regard to proximity, but Blacks had smaller networks, more contact with network members, and more family members in their networks. Race differences in frequency of contact and proportion of kin were moderated by age, such that the differences in these variables diminished with increasing age.
Discussion. A systematic analysis of how age, race, and their interaction influence the characteristics of social networks furnishes important empirical knowledge about social networks among diverse groups. Such data may provide a context for how, and some explanation for why, support exchanges occur.
THE effect of race and age on social relations is receiving growing attention in the gerontological literature. This interest stems, in part, from the increasing diversity within the elderly population of the United States (
U.S. Bureau of the Census 1996
). Although early research highlighted the strengths and interdependence exhibited within the social networks of African Americans (e.g.,
Stack 1974), recent studies that compared social relations between African Americans (Blacks) and European Americans (Whites) have suggested vulnerabilities in support exchanges among Blacks (e.g.,
Hogan, Eggebeen, and Clogg 1993;
Silverstein and Waite 1993). These studies, however, often did not describe in any detail the nature of the respondent's social networks from which this support emanates (
Mutran 1985). Age comparisons in social networks are more numerous, with findings suggesting that the nature of social networks varies over the life course (
Antonucci and Akiyama 1987;
Morgan 1988). However, the literature lacks any systematic analysis of how network structure differs between Blacks and Whites and whether age has an effect on these differences. To help further the understanding of the relationships among race, age, and social resources, we conducted a detailed network analysis.
The term social network describes a structure of individuals with a designated relationship to the focal person, as well as an average frequency of contact and a specified geographical proximity to that person. Social networks can be thought of as a key resource over the life course, a form of social capital that potentially influences the exchange of supports (
Coleman 1988). Networks represent a form of social capital, a source of help in times of trouble, a source of comfort in times of pain, and a source of information in times of need. At the same time, as
Granovetter 1973 noted, sometimes there is strength in weak ties. Thus, having a multifaceted, diffuse, large network may be considerably more helpful in solving a problem than one which is dense, family based, and small.
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The Life Course Perspective and Race |
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TOPAbstractThe Life Course Perspective...Social Networks and AgeSocial Networks and RaceSocial Networks and the...MethodsResultsDiscussionReferences |
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;
Marshall 1996). More specifically, the life course perspective posits that the historical circumstances encountered earlier in life shape the life experiences of different groups, and may do so differently by age (
Harevan 1994).
Researchers who focus on aging and race have suggested a theoretical approach that incorporates both culture and sociodemographic attributes when examining the situation of any particular group, especially in a comparative framework (
Mutran 1985;
Roschelle 1997;
Taylor, Chatters, and Jackson 1997). The life course perspective recognizes the complexity of race as a variable. The effect of multiple characteristics should be included in the interpretation of any race differences, and simple explanations for a discussion of the effect of factors such as socioeconomic position, culture, age, marital status, and gender should be eschewed.
Network characteristics may differ according to stage of life. Experiences at a given stage in life may differ by racial group membership as well as by current conditions in society (
Barresi 1987). Race is a social status that influences life chances such that minorities may incur more hardship and have less opportunity than the dominant group, thus affecting network resources. Furthermore, racial status influences life chances in that minorities, even those with high education and high income, often experience stress from exposure to racism and discrimination (
Smith and Kington 1997). This experience may lead to a situation where the structure of social networks among minorities is denser and less diffuse, thus limiting access to potential aid in times of need (
Granovetter 1973;
Portes 1998). The life course perspective acknowledges these influences, as well as both continuity and change in social relationships during a lifetime (see, e.g., the Convoy Model of Social Relations,
Antonucci 1985;
Kahn and Antonucci 1980). We believe that a life course perspective facilitates an organized and comprehensive interpretation of how race and age influence the structure of social networks.
The examination of these influences may help us to better understand the diversity of social networks among Blacks and Whites across the life span. Network characteristics affect the probability of support (
Pugliesi and Shook 1998;
Taylor, Hardison, and Chatters 1996) as well as the quality and type of support (
Antonucci and Akiyama 1987;
Seeman and Berkman 1988). They also indicate the extent to which individuals participate in a number of social activities (
Morgan 1988).
