When being alone might be better: neighborhood poverty, social capital, and child mental health

Social Science & Medicine 57 (2003) 227–237

When being alone might be better: neighborhood poverty, social capital, and child mental health Margaret O’Brien Caughya,*, Patricia J. O’Campob, Carles Muntanerc a

UT-Houston School of Public Health, MPH Program at UTSW-Dallas, 5323 Harry Hines Blvd, V8.112A, Dallas, Texas 75390-9128, USA b Department of Population and Family Health Sciences, Johns Hopkins University, 615 North Wolfe, Room E4001, Baltimore, MA 21205, USA c Department of Behavioral and Community Health Nursing and Epidemiology and Preventive Medicine, University of Maryland at Baltimore, 655 West Lombard Street, Baltimore, MA 21201, USA

Abstract Public health researchers have provided a growing body of evidence on the salutary effects of social capital for individual well being. The importance of these findings for social epidemiology, however, may have precluded so far a full examination of the complex association between neighborhood social processes and the well being of individual residents, including the often acknowledged potential ‘‘downside’’ of social capital. In this study, we examine the association between attachment to community, an indicator of social capital, in a sample of African American parents, and the presence of behavior problems in their preschool children. Participants were recruited from a socioeconomically diverse set of neighborhoods. Attachment to community was assessed using a multi-item scale comprised of two subscales, general sense of community and how well one knew one’s neighbors. Results indicated that the association between how well a parent knew her neighbors and the presence of child behavior problems differed depending on the degree of economic impoverishment of the neighborhood. In wealthy neighborhoods, children whose parent reported knowing few of the neighbors had higher levels of internalizing problems such as anxiety and depression compared to those who knew many of their neighbors. In contrast, in poor neighborhoods, children whose parent reported knowing few of the neighbors had lower levels of internalizing problems compared to those who knew many of their neighbors. These results are discussed in terms of furthering the study of the contextual nature of the social capital in explaining community inequalities in mental health among children. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Social capital; Neighborhoods; Child mental health; USA

The concept of social capital, understood as the norms of trust and reciprocity and the concern for the well being of one’s community, has captured the interest of many researchers in epidemiology and public health (Kawachi & Berkman, 2000; Cattell, 2001). Some early critics contended that the social properties arising from social networks are not necessarily always good for a community (Portes & Landolt, 1996) or for its members *Corresponding author. Tel.: +1-214-648-1052; fax: +1214-648-1081. E-mail address: [email protected] (M.O. Caughy).

(Muntaner & Lynch, 1999). Since then the consensus has gravitated towards an explicit acknowledgment that social capital can also have negative impacts (Kawachi & Berkman, 2000). For example, it has been observed that some community organizations that increase trust among their members also make them less trusting of those who do not belong to the association (Stole, 1998; e.g., racialized organizations). This communitarian definition of social capital emphasizing psychosocial mechanisms such as face-toface interpersonal relationships (Putnam, 2000) has been associated with better population health in a number of studies (Kawachi, Kennedy, Lochner, & Prothrow-Stith,

0277-9536/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 2 ) 0 0 3 4 2 - 8

