Social capital and homeownership in low- to moderate-income neighborhoods.
This study examined the relationship between homeownership and social capital among low-and moderate-income (LMI) households. Using data from the Community Advantage Panel Study, the authors used propensity score weighting and regression analyses to explore the relationship between LMI homeownership, neighborhood conditions, and social capital. After controlling for several important individual-and neighborhood-level characteristics, the authors found that homeownership is related to greater access to social resources in general but not to social resources within the neighborhood. Instead, resource generation within the neighborhood is largely predicted by neighborhood stability and perceived neighborhood size. Policy implications are discussed.
KEY WORDS: Community Advantage Home Loan Secondary Market Program; homeownership; low-income; resource generation; social capital
Homeownership has long played an integral role in wealth and asset accumulation, community growth, and the promotion of positive social outcomes for individuals and families in the United States (Retsinas & Belsky, 2002; Rohe & Stewart, 1996; Shlay, 2006). Since the 1930s, government policies such as tax incentives, subsidy payments, and market regulation have promoted homeownership as a vehicle to stimulate economic growth (Carliner, 1988). Since the 1980s, they have also been used to spur community redevelopment and provide improved housing options for low-income families (Shlay, 2006).
In light of economic and racial disparities in homeownership rates (Collins, 2002; Herbert, Haurin, Rosenthal, & Duda, 2005; Williams, Nesiba, & McConnell, 2005), recent economic and social policies have focused on promotion of homeownership as a way to provide long-term economic stability and other benefits to low- and moderate-income (LMI) families who may not have benefited from earlier policies. As a result, the rate of entry into homeownership for low-income and minority households has been increasing faster than that for other groups (Belsky & Duda, 2002).
Research has supported the belief that the benefits of homeownership, such as life satisfaction and neighborhood stability, transcend wealth accumulation for both homeowners and communities (Rohe, Van Zandt, & McCarthy, 2002), although this body of research has primarily focused on middle-and high-income populations. It is important to examine whether LMI individuals and families also experience the positive benefits of homeownership found for middle-and higher-income households. Furthermore, the recent housing crisis, which has been blamed in part on the expansion of risky mortgages to LMI borrowers, underscores the need for a careful look at the costs and benefits of LMI homeownership. This study focused on one perceived benefit of homeownership: its potential impact on access to social capital. Specifically, using data from the 2007 Community Advantage Program (CAP) panel survey and the 2000 U.S. Census, this study examined the differences in the level of access to social resources between LMI homeowners and LMI renters and what role neighborhood economic and social conditions play in the relationship between resource generation (RG) and homeownership.
Research has sought to identify externalities of homeownership and the potential social benefits that help determine the overall impacts of promotion of homeownership. Rohe et al., (2002) conducted a critical assessment of the research on the costs and benefits of homeownership and concluded there is evidence of positive benefits of homeownership, but they also cautioned that the potential negative impacts should be investigated further. Numerous studies lend support to the belief that homeownership is related to increased involvement in local organizations, neighborhood stability, local problem solving, and satisfaction (DiPasquale & Glaeser, 1999; Rohe & Stegman, 1994b; Rohe & Stewart, 1996).
Several studies on the relationship between homeownership and social capital have focused on the levels of participation in organizations as an indicator of investment in the neighborhood (DiPasquale & Glaeser, 1999; Rohe & Stegman, 1994b). In a longitudinal study on social capital, Rohe and Stegman (1994b) examined a sample of 143 low-income homebuyers participating in an affordable homeownership program in Baltimore, Maryland, sponsored by the Enterprise Foundation. A comparison group included 140 randomly selected, non-elderly, Section 8, low-income renters. They interviewed participants on a series of measures of community involvement while controlling for characteristics such as income, age, and education. They found that homeowners were more likely to participate in neighborhood and block associations but not in other community organizations, such as political or social organizations, compared with a matched set of renters. However, they also found that homeowners had lower levels of involvement with neighbors, suggesting that homeowners may be more likely to participate in formal social interactions at the neighborhood level but less likely to participate in informal social interactions.
Rohe and Stegman (1994b) offered two explanations for these findings. First, the significant number of new homeownership units clustered together may have limited the existing informal networks that typically help integrate new residents into neighborhoods. Second, homebuyers are thought to have a higher economic interest in the conditions of the local area and thus may be more likely to participate in formal interactions such as block associations.
Other studies on the effects of homeownership on both informal and formal local social interaction over time have supported the finding that homeownership is related to increased formal social interactions but not informal ones (Rohe & Basolo, 1997; Rossi & Weber, 1996). Rossi and Weber used three public use data sets--the General Social Survey, the National Survey of Families and Households, and the American National Election Studies--to identify social correlates of homeownership. They found that renters are shown to be more sociable than owners in terms of spending time with neighbors, coworkers, or friends. Thus, homeowners are less neighborly than renters. However, the authors cautioned that the homeowners and renters in this sample differed in important ways, but only age and socioeconomic status were controlled for, so other variables could help explain the differences. For example, the authors suggested that homeowners may have more children in the household and thus may be more likely to interact socially with family rather than neighbors. Rossi and Weber concluded from their research that the overall social effects of homeownership are minimal and inconsistent.
SOCIAL CAPITAL THEORY
The concept of social capital has been defined in myriad ways and can be conceptualized as either an individual resource (connections to others that yield material or social benefits; Bourdieu, 1985) or a collective resource (shared stocks of trust that contribute to collective problem solving; Putnam, 1995). For the purposes of this study, we focused exclusively on individual social capital as defined by Van der Gaag and Snijders (2004): Social capital is "the collection of resources owned by the members of an individual's personal social network, which may become available to the individual as a result of the history of these relationships" (p. 200).
The present study draws on network theory (Lin, 1999), which characterizes social capital as assets in networks by highlighting investment in social relations with expected returns. Network theory, as defined by Lin, differentiates the access and the use of social capital. Access-based social capital is a collection of potentially usable social resources. Use-based social capital, on the other hand, refers to actions and consumption of resources to generate returns (Lin, 2001). We operationalize access-based social capital in this study as RG, which focused on the degree to which respondents had access to various social and economic resources through social networks. This study used a modified version of the Resource Generation instrument developed by Snijders (1999), a version based on Lin's network theory, as a way to conceive of social capital as an individual pool of resources embedded in personal networks.