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Social Networks and Age |
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;
Morgan 1988). However, analyses of the 1976 Americans View Their Mental Health data, which include more elaborate social network data on individuals including family, friends, neighbors, and formal group membership, showed that the number of close ties increases with age (
Pugliesi and Shook 1998). These contrasting findings may be explained in part by the different approaches to measuring the social network or to cohort differences (
van Tilburg 1998).
Frequency of contact with network members also varies with age. Older adults tend to have less frequent contact with their network members than do younger adults (
Mardsen 1987;
Morgan 1988;
Taylor 1986). This finding is also evident when comparisons are made between young-old and old-old adults (
Antonucci and Akiyama 1987). One network analysis of elderly African Americans revealed older respondents with children had less decline in frequency of contact with their social network (
Taylor 1986).
Research on age variations in the proximity of network members is almost nonexistent. One exception is found in an analysis by
Antonucci and Akiyama 1987, which showed that adults aged 50–64 lived closer to their networks than did adults aged 75–95. The lack of information on this network component represents a gap in understanding the structure of social networks. Proximity is important because it may indicate the feasibility and likelihood of support exchanges with network members, that is, the availability of social capital. This may in turn have implications for policy, helping to shape how service programs may aid individuals during times of need.
Proportion of kin is the aspect of network composition that has received the most attention, because family members are expected to provide support should the need arise. In general, findings indicate that kin are more prevalent in the networks of young and old, compared with middle-aged, persons (
Mardsen 1987;
Morgan 1988). For instance,
Morgan 1988 found that older adults tend to rely on family members, non-age peers, and those whom they have known for an extended period of time.
In summary, the manner in which age is associated with network characteristics other than size is not given systematic consideration in the literature. This may be due to the lack of data available across age groups that would allow for a more in-depth examination of other network characteristics such as proximity and network composition (kin vs non-kin). Yet the analysis of these characteristics is critical to understanding social networks over the life course. The social resources available to individuals may be related to age and, therefore, differentially influence the nature of relationships at various points in the life course. A more thorough treatment of age differences in a large, regionally representative sample will contribute to a deeper understanding of how social networks change over the life course.
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Social Networks and Race |
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;
Pugliesi and Shook 1998) or to shorter life expectancies among Blacks (
Ferraro and Farmer 1996).
Research on other dimensions of social networks has yielded findings that are less consistent. Findings of differences in frequency of contact with network members between Blacks and Whites are contradictory. For instance,
Pugliesi and Shook 1998 found that Blacks have less frequent contact with relatives, friends, and neighbors than do Whites.
Kim and McKenry 1998, on the other hand, found that Blacks have more frequent contact with political organizations, church, and their children.
Johnson and Barer 1990 reported that although Blacks and Whites have similar contact with spouse, children, and friends, Blacks have more frequent contact with relatives in general. Blacks may also have greater face-to-face contact with relatives (
Cantor et al. 1994).
Geographic proximity is often generally overlooked in comparisons of Black-White networks. One exception is Cantor and her colleagues (1994), who examined proximity to children, siblings, and other kin among Whites, African Americans, and Latinos living in New York City. Findings revealed that older African Americans were more likely than Whites to report that their children lived nearby or in the same household. Additionally, Whites reported seeing their siblings more often than Blacks, although Blacks had more relatives living in the same city than their White counterparts.
Black-White comparisons in aspects of network composition have yielded varied findings, with some recurring between-group similarities. Specifically, findings suggest that regardless of race (a) women are central to receiving and giving support and (b) there is a hierarchical preference for accessing support with spouse first, followed by children, other kin, and finally friends and neighbors (
Cantor 1979;
Cantor et al. 1994;
Chatters, Taylor and Jackson 1986;
George 1988). There are also some consistent Black-White differences; for example, although they tend to be smaller, the networks of African Americans are more likely to include extended family, fictive kin, friends, or church members (
Cantor et al. 1994;
George 1988;
Kim and McKenry 1998).
As the literature discussed previously suggests, the creation and use of social capital by Blacks and Whites include both similarities and differences. However, although social networks may constitute a resource in some cases, they may concurrently yield negative effects in cases where personal ties prohibit access to resources outside of the personal network (
Portes 1998;
Roschelle 1997). For example, if enmeshment in a network is a reaction to discrimination, then network members are more likely to experience a drain on their resources, increasing the likelihood of negative effects as well as limiting access to other available resources in times of need.