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1997; Kawachi, Kennedy, & Glass, 1999; Runyan et al., 1998; Veenstra, 2000; Lindstrom, Hanson, & Ostergren, 2001; Kennedy, Kawachi, & Brainerd, 1998; Veenstra, 2002). On the other hand, some studies have failed to find an association between social capital indicators and better outcomes (Lynch, Due, Muntaner, & Smith, 2000; Rosenheck et al., 2001). In addition to the communitarian definition of social capital given above, social scientists have defined social capital in terms of just social networks or institutional relations (e.g., relationships between civic organization and the government) (Woolcock, 1998). Other scholars have contended that, in order to understand the diverse effects of social capital, researchers need to take into account the role of labor markets, suburban development, race and social class in increasing or reducing social capital (Muntaner & Lynch, 1999; Smidt, 1999; Cattell, 2001). Community health researchers have begun to examine the complexities of the relationship between social capital and individual well being in neighborhood research (Cattell, 2001). As Mary Patillo (1998) has shown in her studies on African American neighborhoods, networks that produce beneficial outcomes are often those that can hold back community development. For example, gangs and mafia families can produce access to resources, social integration and enforce social norms as well as thwart upward social mobility (Portes & Landolt, 1996). Qualitative studies of resilient African American mothers in Washington, DC suggest that one way to increase the likelihood of successful development of children is to isolate them from local community networks (Brodsky, 1996). However, research on African American neighborhoods has often lacked an adequate representation of middle-class families and has generally employed a unidimensional view of ‘‘lack of community social capital’’ (Patillo, 1998) One important outcome of community social capital research is youth behavior (Sampson, Raudenbush, & Earls, 1997), yet studies of neighborhood context and youth behavior have paid little attention to the potential role of social capital (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993; Kupersmidt, Griesler, DeRosier, Patterson, & Davis, 1995; Martinez, 1999). In this study, we examine the impact of attachment to community of parents, a social capital indicator, as a predictor of mental health status of their preschool-aged children. Sampson (1992, 2001) has explicated the potential processes by which neighborhood social capital might influence the development of children. One mechanism he identifies are ‘‘rules’’ or ‘‘shared action-expectations and social controls for guiding children’s behavior’’ (Sampson, 2001, p. 9). This mechanism emphasizes the connections between parents and how these connections foster the development

of norms and the collective socialization of children in the community. Therefore, although social capital has been defined as a broader concept, we have chosen to focus on those aspects of social capital, specifically social anchorage and social networks, which are most relevant to the outcomes for children examined in this study. The sample of families participating in this study were all African American and represented the breadth of socioeconomic backgrounds, both in terms of family level measures of socioeconomic status as well as neighborhood economic characteristics. We chose to limit this study to a wholly African American sample for several reasons. First, studies of family and child development processes among African Americans have been largely limited to studies of families in poverty thereby confounding race with social class (Peters, 1988). Furthermore, there is a need to undertake research that examines processes that occur within specific race and ethnic groups rather than focusing on comparing groups to one another (Garcia Coll, 1990; McLoyd & Randolph, 1984). This is probably even more the case when it comes to the examination of neighborhood processes because the ecological niches of African American families are so different from those of European American families. According to 1990 census data, African Americans were more than 15 times as likely to live in high poverty neighborhoods than nonHispanic whites, and poor African Americans were more than seven times as likely to live in high poverty neighborhoods than poor non-Hispanic whites (Jargowsky, 1997). In this sample of African American families and their preschool children, we hypothesized that attachment to community would be differentially related to child mental health status depending on the characteristics of the neighborhoods in which the families lived.

Method Setting This study was conducted in Baltimore, Maryland, an older urban center on the Atlantic seaboard of the United States, between 1998 and 1999. Neighborhoods, defined as census block groups for this study, were selected to represent the range of socioeconomic status and racial composition of neighborhoods in Baltimore City. Census block groups are the smallest geographic areas for which the US Census provides data and have an average population size of 1500 people. Between 1 and 6 census block groups are combined to form a census tract, which have an average population size of 4000 people. We chose to use census block groups as neighborhoods because of