The theoretical relationship between social capital and homeownership has been explained in terms of financial motivation. The economic rational choice theory posits that homeowners have a higher financial stake in their neighborhoods and communities because house values are affected by neighborhood and community vitality. Thus, homeowners are more likely than renters to get involved in politics, community organizations, or other efforts to improve their neighborhoods in order to protect their investment (DiPasquale & Glaeser, 1999; Herbert & Belsky, 2006). For example, local land-use decisions affect property values, thus homeowners have a higher economic stake in these political decisions. Likewise, neighborhood safety is likely to affect home values, thus encouraging homeowner involvement in neighborhood watch groups. However, research that examined economic reasons for home buying and social capital found no evidence to support the economic rational choice theory, meaning that economically motivated homebuyers were no more likely than buyers with other motivations to have higher social capital (DiPasquale & Glaeser, 1999; Rohe & Stegman, 1994a).
A second explanation, related to financial motivations that developed as part of the rational choice theory, is the cost of moving, which is typically higher for homeowners than renters. Due to the high transaction costs associated with moving, homeowners may be more likely to improve and maintain neighborhoods than to move away (DiPasquale & Glaeser, 1999; Herbert & Belsky, 2006). This increased involvement and residential stability mean that homeowners develop greater personal and emotional connections to neighbors, their homes, and the area. These connections motivate them to form ties with others (DiPasquale & Glaeser, 1999; Herbert & Belsky, 2006; Retsinas & Belsky, 2002). These ties to others may provide homeowners increased access to social capital, which is to say access to social resources held by others.
LMI homeowners, who are more restricted than other homeowners to purchasing housing in affordable neighborhoods, are likely to differ in important ways from middle- and upper-income homeowners. It is therefore unclear whether the relationship between homeownership and social capital will hold for this particular group. One way that LMI homeowners are likely to differ from homeowners in general is with respect to the characteristics of the neighborhoods in which they reside. LMI homeowners in impoverished neighborhoods may experience a different set of social processes that result in a different relationship between homeownership and social capital than that seen in existing literature for middle- and upper-income homeowners.
In fact, some research has found that neighborhood poverty is negatively related to available social capital. For example, Caughy, O'Campo, and Muntaner (2003) found that in high-poverty neighborhoods, individuals' general sense of community was lower. Sampson, Morenoff, and Garmon-Rowley (2002) provided insight into one potential reason for this relationship, in that they characterized high-poverty neighborhoods as centers of a variety of social problems, including homicide, suicide, infant mortality, low birth weight, teenage pregnancy, and sexual risk taking (Sampson, 2002). As a result, residents of such neighborhoods may feel less comfortable nurturing ties with one another, which would lead to low social capital. Low social capital is further theorized to be predictive of higher rates of crime (Sampson & Groves, 1989). This suggests that there may be a damaging negative feedback loop in high-poverty neighborhoods where crime and other social problems result in reduced social capital, creating a disorganized environment in which social problems increase. If such a characterization of high-poverty neighborhoods is accurate, we would not expect increased social capital for homeowners living in those neighborhoods. Limited mobility due to homeownership may impose financial costs that exceed any increased benefits of social capital (DiPasquale & Glaeser, 1999) by preventing these households from leaving a neighborhood that is experiencing increasing disinvestment and depreciation (iKohe, Van Zandt, & McCarthy, 2002).
On the other hand, some literature has suggested that more economically disadvantaged neighborhoods have high levels of socially supportive networks and resources. One example is the ethnographic work of Stack (1974), who found that lower-income individuals tended to develop strong social support networks to cope with the uncertainties of economic hardship. In a quantitative study, Boisjoly, Duncan, and Hofferth (1995) similarly found that families in very poor neighborhoods reported access to increased social capital from relationships with friends and lower social isolation. However, Boisjoly et al. defined social capital as access to time and monetary help from friends and family as measured through the individual's perceived access to social capital, not whether the individual actually received the help. Thus, the contradictory findings of high social capital in these studies and not in others could be due to the fact that social capital is defined and measured differently. If neighborhood disadvantage does contribute to higher levels of social capital, then we would expect LMI homeownership in disadvantaged neighborhoods to show a similar or stronger positive relationship between homeownership and social capital, as has been shown in higher-income homeowners.
Of course, the relationship between neighborhood disadvantage and social capital is likely more complicated than simple positive or negative associations. Other variables, such as the overall stability of the neighborhood and the mobility of the individual in question, likely have an effect on one's social capital. The influence of these variables is supported by findings that residential stability is related to local friendship networks and fewer social problems (Sampson & Graft, 2009; Sampson et aL, 2002). Furthermore, Greenbaum (2008) found that low-income fanfilies forced to move to more advantaged neighborhoods as a result of public housing demolition actually had much lower social capital after the move due to the disruption of their existing social networks. Such research suggests that individuals with homes in disadvantaged yet relatively stable neighborhoods may have higher levels of social capital, whereas homeowners who have recently moved into a neighborhood may have lower levels of social capital, regardless of other neighborhood factors.
Furthermore, along with the difficulty of disentangling the effects of neighborhood characteristics from homeownership, research has suggested that the effects of neighborhood characteristics may vary by group. Harkness and Newman (2003) used data from the Panel Study of Income Dynamics (waves 1968-1993) to compare results between homeowners and renters while allowing for the interaction between tenure and neighborhood characteristics. To follow up on research that suggested that growing up in a home-owning family confers benefits on children, their research focused on children between the ages of 11 and 15 years. Their results showed that neighborhood characteristics mattered more for the children of homeowners than for children of renters. Thus, homeowner children were more negatively affected by neighborhood distress and more positively affected by the level of homeownership in the neighborhood than renter children.
Three issues arise when examining the literature on the relationship between homeownership and social capital: (1) the need to differentiate between the effect due to the homeownership rate and that due to residential stability (DiPasquale & Glaeser, 1999; Harkness & Newman, 2003); (2) self-selection bias: people who are naturally more likely to be homeowners (those who are employed, married, and have higher income) may also have higher levels of social capital (Rohe et al., 2002); and (3) the neighborhood-level social and economic characteristics that may influence the degree to which residents participate in the civic life of their neighborhood (Rohe et al., 2002). Our study aimed to address these issues in the following ways. First, we controlled for neighborhood stability through two indicators, percentage of residents living in the same house at least five years and percentage of owners in the neighborhood. Second, to control for selection bias, this study used propensity score weighting (PSW) to match homeowners to renters. Third, we directly tested whether neighborhood conditions such as poverty, unemployment, and high mobility had an impact on the social resources held by those in the neighborhood.