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Social Networks and the Race by Age Interaction: Limitations in the Literature |
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). Results indicated that age is an important moderating variable for Black women; support exchanges are greater for older Black women than those at midlife. No information is provided, however, about the structural characteristics of the network providing this support.
Taylor, Chatters, and their colleagues have developed a body of knowledge on African American social networks, especially among elderly persons. Using a nationally representative sample, they showed that marital status, gender, and level of education affect network composition among African American elderly persons (
Chatters, Taylor, and Jackson 1985;
Chatters et al. 1986;
Taylor et al. 1996). The significance of Taylor and Chatters' work is the great progress made in understanding the networks of elderly African Americans. This strength was achieved through an emphasis on multivariate analysis, which provides a more thorough understanding of the size and composition of social networks. However, some gaps remain. Their work generally did not include a comparative framework primarily because as pioneers in the field they believed that basic information on African Americans was needed before the field would benefit from comparative analyses. It may now be both appropriate and useful to begin such a comparative exploration.
In the present study we contribute to the literature in the following ways. First, in contrast to most research on comparisons of Black-White social relations (for an exception, see
Cantor et al. 1994), we examined the four major aspects of social networks among Blacks and Whites, including size, frequency of contact, proximity, and composition, in the context of control variables shown to affect social relations, including education, gender, and marital status. Second, we examined the effects of age on these network characteristics. Third, we investigated the interactive effect of race and age. These aspects are central components of a personal network structure and potentially provide important information about the structure of social relationships (
Granovetter 1973).
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Methods |
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The measures of multiple dimensions of social network characteristics allowed for a detailed comparison between Black and White respondents. In addition, the broad age range enabled us to thoroughly examine age differences in network characteristics. Respondents were asked a detailed series of questions about social relations. We used a hierarchical mapping technique (
Antonucci 1986) to diagram the nature of respondents' social relations. With this technique, respondents were presented with a set of three concentric circles with the word "YOU" written in the middle. We gathered network structure data by first asking the respondent to name those individuals to whom they felt closest, then asking them to list others with whom they felt less close but still considered important, and finally asking them to list any others that they would include in their network. This approach created a map of concentric circles that represent three levels of closeness in an individual's network. The first name or initials were written on the diagram in the order indicated by the respondent. Respondents were then asked a series of questions concerning social support and social network characteristics.
Dependent Measures
Network size was designated by the total number of people the respondent included on his or her diagram after stating the names of people to whom the respondent felt closest (inner circle), close (middle circle), and somewhat close (outer circle). All named individuals from each concentric circle were counted, and that sum became the total network size. Values ranged from 0 to 20. Frequency of contact was the mean frequency of contact with all network members. Respondents used a 5-point scale: every day, once per week or more often, once per month or more often, once per year or more often, and irregularly (1 = every day, 5 = irregularly). Geographic proximity was a dichotomous measure that assessed whether each network member lived within an hour's drive of the respondent (yes or no). If the network member lived with the respondent, he or she was included as one who lived within an hour's drive. The proportion who lived within an hour's drive was calculated, with a possible value range from 0–1. Network composition was the proportion of kin to non-kin network members. Possible values ranged from 0 to 1, with higher numbers indicating a higher proportion of family members in the network.
Independent Measures
Race was a self-reported measure. Specifically, the respondent was asked to answer the following question: "Are you White, Black, Native American, Asian, Hispanic, or another race?" If the respondent answered with multiple categories, then he or she was asked to list the one category that best described his or her race. For purposes of this analysis, race was dummy coded with 0 representing White and 1 representing Black. All other categories were removed from the analysis. Age was a continuous variable measured in number of years.
Control Measures
Gender was a dummy variable with 0 representing male and 1 representing female. Education was a continuous variable measured in number of years. Respondents were asked to state the highest grade of school or year of college that they completed. To discover marital status, we asked the respondent to state whether he or she was currently married or living with a partner, widowed, divorced, separated, or never married. This measure was a dummy variable where 0 = not married and 1 = married or living with a partner.