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their smaller size and because they are more homogeneous than census tracts. To select the study neighborhoods, we stratified the census tracts in Baltimore according to average household wealth (derived from 1997 projected data provided by Claritas/NPDC, P.O. Box 610, Ithaca, NY 14851-0610) and by racial composition (African American, European American, and racially mixed). With regards to racial composition, Baltimore City is not terribly diverse. In 1997, Baltimore City was comprised of approximately 65% African Americans and 32% European Americans with other race/ethnic groups comprising less than 3% of the city’s population. From each strata, we chose two census tracts. The selection of tracts was not random but rather was made to maximize differences in average household wealth and average household income between tracts from different strata. From each census tract, we chose between one and three block groups to include as study neighborhoods. The number of block groups chosen was not constant because the number of block groups varied per tract and the racial compositions of the block groups were not always consistent with the racial composition of the tract (i.e., the tract may have been racially mixed while the census block group was racially homogeneous). This report is limited to data from interviews with 200 African American families that were living in one of 39 neighborhoods in the study. The number of participants per neighborhood ranged from 1 to 16 with an average of 5.13. The characteristics of the 39 study neighborhoods are compared to Baltimore City as a whole in Table 1. There were no significant differences between the study neighborhoods and Baltimore City as a whole except with regards to racial composition. The average proportion of African American residents was higher among the study neighborhoods than in the city as a whole (85.7% vs. 61.9%, t ¼ 6:76; po0:01). This is not surprising given the fact that African American participants were recruited primarily from African American or racial mixed neighborhoods. Participants Families with children between 3 and 412 years old in which the primary caregiver self-identified as African American were recruited from the study neighborhoods through door-to-door canvassing, targeted mailings, from day care centers, and from Head Start programs. Two home visits were conducted no more than two weeks apart, each lasting two and a half hours on average. All interviews were conducted by one of two African American interviewers who had a combined experience of more

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than 35 years conducting community-based interviews in Baltimore.1 The first interview consisted of an interview with the primary caregiver, hereafter referred to as the parent, and the second interview consisted of an assessment of the child as well as additional questions for the parent. Parents received $50 in appreciation of their time, and children received a small gift after completion of the assessment battery. Interviews were completed with 200 families. The number of refusals was minimal (less than 5%). Although we attempted to collect data on families who refused participation, this was not possible because once an individual refused, s/he was not receptive to providing additional information. Therefore, it is not possible to compare participants with those who were approached but who chose not to participate. Measures Variables in this analysis included family socioeconomic position, neighborhood socioeconomic status, parental psychological sense of community, child behavior problems, and child characteristics. Socioeconomic position measures included family poverty level and parental education. Family poverty level was a continuous variable defined as family income adjusted for family size as a proportion of the federal poverty level. For example, a family whose income was equal to the federal poverty level for their family size was given a value of 100% for family poverty level, and a family whose income was twice the federal poverty level for their family size was given a value of 200%. The validity of self-report income was supported by its strong association with home ownership, car ownership, and receipt of welfare benefits (data available upon request). Parental education was a categorical variable: less than high school, high school/GED, more than high school. Neighborhood socioeconomic position was assessed using a measure of neighborhood impoverishment. This factor score was based on the work of Korbin & Coulton (1997) and included neighborhood poverty rate, unemployment rate, vacant housing rate, and proportion of households with children under the age of 5 which were single headed. Using all block groups in the city, each variable was standardized, and the component variables were averaged to yield an impoverishment score. The internal reliability of the 1 All interviewers were trained on the child assessments and subjective components of the interview with ‘‘pilot families’’ until they achieved an reliability of 85% agreement or higher with an independent observer. The independent observer was either the first author (MC) or the project director, a doctoral student in psychology. During the field period, the project director or first author attended home visits periodically to monitor reliability.

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Table 1 Characteristics of study neighborhoods compared with all neighborhoods in Baltimore City

Average household wealth ($1000s) Poverty rate for children 0–5 (per 100) Unemployment rate (per 100) Proportion African American Per capita crime rate

Baltimore City (N ¼ 832) Mean (range)

Study neighborhoods (N ¼ 39) Mean (range)

107.3 (0–416) 29 (0–100) 10 (0–100) 61.9% (0–100%) 0.51 (0.01–179)

111.8 (7.5–24.3) 27 (0–91) 11 (0–65) 85.7% (3.2–99.7%) 0.11 (0.05–0.38)