Social capital has been used to explain outcomes at both the individual (Hao, 1994) and community (Putnam, 1995) level. Although researchers have extensively documented what social capital does for people, we know less about how it is generated in neighborhoods and communities. This study aimed to examine the relationship between LMI homeownership and social capital and to explore neighborhood effects on social capital. Based on the theoretical framework outlined previously, we developed the two following research questions: (1) Do LMI homeowners report having access to greater social capital than a comparable group of renters in models controlling for individual and neighborhood characteristics? and (2) Are neighborhood characteristics related to the social capital of LMI homeowners and renters?
Data and Sample
This study used data from the 2007 CAP panel survey for individual-level information and data from the 2000 U.S. Census for neighborhood-level information. CAP is a secondary-market mortgage pilot program for LMI households. It enables borrowers with lower credit scores to obtain prime financing for homeownership. To qualify for CAP, applicants must meet at least one of the following three criteria: (1) have an income less than 80% of the area median income (AMI); (2) have racial or ethnic minority status and income less than 120% of AMI; or (3) purchase a home in a high minority (greater than 30% concentration of minority populations) or low-income (less than 80% AMI) census tract area and have an income less than 120% of AMI.
Started in 1994 in North Carolina by the Self-Help Ventures Fund, a community development financial institution, CAP expanded nationally in 1998 through a partnership with the Ford Foundation and Fannie Mae (Federal Reserve Bank of Philadelphia, 2004). The goals of CAP are to demonstrate the credit worthiness of LMI borrowers to secondary mortgage lenders and to provide evidence to lenders and policymakers that LMI borrowers are "bankable." Through an annual panel survey, CAP also provides important information about the effects of LMI homeownership on a variety of economic and social outcomes.
Since its inception, the program has funded more than 46,000 mortgages across the United States. The median loan amount is $78,800, and the median income of a CAP borrower is 60% of AMI. Thirty-nine percent of the borrowers are minority, and 44% are female-headed households. More than half (52%) of the loans have an original loan-to-value ratio of 97% or above, and 44% of the borrowers had a credit score below 660 at origination.
The CAP panel survey, conducted by Research Triangle Institute International, consists of six annual in-depth telephone interviews of a sample of CAP homeowners and a matched comparison group of renters. The renter panel was chosen to match the homeowners in the owner panel based on neighborhood location and income criteria of the CAP program. The annual survey started in 2003 for the homeowner panel and in 2004 for the matched renter panel. Because of the large number of study participants and the rigorous design, CAP provides an excellent opportunity to advance research on LMI homeownership.
The 2007 CAP panel survey included 2,071 respondents from the owner panel and 893 from the renter panel. Among them, respondents who changed homeownership status since the first wave of the CAP survey were removed from the analysis for this study (n = 361). Thus, the analysis sample in this study included only respondents who were homeowners for at least four years or renters for at least four years. There were no significant differences in individual characteristics and RG between the final sample and the 361 excluded cases, except with regard to age. The analysis sample was older (M= 34.72, t = -3.54, p < .001) compared with the excluded cases (mean = 34.72). We also excluded 23 respondents over the age of 65 from the analysis because of skewed distribution of age and the small frequency. Further, we removed 564 respondents reporting household income greater than $60,280 (that is, 120% of the 2007 U.S. median household income). Trimming of household income ensured that our sample was composed of LMI households. Therefore, the final analysis sample included 1,918 LMI respondents (1,235 homeowners and 683 renters) from 1,452 census tract areas.
In the analyses presented here, R,G was used as a measure of social capital (Snijders, 1999) that focused on the degree to which respondents had access to various social resources through their social networks. There were two dependent variables for this study: (1) within-neighborhood RG (neighborhood R,G) and (2) general resource generation (general RG). Scales measuring both general RG and neighborhood RG consisted of eight items, and both scales appeared to have good internal validity and consistency. Factor analyses indicated that the eight items for both general RG and neighborhood R,G comprised a single factor for each concept, confirming that all eight items measure the same concept. Subsequent reliability tests also showed a good reliability for both general RG ([alpha] = .81) and neighborhood R,G (ix = .80).
For neighborhood R,G, the respondent was asked if she or he knew anyone outside the household, but within the neighborhood, who could provide a given resource. Response options for each measure of neighborhood RG were dichotomous (0 = no; 1 = yes), and the variable neighborhood R.G was a composite score of eight dichotomous items. For general RG, the respondent was asked the number of people they knew who could provide them with a given resource. The responses excluded household members but included people both within and outside a respondent's neighborhood. To make general RG easily comparable with neighborhood RG, responses to general P,G items were converted to dichotomized measures of whether or not respondents knew one or more people who could provide a given resource. Thus, like neighborhood P,G, the outcome variable used for general P,G in these analyses was a composite score of eight dichotomous measures.
The independent variables for this study included various individual characteristics and neighborhood conditions (see Table 1 for descriptive statistics). Individual-level independent variables included the following 12 variables: (1) tenure (0 = renters; 1 = owners); (2) gender (0 = female; 1 = male); (3) age (in years); (4) a set of dummy variables indicating race/ethnicity: white (the reference group), black, Hispanic, and any other race/ethnicity; (5) a set of dummy variables for education level: no high school diploma, high school diploma or GED (the reference group), some college, and bachelor's degree or more; (6) a dichotomous variable for the employment status of the respondent (0 = non-employed; 1 = employed); (7) a set of dummy variables for marital status: partnered, married (the reference group), separated/divorced/widowed, and never married; (8) the number of adults in the household; (9) the number of children in the household; (10) a set of dummy variables for total annual household income: less than $10,000 (the reference group), $10,000-$19,999, $20,000-$29,999, $30,000-$39,999, $40,000-$49,999, and $50,000 or more; (11) a set of dummy variables for perceived neighborhood size, representing how respondents defined their neighborhood: the block or street you live on (the reference group), several blocks or streets in each direction, the area within a 15-minute walk, and an area larger than a 15-minute walk; and (12) a dichotomous measure of moved to a new neighborhood since the last survey wave (0 = no; 1 = yes), to adjust for the length of time living in the neighborhood.