Methods of Analysis
To explore the main effects of age and race on each of the four dimensions of network structure, we performed a hierarchical regression analysis. We centered the variables of age and race to reduce potential problems with multicollinearity, and we constructed a regression model for each dependent variable of interest. The first step involved testing for possible effects from the control variables of education, gender, and marital status. In the second step, race and age were added to the model. To determine whether race differences in social network characteristics were moderated by age, we added the interaction term (Race x Age) in the third step to discern whether this interaction term accounted for a significant amount of additional variance (p 
.05) in the dependent variable.
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Results |
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.05) on network size, such that married people, women, and more educated people had larger networks. In Model 2, results indicated a main effect of age and race on network size. More specifically, older respondents and African Americans had smaller networks. The Race x Age interaction term was not significant, suggesting that race differences were not affected by the age of the respondent. In other words, although younger people had larger networks, Blacks had smaller networks than their White counterparts regardless of age.
Frequency of Contact
Results again showed that marital status of the respondent, gender, and education level were all significant (p .05) predictors of frequency of contact in all three models.
In Model 2, there was a significant main effect of both age and race on frequency of contact. The effect of age suggests that older respondents had less contact with their network members, and the effect of race suggests that Blacks had more frequent contact. Entering the interaction term in Model 3 indicated that race differences in frequency of contact were moderated by age so that with increasing age, the average frequency of contact became more similar between Blacks and Whites, although the added explained variance was small (an additional .003).
Proximity
Of the three control variables, education (but not marital status or gender) was a significant predictor of network proximity in the three models.
There was a significant effect of age on geographical proximity, indicating that older respondents had less proximal networks. However, results indicated no Black-White differences in proximity of network members and no significant interaction effect of race and age.
Network Composition (Proportion of Kin)
Marital status and education, but not gender, were significant predictors of proportion of kin in all three models. There was a main effect of age and race on the proportion of kin. Furthermore, age moderated the detected race differences. Older respondents had a higher proportion of kin in their networks, as did African Americans. However, with age, the magnitude of this difference was reduced, so that older Blacks and Whites had a similar percentage of family in their networks.
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Discussion |
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;
Cohen and Syme 1985;
House, Landis, and Umberson 1988;
House, Umberson, and Landis 1988;
Pearlin 1989). Our findings indicate that there are some differences by age and race, as well as evidence of a small interaction effect of these variables on frequency of contact with network and proportion of kin in network.
Older age is associated with smaller, less frequently seen, and less proximal networks that have a higher proportion of kin. These findings corroborate previous research in this area (
Antonucci and Akiyama 1987;
Mardsen 1987;
Morgan 1988;
Taylor 1986). With networks becoming smaller, less diverse, and less accessible because of the lower rates of contact and geographical proximity, older adults seem to have fewer social resources than their younger counterparts. On the other hand, quantity does not guarantee quality, and it may be that older individuals discard the "draining" aspects that accompany larger networks that are more proximal and more frequently seen. Furthermore, they may derive more satisfaction from relationships with family members than with others, as hypothesized in socioemotional selectivity theory (
Carstensen 1993), and as suggested in the hierarchical preference finding that older adults choose family members first in situations of need (
Cantor et al. 1994). It is also likely the case that contact with peers may decline with age because of health problems or moving, and that with age, peers and family members may be lost through death and not replaced (
Antonucci and Akiyama 1987).
Thus, successful aging may be threatened on the one hand, as opportunities for contacts and social transactions decrease, yet enhanced on the other, if older adults focus on relationships that they find most beneficial. Research should assess the degree to which network proximity is voluntary and if it is correlated with receipt of instrumental support. Instrumental support from one's social network may be jeopardized if contact is less frequent and members are less proximal. Policymakers may want to consider ways in which instrumental support may need to be supplemented among those elderly persons with the least proximal networks.
We also found significant race differences in characteristics of social networks that did not vary with age: Blacks have smaller networks, more family members in their networks, and more contact with network members. The finding that network size is smaller for Blacks than Whites is one that is common in the race and social relations literature (e.g.,
Cantor et al. 1994). The emphasis on family is also supported in previous literature. These findings are sometimes explained as the strategies that emerge among African Americans because of economic deprivation and discrimination (
Roschelle 1997;
Wilson 1996). Lifelong experience with discrimination may influence who is trusted, affecting who becomes a network member. Family members may be most trusted among African Americans, explaining to some degree the consistent finding that social networks among Blacks are smaller, are more likely to include a majority of kin, and include frequent contact. Although smaller networks may describe warm, supportive, close, and loving relationships, they may not be much help for accessing resources beyond the immediate network (
Granovetter 1973). For example, if everyone knows the same people as you, it will not be as much help if you are looking for a job and a friend of a friend asks your friend if he or she has a friend who is looking for a job. Future research should systematically examine how patterns of network characteristics that have been shown to differ by ethnic or racial status affect well-being ranging from problem-solving abilities to physical and mental health.