Impoverishment factor was 0.57 at the block group level for all of the block groups in Baltimore City. Parental psychological sense of community (PSOC) was assessed using a scale developed by Chavis, Florin, Rich, and Wandersman (1987 as cited by Linney & Wandersman, 1991; McMillan & Chavis, 1986). PSOC is defined as ‘‘ya feeling that members have of belonging, a feeling that members matter to one another and to the group, and a shared faith that members’ needs will be met through their commitment to be together’’ (McMillan & Chavis, 1986). PSOC has been shown to be associated with positive individual- and community-level outcomes (e.g., Chavis & Newbrough, 1985; Chavis & Wandersman, 1990; Davidson & Cotter, 1991; Pretty, Andrewes, & Colett, 1994). The scale included 13 likert-scale items reflecting the respondent’s perceived sense of membership, shared emotional connection and degree of mutual influence with his/her neighborhood of residence. The reliability and validity of this instrument is reported previously (e.g., Chavis & Pretty, 1999; Chipuer & Pretty, 1999). Factor analysis of the 13 item scale utilizing principal axis factoring and a varimax rotation revealed a two factor solution explaining 60% of the variance. The factor loadings of the thirteen items are shown in Table 1. One item, ‘‘My neighbors look after my children when I’m not around’’, loaded too heavily on both factors to be assigned to one factor over the other and was therefore dropped. The first factor, referred to as psychological sense of community-general (PSOC-G) consisted of the first 10 items listed in Table 1 and had an internal reliability of 0.92. The second factor, referred to as psychological sense of community - knows neighbors (PSOC-K) consisted of the last two items listed in Table 1 and had an internal reliability of 0.58. Both scale scores were computed as the unweighted average of the component items. For PSOC-K, the second item, ‘‘Very few of my neighbors know me’’, was reverse-coded prior to calculating the scale score. The PSOC variables were utilized in the analyses as both continuous as well as dichotomous variables. Because we were interested in parents who were at the extreme lower end of the spectrum of attachment to community, we created dichotomous variables for PSOC-G and

PSOC-K which were coded 1 if respondents were in the lowest quartile for that variable. Child mental health status was assessed using the child behavior checklist (CBCL, Achenbach & Edelbrock, 1981). The CBCL yields scores for internalizing problems (e.g., anxiety, depression, withdrawal), externalizing problems (e.g., aggression), as well as a score for total problem behaviors. The CBCL/2-3 version was used for 3 year old children in the sample, and the CBCL/4-18 was used for the 4 year olds. The raw scores for total problem behaviors, internalizing behaviors, and externalizing behaviors were converted to t-scores using a program supplied by the scale’s authors. T-scores of greater than 60 or 70 (depending on the age of the child) would indicate a clinically referable behavior problem. However, because this was not a clinic sample, we analyzed the CBCL data as a continuous variable. Test– retest reliability of the CBCL is 0.95 for behavior problems and 0.996 for social competence items. Interparent agreement is reported as 0.98 for both behavior problems and social competence. The CBCL has been found to reliably discriminate between referred and non-referred children (Achenbach & Edelbrock, 1981). Other child characteristics that were included in the analysis were limited to child gender. There is a significant amount of literature supporting a higher prevalence of externalizing behaviors among boys versus a higher prevalence of anxiety disorders among girls (US Department of Health and Human Services, 1999). Because there is no empirical evidence of differences in behavior problems related to presence of siblings and/or birth order, these variables were not included in the analysis. Analysis methods Bivariate analyses were used to examine the association between child behavior problems, PSOC, neighborhood conditions, and family covariates and consisted of analysis of variance. Regression analysis was used to examine associations between PSOC and child behavior problems while controlling for confounders and to test potential interactions between PSOC and neighborhood conditions.

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Results Characteristics of the study sample are shown in Table 2. Of the 200 participants, 173 (86.5%) were mothers of the target child. The next largest category of primary caregivers were grandmothers, comprising 9% of the sample. The sample was economically diverse, with 44.5% living below poverty and 30.5% living above 180% of poverty. Slightly more than half of the study children were girls. The characteristics of the neighborhoods in which participants lived are shown in Table 2 as well. Although the study sample was fairly evenly distributed across neighborhoods in terms of neighborhood economic status, the majority of the participants were residing in predominantly African American neighborhoods. Of the 200 participants, 174 (87%) were living in an African American neighborhood. This is consistent with the segregated nature of Baltimore City. In addition, the participants were more heavily concentrated in poorer neighborhoods relative to the city as a whole, with 32.5% of participants living in the poorest quartile of neighborhoods and 19% of participants living in the wealthiest quartile of neighborhoods. This is consistent with the economic distribution of Baltimore City. The poorest neighborhoods in the city are almost wholly African American whereas the wealthiest neighborhoods are primarily European American in terms of racial composition. The bivariate associations between child behavior problems, psychological sense of community, and neighborhood and family measures in Table 3. Child behavior problems were lowest and general psychological sense of community was highest in neighborhoods with the highest average household wealth. Externalizing problems were highest and general psychological sense of community was lowest among families living below poverty level. Interestingly, single parents were