In addition to various individual characteristics, this study also included perceived neighborhood size and recent movers as control variables. It is reasonable to expect the respondents who conceived a broader neighborhood boundary to have more social resources within the neighborhood than the respondents who defined a narrower neighborhood boundary. Thus, it was important to control each respondent's different definition of the neighborhood based on size. It was also important to control the length of time in which a respondent had resided in the neighborhood. The recent movers into a neighborhood would be expected to have a lower level of social resources within the neighborhood than their counterparts.
In this study, neighborhood information came from the 2000 U.S. Census. Census tract information was assigned to each respondent on the basis of his or her address reported at the time of the survey. Two neighborhood variables were the concentrated economic disadvantage (CED) scale (Caughy, Hayslett-McCaU, & O'Campo, 2007; Sampson, Raudenbush, & Earls, 1997) and the neighborhood stability scale (Morenoff, Sampson, & Raudenbush, 2001; Swaroop & Morenoff, 2006). Among various neighborhood characteristics, these two neighborhood variables were selected to represent neighborhood conditions in our study because neighborhood economic surroundings and neighborhood stability were expected to be important neighborhood factors related to social capital, as suggested by previous studies.
The CED scale represents the relative economic condition of a neighborhood and was constructed using the following four census tract indicators: percentage of individuals below the poverty line, percentage of people receiving public assistance, percentage of people unemployed, and percentage of female-headed households with children. To develop the CED scale, each indicator was first standardized and then a composite score was divided by 4, the number of indicators ([alpha] = .91). The population stability scale was developed by combining two census tract indicators: percentage of homeowners and percentage of residents who lived in the same house for at least five years. These two measures were standardized and combined into a composite score, which was then divided by 2, the number of measures ([alpha] = .68).
Although the CAP panel survey was intended for LMI homeowners and renters to be matched in terms of income eligibility for CAP program and location, they appeared to differ greatly on almost all individual characteristics (for example, gender, race, marital status, income). The imbalance between the two groups raises questions about the potential effects of sample selection and endogeneity.
Self-selection bias exists to the extent that LMI people in our sample with the ability to more easily access and form social capital may have also been more likely to have been homeowners at baseline. In other words, homeownership can be both a cause and a product of social resources. Tenure would be endogenous if the decision or ability to be a homeowner was correlated with observable or unobservable factors that affect the level of social capital.
In the absence of random assignment of tenure status at baseline, the estimate of causal relationship from tenure to social resources would be questionable. Of course, housing tenure cannot be assigned randomly. Alternatively, this study applied propensity score analysis to remedy potential problems of sample selection and endogeneity in the data (Freedman & Berk, 2008; Leslie & Ghomrawi, 2008). Among various propensity score methods, this study used PSW for the following reasons. First, most other propensity matching methods (for example, kernel matching, full optimal matching, matching estimator) do not produce a coefficient of each covariate. Because we were interested in neighborhood effects and a tenure effect on resource generation, presenting estimations of neighborhood effects after propensity score analysis was critical. Second, PSW handles non-normal distributed outcomes unlike other matching methods. Third, one-to-one matching (for example, greedy matching) also provides an estimation of each covariate and works with non-normal distributed outcomes. However, it substantially decreases a sample size, which can be criticized for sample representativeness. PSW in our data was found to be efficient at making the sample balanced (see Appendix A); thus, we used it for our propensity score scheme.
Regression adjustment with PSW proceeded as follows. A logistic regression predicting group membership (homeowner versus renter) was conducted to estimate a propensity score, which was the conditional probability of a participant to be a homeowner and based on observable characteristics. Logistic regression included the same set of independent variables as those later used in regression models to predict RG. Propensity score weights were calculated as the inverse of the propensity score for the treatment group (that is, homeowners) and as the inverse of one minus its propensity score for the nontreatment group (that is, renters).
Because our dependent variables were count variables with overdispersed distribution, negative binomial regression was used to assess the relationship between tenure and RG. This study ran three models for each outcome. The first model, which controlled for individual and neighborhood characteristics, was conducted without applying PSW. This model did not account for the endogeneity and sample imbalance issue. The second model added PSW to remedy possible endogeneity and sample selection problems. To explore whether neighborhood stability and economic conditions alter the significance of the relationship between tenure and P,G, a third model was run with PSW, but it did not include neighborhood characteristics. The strength of relationships between covariates and RG was evaluated using incidence rate ratios (IRKs), calculated by exponentiating the negative binomial regression coefficient. IRPs can be interpreted as the relative change in the incidence rate of outcome variable Y for a one-unit change in a given independent variable X.
Differences of sample characteristics between LMI homeowners and LMI renters are presented in Table 1. In spite of efforts to match LMI homeowners with LMI renters in terms of income eligibility for CAP program and location, the differences between homeowners and renters in our sample were significant. For all individual indicators, homeowners had different socioeconomic profiles than renters. The majority of owners were male, white, and married. However, most renters were female, nonwhite, and not married. Most owners were located in higher income categories, but the majority of renters were in lower income categories (p < .001). Regarding census-level indicators, homeowners lived in less economically disadvantaged (p < .001) and more stable (p < .001) neighborhoods than renters.
To account for the variety of differences between homeowners and renters, PSW was used. The success of this technique in balancing the sample through a series of single logistic regressions predicting homeownership is demonstrated in Appendix A. Utilization of logistic regression is recommended for checking sample balance after PSW (Guo & Fraser, 2010). Without propensity score weights, most sample characteristics were significant predictors of homeownership, implying heavily sample imbalance between the two groups, owners and renters. Once propensity score weights were applied to a series of weighted simple logit regressions, all individual and neighborhood variables became nonsignificant predictors of homeownership.
Overall differences in RG between homeowners and renters with and without accounting for propensity score weights are presented in Table 2. Without PSW, homeowners had higher scores than renters on neighborhood RG and general RG. For neighborhood RG, most items were significantly higher for homeowners than for renters. However, no significant difference existed between the two groups when asked if they knew anyone in the neighborhood who is good with computers or if they knew anyone in the neighborhood who provides good advice about new job opportunities. For general RG, all eight items were significantly higher for homeowners than for renters. The largest difference between the two groups was with regard to whether respondents knew someone who would lend them $500 if needed.
After we applied PSW, the differences in RG indicators between the two groups became non-significant for most measures. Exceptions were found in the following three measures, for which homeowners still had higher percentages than renters: (1) if they knew anyone who would help them move to a new home (p < .05), (2) if they knew anyone who would lend them $500 (p < .05), and (3) if they knew anyone active in a political party (p < .05).