Cantor and her colleagues (1994) found that Blacks had more proximal networks than Whites. Our findings suggest, however, that between-group proximity is similar. This discrepancy may be explained by the ways in which this dimension is measured. In the present study, we measured proximity by asking the respondent whether the network member lived within an hour's drive. Cantor and her colleagues used a more detailed measure (living in the same home or building, within walking distance, within city limits, within the metropolitan area, or beyond the metropolitan area). This measure allows for more variance, and hence may explain in part the significant finding. The difference in findings may also be due to the characteristics of the geographic areas from which respondents for these studies were sampled (New York City vs the metropolitan Detroit area). The metropolitan Detroit area is dominated by the auto industry and lacks a comprehensive public transit system. New York City is more densely populated, with less reliance on automobiles. Access to transportation as well as city culture may both influence proximity of network members and the degree to which neighborhoods are segregated, suggesting that regional characteristics influence the structure of relationships and hence the nature of social capital.
The fact that race differences in frequency of contact and proportion of kin are minimized in older age suggests that age may be a more powerful predictor of contact and network composition than race and that the network structure of older African Americans and White Americans contain similar attributes. The life course perspective may contribute to our understanding of these moderating effects. The process of aging, as well as the particular time period in which this sample grew old, contributes to diminishing race differences among the older respondents in this sample. The cross-sectional nature of the data makes it difficult for us to assert with confidence that this is an age effect and not a cohort effect. For instance, an age-as-leveler explanation posits that aging produces a different interaction pattern than the one that existed during youth. The way our modern, industrial/postindustrial society operates contributes to less opportunity for leisure time spent with aging relatives or friends, and this effect is felt across race. The life course perspective also allows for a cohort explanation. African Americans have been especially affected by changes in the economic restructuring of the United States during the 1980s, and so race differences among the younger respondents in the sample may reflect the impact these policies have had on network characteristics (
Roschelle 1997). These changes may contribute to less time afforded to older kin, even though kin are those individuals who make up the largest percentage of a given network. The life course perspective allows for an understanding that incorporates the nature of changes in society and how those changes may impinge upon the structure of social relationships. Interpretations that consider the impact of social policies and social change uncover some of the complexity that accompanies studies of race and age and, furthermore, highlight the factors that influence forms of social capital, that is, those resources that are lodged in the structure of social ties.
The current findings represent a systematic analysis of social network characteristics across race, demonstrating both differences and similarities. They suggest that age may have more influence on social networks than does race, thus supporting the notion that race alone is not a sufficient variable for understanding variance in social relations (
Mutran 1985). However, there remain many areas to examine. Gender and socioeconomic position are significant factors for investigators to consider in the study of social networks and analyses of race and age differences. Although in our analysis gender and education served as control variables, future research should directly examine these variables as they relate to race and age. Similarly, the analyses presented here examine only one dimension of social relations. The next step is to investigate how network variables are associated with other dimensions, such as support type and quality.
In sum, in the present study we advance the literature on social relations by focusing on network structure differences and similarities that exist between African Americans and White Americans. Although the quality and nature of such relationships are more thoroughly assessed through ethnographic and qualitative approaches (
Dilworth-Anderson and Burton 1999;
Dressel, Minkler, and Yen 1997;
Johnson 1999), the characteristics of social networks such as size, frequency of contact, geographical proximity, and composition are exceptionally amenable to survey methodology. Furthermore, in the present study we identify how certain race differences may diminish with age. These findings suggest, therefore, that policymakers and program planners should not view race and age simplistically but instead consider how their interaction influences the social capital available through personal networks. In this way, enacted policies will be more likely to benefit and be sensitive to the needs of all individuals as they age.
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Acknowledgments |
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Received for publication July 7, 2000. Accepted for publication October 26, 2000.
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