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more likely to report knowing their neighbors than parents in other types of families. A comparison of average total, internalizing, and externalizing behaviors revealed slightly higher scores for internalizing behaviors among children whose mother was in the lowest quartile for PSOC-G (49.37 versus 45.81, t ¼ 1:90; df=184, p ¼ 0:058). There were no significant differences in problem behaviors for children whose mother was in the lowest quartile of PSOC-K. Regression analysis was used to examine potential interactions between attachment to community and neighborhood impoverishment for the outcome of behavior problems while controlling for family income. The PSOC variables were used as binary variables coded as 1 if the respondent was in the lowest quartile for that variable. In addition, neighborhood impoverishment was stratified into quartiles. Results can be found in Table 4. Being in the lowest quartile of PSOC-G was related to higher rates of internalizing behaviors which were marginally statistically significant. For PSOC-K, however, there was an interaction between being in the lowest quartile and neighborhood impoverishment (Table 5). The rate of total problem behaviors and internalizing behaviors for children living in neighborhoods in the lowest quartile of impoverishment was higher if their mothers knew very few of their neighbors. In contrast, for children living in highly impoverished neighborhoods, having a mother who knew very few of the neighbors was associated with significantly fewer total behavior problems and internalizing problems as well as moderately lower externalizing problems. The interaction is graphically displayed in Fig. 1. It appears that lack of attachment to community was a risk factor for behavior problems for children living in wealthy communities but a protective factor for children living in highly impoverished neighborhoods.

Table 2 Factor loadings of psychological sense of community items Item

Factor 1

I expect to live in this neighborhood for long time It is very important to me to live in this particular neighborhood People in my neighborhood share the same values If there is problem in this neighborhood, people who live here can get it solved I have influence over what my neighborhood is like People in this neighborhood get along with each other I think my neighborhood is a good place for me to live My neighbors and I want the same things I feel at home in this neighborhood I care what my neighbors think of my actions My neighbors look after my children when I’m not around I can recognize most of the people who live in this neighborhood Very few of my neighbors know me

0.823 0.753 0.752 0.752 0.748 0.729 0.715 0.679 0.647 0.528 0.456

Factor 2

0.453 0.773 0.532

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Table 3 Description of study sample (N ¼ 200) N

%

174 1 25

87.0 0.5 12.5

65 45 52 38

32.5 22.5 26.0 19.0

173 4 18 5

86.5 2.0 9.0 2.5

Family structure Nuclear Single parent Nuclear/extended Single/extended Other family/no parent Mother/boyfriend

39 79 11 51 18 2

19.5 39.5 5.5 25.5 9.0 1.0

Poverty status o100% poverty 100–179% poverty 180%+ poverty

89 50 61

44.5 25.0 30.5

Educational attainment Less than high school High school/GED More than high school

47 82 71

23.5 41.0 35.5

93 107

46.5 53.5

Neighborhood characteristics Racial composition African American European American Racially mixed Average household wealth Lowest quartile ($62k or less) Lower middle quartile (>$62k–102k) Upper middle quartile (>$102k–144k) Highest quartile (>$144k) Family characteristics Relationship to child Mother Father Grandparent Other relative