The results of negative binomial regression models predicting neighborhood RG are presented in Table 3. The first model, which did not include a propensity score weight, supported the hypothesis that there is a significant difference in the level of social capital (that is, RG) between LMI homeowners and renters. However, after adding propensity scores to the model, the significant effect of tenure on neighborhood RG disappeared, suggesting that the original effect was due to differences between homeowners and renters on characteristics other than homeownership. To explore the concurrent effect of neighborhood characteristics and tenure, propensity score-weighted models were specified both with and without measures of neighborhood disadvantage and stability. Across models, the magnitude and significance of the effect of tenure on neighborhood RG did not appear to be meaningfully changed by the presence or absence of these variables.
Turning our attention to the full propensity score-weighted model, perceived neighborhood size and neighborhood stability were the only significant predictors of neighborhood RG. Neighborhood stability was positively related to neighborhood RG; all other variables in the model were held constant. In addition, respondents in neighborhoods that were perceived to be larger than a 15-minute walk in size had a rate 1.5 times greater for the neighborhood RG compared with respondents in neighborhoods that were only measured as a block or street in size.
The model that included interaction terms between neighborhood conditions and neighborhood RG is not presented because of nonsignificant results. In other words, the relationship between tenure and neighborhood RG did not differ by the level of neighborhood conditions.
Results of a similar series of regressions predicting general RG are presented in Table 4. Unlike the effect in models predicting neighborhood RG, the positive effect of tenure remained significant even after the addition of propensity score weights. By comparing the full and reduced propensity score-weighted models, one can see that the addition of neighborhood disadvantage and stability variables resulted in a negligible change in the effect of tenure on general RG, indicating that the effect of tenure on general RG was independent of these neighborhood characteristics.
In fact, based on the full propensity score--weighted model, neighborhood stability appeared to be a significant predictor of general RG (p < .05). Further, a variety of individual characteristics contributed to general RG. Being male was negatively related to general RG, as was being partnered rather than married. Hispanic respondents had a rate 9% lower for general RG scores compared with white respondents. This is an unexpected finding given past research on the importance of familism (which emphasizes high levels of support among family members) in Hispanic culture, which should theoretically result in greater-than-average social capital (Sabogal et al., 1987). However, this may be explained by the way general RG was measured; it did not include resources generated in the household, which for Hispanics may include extended family. In addition, the focus on familism in Hispanic culture may be offset by increased access to nonfamily sources of social capital in non-Hispanic groups.
In addition, increasing levels of education were positively related to increased general RG, such that high school graduates had a rate 1.10 times greater for the general RG compared with respondents with less than a high school degree (p < .01), whereas respondents with a BA degree and more had a rate over 1.17 times the general RG of a respondent without a high school degree (p < .001). Employment had a positive effect on general RG.
Similar to neighborhood RG, we did not find significant interaction terms between neighborhood conditions and general RG. This indicates that the positive effect of homeownership on general RG was consistent across different neighborhood conditions.
This study examined social capital among a sample of LMI homeowners and a comparison panel of LMI renters. The sample was drawn from 20 states and the District of Columbia as part of CAP, a secondary-market mortgage pilot program of a leading community development financial institution. Because CAP offered LMI individuals prime-like mortgages similar to those that are usually provided to individuals with higher incomes and good credit, the study findings are not complicated by potential negative effects of costly sub-prime mortgage products. Specifically, the study tested two main questions: (1) Do LMI homeowners have access to greater social capital than a comparable group of renters? and (2) Are neighborhood characteristics related to the social capital of LMI homeowners and renters? For the purposes of this study, social capital was measured as the degree to which respondents had access to various social resources through their social networks, and access was defined as 1LG (Snijders, 1999). The respondents' RG was assessed both within their neighborhoods and in general.
Without controlling for imbalance between homeowners and renters, we found that homeownership was significantly and positively related to both neighborhood and general 1LG. With regard to neighborhood characteristics, both neighborhood characteristics (disadvantage and stability) were positively related to neighborhood RG. However, only neighborhood stability significantly predicted greater general ILG.
Because the sample of LMI homeowners and renters differed on important key variables (for example, education, income, employment, and marital status), which implies that self-selection bias and endogeneity problems exist in the data, we used PSW to create a more balanced sample and to account for self-selection and endogenity issues. After we reran the analysis using PSW, the results were mostly consistent for general RG. Homeownership and a variety of individual characteristics (for example, marital status, ethnicity, and gender) and neighborhood stability remained significant predictors of general RG. With regard to neighborhood RG, applying PSW produced a different result regarding the relationship with tenure and neighborhood characteristics. After we controlled for imbalance between homeowners and renters, homeownership was no longer a significant predictor of neighborhood RG, and neighborhood size and stability were the only significant predictors of neighborhood RG.
There are several explanations of our finding that homeownership affected general RG but not neighborhood RG. First, the relative boundaries of the neighborhood could affect the findings. Past studies on homeownership and social capital have found that homeowners are less likely to have informal exchanges with neighbors (Rohe & Stegman, 1994b; Rossi & Weber, 1996) but are more likely to be invested in the community by belonging to nonprofessional organizations, knowing their elected officials, and voting in local elections (DiPasquale & Glaeser, 1999). Given that roughly 76% of study respondents identified their neighborhoods as less than or equal to a 15-minute walk in size, these forms of social investment will likely not take place within the boundaries of a person's neighborhood. Our findings support this idea that homeownership increases access to a wider social network that may not otherwise be encountered at the doorstep or in day-to-day life.
Second, general RG was calculated as a person's combined social capital both within his or her neighborhood and across the community as a whole. The survey items used to generate general RG asked if a person knew anyone who could help them access a given resource. Thus, the combined social capital, both within and outside the neighborhood boundaries, accounted for the significant difference between homeowners and renters in access to social capital.
Looking at our second research question, we found that the effect of neighborhood characteristics on social capital appeared to be limited. Interestingly, this study found that, without PSW, higher neighborhood disadvantage was related to higher within-neighborhood P,.G, but these findings did not hold when PSW was applied. Previous work has suggested that, within economically disadvantaged neighborhoods, residents display close and strong relationships and often rely on one another to survive on limited resources (Boisjoly et al., 1995; Stack, 1974). On the other hand, one potential concern is that LMI households may be limited to buying homes in disadvantaged neighborhoods and may not reap the same economic or social benefits of homeownership as those in less distressed areas (Rohe et al., 2002). Additional research is needed to further isolate and examine the effects of neighborhood characteristics on RG.