Gender of target child Boy Girl

Discussion Social capital has gained a prominent place in recent research on the well being of populations (Kawachi & Berkman, 2000). Despite the presence of conceptual and methodological problems of many of the studies discussed earlier in this paper (e.g., lack of consistent definitions across studies), researchers have focused mainly on the salutary effects of communitarian social capital on individual well being (Putnam, 2000). We examined two aspects of family social capital, general sense of community and knowing neighbors, in relation

to behavior problems in preschool-aged African American children. By building on previous research on lowincome families, we hypothesized that some children may benefit from parents having weak attachment to their neighborhoods (Patillo, 1998). The first of the two indicators of social capital that we examined captured a general sense of community such as sharing values with neighbors, getting along and liking neighbors and feeling at home in one’s neighborhood. The second indicator captured the levels of interaction and familiarity with one’s neighbors. High values on the first indicator suggest that the respondent shares values and feels good about one’s neighbors, whereas high values on the second indicator suggest that the respondent has a high degree of familiarity with the people in the neighborhood. In our study, general sense of community was not strongly related to behavioral problems in preschoolers. In our bivariate analyses, general sense of community was moderately associated with total, internalizing and externalizing behavioral problems, but once we accounted for potential confounders in the regression (e.g., family poverty level) the association only remained for internalizing behaviors and was only significant at a po0:10 level. Knowing neighbors, however, was associated with children’s behavioral problems. In our regression analyses, we observed an interaction between whether parents knew their neighbors and level of neighborhood impoverishment. Specifically, among neighborhoods with low levels of impoverishment, behavioral problems were highest for those whose parents did not know many neighbors (low social capital). In fact, those families living in low impoverishment neighborhoods and who had low social capital had children with the highest levels of behavioral problems. This finding for higher income families was not anticipated. In neighborhoods with high levels of impoverishment, the lowest levels of behavioral problems were for those children whose parents did not know many neighbors (low social capital). Indeed, the lowest levels of behavioral problems for the sample as a whole were for those children living in high impoverishment neighborhoods whose parents had low levels of social capital. This interaction with neighborhood impoverishment illustrates the complexity of social capital and that high levels are not uniformly beneficial to individuals. Moreover, there are situations in which low levels of social capital as measured by levels of interaction with neighbors is protective of adverse outcomes. This finding supports previous qualitative work on this topic (Brodsky, 1996; Furstenberg, 1993). The primary limitation of our study was the crosssectional design which limits our ability to make any conclusions about the path of causality between family social capital and preschool behavioral problems. It is possible that knowledge of one’s neighbors is associated

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Table 4 Differences in child behavior problems and psychological sense of community by family and neighborhood characteristics Problem behaviors Total

Internalizing

X (SD) Neighborhood racial composition African American Racially mixed Neighborhood average wealth Lowest quartile Lower middle quartile Upper middle quartile Highest quartile Family structure Single-headed household Other Poverty status Less than 100% poverty 100–179% poverty 180%+ poverty Educational attainment Less than high school High school/GED More than high school Child gender Boy Girl

Psychological sense of community

F

X (SD)

Externalizing F

X (SD)

General F

X (SD)

Knows neighbors F

X (SD)

F

45.64 (11.11) 2.56 47.45 (10.46) 1.97 45.92 (9.89) 1.79 3.21 (0.96) 1.86 3.66 (0.94) 2.36 41.83 (9.26) 44.29 (9.29) 43.08 (8.38) 3.49 (0.88) 3.34 (1.18) 44.47 43.77 48.58 42.87

(11.89) 2.61a 45.35 (10.59) 3.57b 45.03 (9.99) 2.72b 2.79 (0.91) 11.84c 3.56 (1.04) 1.71 (10.58) 45.93 (11.08) 45.37 (9.74) 3.41 (0.88) 3.40 (1.02) (10.15) 50.92 (9.09) 48.35 (9.26) 3.25 (0.81) 3.70 (1.00) (10.19) 45.39 (9.92) 42.61 (9.32) 3.84 (0.92) 3.86 (0.72)