The results of the study should be viewed in light of its limitations. First, this study was based on a cross-sectional design. Although the CAP survey is an annual panel survey that assesses universal core items (for example, demographics, employment, household expenses) every year, data on RG variables were collected in the 2007 CAP survey only. Thus, this study cannot take full advantage of the longitudinal panel data. Second, because of data limitations, this study did not include the actual length of time that respondents had lived in their homes and neighborhoods as an important covariate. Instead, we included a dichotomous measure of moving to a new neighborhood since the last survey wave, which only indicated whether a respondent had moved in the past year. Third, our study sample came from the CAP participants (that is, owners) and from renters matched to them by location and income eligibility. Thus, our sample is not representative for the general LMI households in the United States. However, CAP survey participants are similar to comparable Current Population Survey LMI respondents regarding characteristics such as household size, income distribution, and minority representation (Riley, Ru, & Quercia, 2009). Fourth, as in other studies using the P-,G scale, this study equally weighted each RG question in the composite. However, among the questions in the RG scale, certain items (for example, who could help you by lending money, or helping with a job) may represent more important questions in terms of social capital than other items among LMI households. Although we could not find a theoretical or empirical basis to support our concern, it would be worthwhile to focus on this issue in a future study by evaluating different weights of each item in term of the contribution to asset building.
Last, homeowners in our sample had significantly better social and economic backgrounds than renters (see Table 1). Thus, we adopted PSW to remedy the concern about the sample imbalance and endogeneity issue between homeowners and renters. After using PSW, the two groups appeared to be balanced and comparable in regard to the sample characteristics measured in this study. However, the two groups may still differ by characteristics not measured in this study (for example, personal character, community commitment and activity, and acculturation). Therefore this study is limited because it is not based on a randomized sample design, which is not feasible for evaluating social outcomes between homeowners and renters. To lessen possible sample imbalance on unmeasured characteristics in this study, future studies should include more complete characteristics when propensity score analysis is adopted.
The results of this study suggest important implications for policies encouraging LMI homeownership. As the recent housing crisis has shown, all homeownership is not created equal (Ding, Quercia, Li, & Ratcliffe, 2010). The CAP homeowners in this study were able to secure good prime loans that the housing market might not otherwise have afforded them and were subsequently better able to keep their homes (Ding et al., 2010). This stability may lead to increased benefit to their general social capital compared with renters, suggesting potential benefits from policies that make favorable homeownership loans available to all consumers.
APPENDIX A Checking Sample Imbalance between Owners and Renters after Propensity Score Weighting p Value of Odds Ratio Variable of Homeownership Male .482 Age .186 Race/ethnicity (white) Black .446 Hispanic .981 Others .537 Education (less than high school graduate) High school graduate .227 Some college .287 BA and more .555 Employed .284 Marital status (married) Partnered .470 Separated/divorced/widowed .575 Single .626 Number of adults .276 Number of children .063 Household income (<$10,000) $10,000-$19,999 .687 $20,000-$29,999 .638 $30,000-$39,999 .814 $40,000-$49,999 .870 [greater than or equal to]$50,000 .843 Neighborhood size (a block or street) Several blocks or streets .793 Within a 15-minute walk .618 Larger than 15-minute walk .773 Moved to a new neighborhood .617 Neighborhood disadvantage .429 Stability .308 Note: The balance check used a series of single logistic regressions to predict homeownership. Reference groups are in parentheses. * p<.05, **p<.01. ***<.001. APPENDIX B Resource Generation, Tenure, and Neighborhood Characteristics: Correlation Matrix and Bivariate Statistics A. Correlation Matrix Neighborhood General Social Capital RG RG Neighborhood RG 1.000 General RG 0.401 *** 1.000 Tenure 0.112 *** 0.107 *** Neighborhood economic disadvantage 0.015 -0.001 Neighborhood stability 0.114 *** 0.089 *** B. A Series of Bivariate Negative Binomial Regression with Propensity Score Weighting Neighborhood RG General RG Tenure 1.099 (0.101) 1.012 (0.023) Neighborhood economic disadvantage 1.009 (0.049) 1.013 (0.012) Neighborhood stability 1.117 (0.058) * 1.015 (0.015) A. Correlation Matrix Neighborhood Economic Neighborhood Social Capital Tenure Disadvantage Stability Neighborhood RG General RG Tenure 1.000 Neighborhood economic disadvantage -0.147 *** 1.0000 Neighborhood stability 0.300 *** -0.410 *** 1.000 B. A Series of Bivariate Negative Binomial Regression with Propensity Score Weighting Tenure Neighborhood economic disadvantage Neighborhood stability Note: Coefficients represent incidence rate ratios. Standard errors are in parentheses. RG = resource generation. *p < .05. ** p <.01. *** p < .001.
As shown here, we further investigated the relationship between tenure, neighborhood variables, and RG outcomes with correlation matrix and bivariate regression models. Findings from the correlation matrix are similar, as we expected. This investigation reveals that tenure is positively correlated with both neighborhood RG and general RG. Tenure is also related to both neighborhood stability and neighborhood economic disadvantage. Neighborhood stability is positively related to both neighborhood and general RG, but it is negatively correlated with neighborhood economic disadvantage. An interesting finding is that neighborhood economic disadvantage is not related to either neighborhood RG or general RG.
A bivariate regression model shows that neighborhood economic disadvantage is not significantly related to either neighborhood RG or general RG, which is the same result from multivariate models presented in Table 3 and Table 4. Neighborhood stability is positively related to neighborhood RG. However, neighborhood stability is not significantly related to general RG, unlike results from multivariate analyses reported in Table 4. This finding suggests that neighborhood stability is not related to neighborhood RG. However, after controlling for individual characteristics and neighborhood economic conditions, the relationship became significantly positive (see Table 3). Thus, we conclude that the relationship between neighborhood stability and general RG depends largely on individual conditions and neighborhood economic surroundings.
Original manuscript received April 26, 2010 Final revision received April 27, 2011 Accepted June 24, 2011 Advance Access Publication February 3, 2013
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Michal Grinstein-Weiss, PhD, MSW, MA, is associate professor, Brown School of Social Work, and associate director, Center for Social Development, Washington University in St. Louis. Yeong Hun Yeo, MSW, is in the Department of Social Welfare, Chonbuk National University, Republic of Korea. Kim R. Manturuk, PhD, MA, is research assistant, Center for Community Capital, University of North Carolina at Chapel Hill. Mathieu R. Despard, MSW, is clinical assistant professor, and Krista A. Holub, MCRP, is research assistant, School of Social Work, University of North Carolina at Chapel Hill. Johanna K. P. Greeson, PhD, MSS, MLSP, is assistant professor, School of Social Policy and Practice, University of" Pennsylvania. Roberto G. Quercia, PhD, MA, is director, Center for Community Capital, and professor, Department of City and Regional Planning, University of North Carolina at Chapel Hill. The authors thank the Ford Foundation for their support of this research. They also thank Mark Lindblad, Sarah Riley, Janneke Ratcliffe, William Rohe, Adriane Casalotti, Andrea Taylor, and Jenna Tucker for their insightful comments, suggestions, and expertise. Address correspondence to Michal Grinstein-Weiss, Brown, School of Social Work, Washington University in St. Louis, One Brookings Drive, Campus Box 1196, St. Louis, MO 63130; e-mail: email@example.com.
Table 1: Characteristics by LMI Homeowners and Renters in the 2007 CAP Survey (Analytic Sample) Owners (n =1,235) Characteristic n / M %1 SD Individual characteristics Male 592 47.94 Age 36.65 10.32 Race/ethnicity White 743 60.16 Black 272 22.02 Hispanic 179 14.49 Others 41 3.32 Education Less than high school graduate 119 9.72 High school graduate 351 28.42 Some college 445 35.95 BA and more 320 25.91 Employed 1,097 88.83 Marital status Partnered 82 6.64 Married 598 48.42 Separated/divorced/widowed 298 24.13 Single 257 20.81 Household characteristics Number of children 1.16 1.24 Number of adults 1.73 0.72 Annual household income <$10,000 38 3.08 $10,000-$19,999 110 8.91 $20,000-$29,999 260 21.05 $30,000-$39,999 292 23.64 $40,000-$49,999 305 24.70 [greater than or equal to]$50,000 230 18.62 Perceived neighborhood size A block or street 271 21.94 Several blocks or streets 253 20.49 Within a 15-minute walk 418 33.85 Larger than 15-minute walk 293 23.72 Moved to a new neighborhood 100 8.10 Neighborhood characteristics Concentrated economic disadvantage scale -0.10 0.83 % Single parent 10.61 5.57 % Unemployed 5.94 4.52 % Public assistance 3.74 3.81 % Poverty 13.02 9.27 Neighborhood stability scale 0.17 0.80 % Living in the same house at least 5 years 53.47 10.87 % Owners 69.46 17.22 Renters (n = 683) Characteristic n / M %1 SD Individual characteristics Male 176 25.77 Age 41.91 12.65 Race/ethnicity White 324 47.44 Black 254 37.19 Hispanic 84 12.30 Others 21 3.07 Education Less than high school graduate 119 17.42 High school graduate 219 32.06 Some college 238 34.85 BA and more 107 15.67 Employed 381 55.78 Marital status Partnered 53 7.76 Married 142 20.79 Separated/divorced/widowed 270 39.53 Single 218 31.92 Household characteristics Number of children 0.85 1.14 Number of adults 1.62 0.94 Annual household income <$10,000 180 26.35 $10,000-$19,999 190 27.82 $20,000-$29,999 161 23.57 $30,000-$39,999 79 11.57 $40,000-$49,999 50 7.32 [greater than or equal to]$50,000 23 3.37 Perceived neighborhood size A block or street 171 25.04 Several blocks or streets 117 17.13 Within a 15-minute walk 225 32.94 Larger than 15-minute walk 170 24.89 Moved to a new neighborhood 171 25.04 Neighborhood characteristics Concentrated economic disadvantage scale 0.17 0.96 % Single parent 12.70 6.82 % Unemployed 6.56 4.67 % Public assistance 4.89 4.61 % Poverty 16.41 10.93 Neighborhood stability scale -0.40 0.92 % Living in the same house at least 5 years 48.16 11.70 % Owners 56.04 20.80 Characteristic t/[chi square] Individual characteristics Male 90.01 *** Age 9.82 *** Race/ethnicity 51.30 *** White Black Hispanic Others Education 43.81 *** Less than high school graduate High school graduate Some college BA and more Employed 271.62 *** Marital status 144.95 *** Partnered Married Separated/divorced/widowed Single Household characteristics Number of children -5.42 *** Number of adults -2.78 ** Annual household income 493.98 *** <$10,000 $10,000-$19,999 $20,000-$29,999 $30,000-$39,999 $40,000-$49,999 [greater than or equal to]$50,000 Perceived neighborhood size 4.75 A block or street Several blocks or streets Within a 15-minute walk Larger than 15-minute walk Moved to a new neighborhood 104.00 *** Neighborhood characteristics Concentrated economic disadvantage scale 6.50 *** % Single parent 7.15 *** % Unemployed 2.84 ** % Public assistance 5.84 *** % Poverty 7.18 *** Neighborhood stability scale -14.41 *** % Living in the same house at least 5 years -9.96 *** % Owners -14.02 *** Note: CAP=Community Advantage Program. * p<.05. ** p<.01. *** p<.001. Table 2: Difference in Resource Generation between Homeowners and Renters. Without Propensity Score Weighting Owners Renters Social Capital M M General resource generation (general RG) 4.79 4.52 *** Within-neighborhood resource generation 2.61 2.08 *** (neighborhood RG) General RG Do you know anyone who would help you move to a new home? 95.55 86.53 *** who would bring you food or medicine if 97.33 93.27 *** you were ill? who has good contacts with a local 54.09 43.19 *** newspaper or TV station? who is active in a political party? 39.19 28.40 *** who gives good advice on how to handle 83.97 75.99 *** stress? who is good with computers? 