44.76 (10.75) 0.28 46.72 (9.97) 0.18 45.13 (9.45) 0.48 3.36 (0.97) 4.20 3.76 (0.95) 6.32b 45.61 (11.34) 47.36 (11.08) 46.12 (10.20) 3.07 (0.90) 3.40 (0.99) 46.31 (11.63) 2.50a 47.76 (11.00) 1.39 46.77 (10.62) 3.55b 2.98 0.92) 14.71c 3.61 (1.03) 0.89 46.16 (10.28) 47.88 (10.57) 46.69 (8.27) 3.10 (0.90) 3.49 (0.92) 42.48 (10.23) 45.12 (9.24) 42.77 (9.09) 3.78 (0.83) 3.74 (0.94) 44.16 (13.06) 0.25 47.09 (12.94) 0.01 44.71 (9.61) 0.24 3.00 (0.98) 3.41b 3.62 (0.97) 0.11 45.59 (11.08) 46.82 (10.10) 45.97 (10.47) 3.20 (1.00) 3.65 (0.92) 45.14 (9.41) 47.07 (8.99) 45.52 (9.06) 3.46 (0.83) 3.58 (1.06) 45.00 (10.88) 0.01 46.42 (10.39) 0.47 45.47 (10.17) 0.00 3.24 (0.94) 0.02 3.50 (1.02) 2.53 45.18 (11.09) 47.45 (10.43) 45.56 (9.4) 3.26 (0.97) 3.72 (0.93)

a

po0:10: po0:05: c po0:01: b

Table 5 Regression of problem behavior t-scores on neighborhood impoverishment, PSOC-G, and PSOC-K Total problem behaviors Variable

B

Constant 44.15 Family poverty level 0.007 1.43 Lower middle impoverishment quartileb Upper middle impoverishment quartileb 2.20 Highest impoverishment quartileb 1.81 PSOC General (lowest quartile) 2.81 PSOC Knows neighbors (lowest quartile) 6.50 Impov Q2  Low PSOC-K 9.37 Impov Q3  Low PSOC-K 6.78 Impov Q4  Low PSOC-K 13.19

SE(B) 2.56 0.009 2.61 2.71 2.88 2.11 3.92 5.85 5.68 5.86

Internalizing behaviors

t

B a

17.22 0.089 0.55 0.81 0.63 1.33 1.74c 1.60 1.19 2.25d

45.73 0.006 2.08 1.62 0.48 3.64 7.55 8.10 8.44 16.07

SE(B) 2.36 0.008 2.41 2.50 2.65 1.94 3.61 5.39 5.24 5.40

Externalizing behaviors

t a

19.36 0.76 0.87 0.65 0.18 1.87c 2.09d 1.50 1.61 2.98a

B

SE(B)

T

45.14 0.010 1.86 3.06 1.19 1.75 5.08 8.74 4.39 9.54

2.25 0.008 2.30 2.38 2.53 1.86 3.44 5.14 4.99 5.15

20.03a 1.34 0.81 1.29 0.47 0.94 1.48 1.70c 0.88 1.85c

a

po0:01: Reference group is lowest quartile of neighborhood impoverishment. c po0:10: d po0:05: b

with other parent characteristics, such as parenting attitudes and/or parent psychological status, and that the nature of the association is different in different neighborhoods. For example, in wealthier neighbor-

hoods, isolation from neighbors may be associated with higher levels of maternal depression. In turn, maternal depression is associated with higher rates of mental health problems in children (Campbell, Cohn, &

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Fig. 1. Problem behavior scores for low PSOC-K by impoverishment for (a) total problem behaviors; (b) internalizing problems; and (c) externalizing problems.

Meyers, 1995; Nolen-Hoeksema, Wolfson, Mumme, & Guskin, 1995; Teti, Gelfand, Messinger, & Isabella, 1995; Webster-Stratton & Hammond, 1988). In contrast, in poor neighborhoods, lower levels of social capital may be associated with better developed coping skills on the part of parents. Parents who are better able to cope with living in highly impoverished neighborhoods may be better at supporting the healthy development of their children. Future research should

incorporate a longitudinal design as well as explicit measures of these processes in order to tease out the association between family social capital and child outcomes in poor neighborhoods. Another limitation of this study is the use of census block groups as a proxy for neighborhood. Appropriately defining neighborhoods has been a methodological limitation of much of the neighborhood research which has attempted to examine how neighborhood

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characteristics affect individual behavior (Tienda, 1991). Neighborhoods have been defined as ‘‘physically bounded areas characterized by some degree of relative homogeneity and/or social cohesion’’ (White, 1987, p. 3 as cited by Tienda, 1991). The social dimension of neighborhoods is crucial because it is derived from the interaction patterns among the residents which are the most likely mechanism by which neighborhood characteristics are transmitted into individual outcomes (Tienda, 1991; Jencks & Mayer, 1990). Although boundaries of census tracts are intended to conform to neighborhood boundaries, our own experience indicates that census tract boundaries are often inconsistent with residents’ perceptions of the spatial limits of their neighborhood. Census block groups, because of their smaller size, are more consistent with neighborhood boundaries. Thus, we chose to use census block groups as a proxy for neighborhood because available secondary data used to characterize the neighborhoods are available by census block group and because census block groups are more homogeneous than census tracts (Krieger, 1991). However, to the extent that census block groups do not conform to the perceptions of neighborhood boundaries of our participants, our attempts to create neighborhood indicators from the perceptions of residents will not be accurate. Furthermore, the accuracy with which individual perceptions represent a valid measure of neighborhood context is also a function of individual characteristics such as length of residence in the neighborhood. One potential confounder that we were unable to include is residential stability or migration patterns of our study neighborhoods. High levels of migration into or out of the neighborhood may impact levels of social cohesion and community attachment, but we did not have access to recent data to include in our analyses (e.g., 2000 Census data). A recent study on neighborhood disorder, however, showed that neighborhood migration was not associated with social ties (Ross, Reynolds, & Geiss, 2000). One of the strengths of our study is that the sample spans the whole range of socioeconomic position for the city. Therefore, we were able to identify differences in the ways in which social capital was operating across various levels of neighborhood impoverishment. Many studies focus within one level of social class and therefore are unable to identify the interaction that we observed here. Another strength of our study is the focus on a wholly African American sample of participants. More studies of neighborhood social processes of which are limited to a single race/ethnic group are needed because, as stated by Sampson & Morenoff (1997, p. 5), ‘‘race and individual outcomes are systematically confounded with important differences in community contexts’’. Furthermore, more effort should be undertaken to examine social capital among African Amer-

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icans in other urban and non-urban settings to determine whether these same patterns hold. Finally, there is a need for studies to examine whether social capital operates in similar ways for other race and ethnic groups. This study also makes important contributions to our understanding of the processes which support or hinder the development of young African American children. The extant literature on the development of African American children is dominated by studies of children living in poverty. In addition, there has been a call to extend the research on children of color to include explicit consideration of the unique ecological niches in which they live including characteristics of families and neighborhood conditions. We are unaware of any other studies which include a socioeconomic diverse sample of African American families and also incorporates contextual information about the neighborhoods in which these families live. Taken together, our paper supports a growing literature questioning the simplicity of the effects of social capital on well-being (Lynch et al., 2000; Hawe & Shiell, 2000; Cattell, 2001; Muntaner, Lynch, DaveySmith, 2001). Specifically, we found that high levels of social capital in highly impoverished communities is associated with higher levels of behavioral problems in preschoolers, and low levels of social capital is associated with lower levels of behavior problems. Thus, the notion of low social capital and its potential salutary effects in some situations deserves greater attention in future studies.

Acknowledgements This research was supported by grant #MCJ-24073101-1 from the Maternal and Child Health Bureau. The authors would like to thank Deborah Brothers and Bennette Drummond-Fitzgerald for conducting interviews, and Kimberly Lohrfink for providing project management. Data management and analysis support was expertly provided by YiHua Chen, Crystal Evans, Patricia Gwayi-Chore, and LiChing Lee. Finally, we would like to thank the families who so graciously welcomed us into their homes.

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