94.74 88.29 *** who provides good advice about new job 69.23 60.76 *** opportunities? who would lend you $500 if you needed it? 88.18 66.33 *** Neighborhood RG Do you know anyone in your neighborhood who would help you move to a new home? 46.57 33.58 *** who would bring you food or medicine if 58.57 49.12 *** you were ill? who has good contacts with a local 18.49 13.63 *** newspaper or TV station? who is active in a political party? 12.05 7.31 ** who gives good advice on how to handle 33.74 28.08 * stress? who is good with computers? 41.03 37.78 who provides good advice about new job 23.99 21.86 opportunities? who would lend you $500 if you needed it? 28.48 18.22 *** With Propensity Score Weighting Owners Renters Social Capital M M General resource generation (general RG) 4.79 4.73 Within-neighborhood resource generation 2.61 2.37 (neighborhood RG) General RG Do you know anyone who would help you move to a new home? 95.55 90.99 * who would bring you food or medicine if 97.33 95.95 you were ill? who has good contacts with a local 54.09 46.42 newspaper or TV station? who is active in a political party? 39.19 35.01 who gives good advice on how to handle 83.97 81.05 stress? who is good with computers? 94.74 92.97 who provides good advice about new job 69.23 67.77 opportunities? who would lend you $500 if you needed it? 88.18 82.90 * Neighborhood RG Do you know anyone in your neighborhood who would help you move to a new home? 46.57 45.14 who would bring you food or medicine if 58.57 52.35 you were ill? who has good contacts with a local 18.49 11.84 newspaper or TV station? who is active in a political party? 12.05 7.07 * who gives good advice on how to handle 33.74 29.21 stress? who is good with computers? 41.03 40.60 who provides good advice about new job 23.99 25.33 opportunities? who would lend you $500 if you needed it? 28.48 26.65 Note: For the columns under "With Propensity Score Weighting," sampling weights were applied by pweight option in the svyset command in Stata. * p<05. ** p<01. *** p<001. Table 3: Homeownership, Neighborhood Characteristics, and Resource Generation within Neighborhood Without PSW: Full Model Variable IRR SE Intercept 1.514 * 0.269 Tenure 1.148 * 0.069 Male 0.986 0.048 Age 1.005 * 0.002 Race/ethnicity (White) Black 0.999 0.054 Hispanic 0.888 0.067 Others 0.966 0.119 Education (Less than high school graduate) High school graduate 1.121 0.086 Some college 1.129 0.088 BA and more 1.227 * 0.105 Employed 1.072 0.071 Marital status Partnered 0.894 0.084 (Married) Separated/divorced/widowed 0.896 0.061 Single 0.941 0.066 Number of adults 0.944 0.032 Number of children 1.010 0.020 Household income (410,000) $10,000-$19,999 1.108 0.101 $20,000-$29,999 0.975 0.092 $30,000-$39,999 1.100 0.108 $40,000-$49,999 0.975 0.100 450,000 1.073 0.117 Perceived neighborhood size (A block or street) Several blocks or streets 0.991 0.073 Within a 15-minute walk 1.111 0.068 Larger than 15-minute walk 1.530 *** 0.091 Moved into a new neighborhood 0.842 * 0.061 Neighborhood disadvantage 1.089 ** 0.032 Neighborhood stability 1.126 *** 0.032 Wald [chi square] 160.71 *** BIC 7,986.29 n 1,918 With PSW: Full Model Variable IRR SE Intercept 1.404 0.414 Tenure 1.145 0.099 Male 0.879 0.083 Age 1.009 0.004 Race/ethnicity (White) Black 0.968 0.088 Hispanic 0.838 0.117 Others 0.611 0.197 Education (Less than high school graduate) High school graduate 1.196 0.126 Some college 1.051 0.120 BA and more 1.166 0.157 Employed 1.050 0.080 Marital status Partnered 0.899 0.119 (Married) Separated/divorced/widowed 0.805 0.105 Single 0.947 0.103 Number of adults 0.982 0.043 Number of children 1.012 0.033 Household income (410,000) $10,000-$19,999 1.131 0.130 $20,000-$29,999 1.033 0.115 $30,000-$39,999 1.085 0.132 $40,000-$49,999 1.118 0.155 450,000 1.192 0.182 Perceived neighborhood size (A block or street) Several blocks or streets 0.927 0.127 Within a 15-minute walk 1.019 0.106 Larger than 15-minute walk 1.481 *** 0.150 Moved into a new neighborhood 1.025 0.115 Neighborhood disadvantage 1.068 0.052 Neighborhood stability 1.116 * 0.057 Wald [chi square] 80.49 *** BIC 10,243.52 n 1,918 With PSW: Reduced Model Variable IRR SE Intercept 1.463 0.435 Tenure 1.152 0.099 Male 0.881 0.083 Age 1.008 0.004 Race/ethnicity (White) Black 0.985 0.084 Hispanic 0.816 0.110 Others 0.605 0.191 Education (Less than high school graduate) High school graduate 1.198 0.127 Some college 1.053 0.122 BA and more 1.136 0.153 Employed 1.052 0.080 Marital status Partnered 0.888 0.122 (Married) Separated/divorced/widowed 0.790 0.102 Single 0.935 0.102 Number of adults 0.991 0.043 Number of children 1.014 0.033 Household income (410,000) $10,000-$19,999 1.129 0.131 $20,000-$29,999 1.025 0.115 $30,000-$39,999 1.068 0.131 $40,000-$49,999 1.094 0.152 450,000 1.173 0.178 Perceived neighborhood size (A block or street) Several blocks or streets 0.902 0.118 Within a 15-minute walk 1.016 0.104 Larger than 15-minute walk 1.478 *** 0.149 Moved into a new neighborhood 1.018 0.115 Neighborhood disadvantage - - Neighborhood stability - - Wald [chi square] 66.75 *** BIC 10,242.96 n 1,918 Note: PSW = propensity score weighting; IRR = incidence rate ratio; BIC = Bayesian information criterion. * p<05. ** p<01. *** p<001. Table 4: Homeownership, Neighborhood Characteristics, and Resource Generation General Without PSW: Full Model Variable IRR SE Intercept 4.817 *** 0.275 Tenure 1.078 *** 0.018 Male 0.952 *** 0.013 Age 0.999 0.001 Race/ethnicity (White) Black 1.049 ** 0.016 Hispanic 0.900 *** 0.023 Others 1.005 0.034 Education (Less than high school graduate) High school graduate 1.079 ** 0.031 Some college 1.113 *** 0.032 BA and more 1.178 *** 0.035 Employed 1.097 *** 0.024 Marital status Partnered 0.928 ** 0.026 (Married) Separated/divorced/widowed 1.003 0.019 Single 1.001 0.020 Number of adults 0.986 0.011 Number of children 0.996 0.006 Household income (<$10,000) $10,000-$19,999 1.060 0.037 $20,000-$29,999 1.074 * 0.035 $30,000-$39,999 1.097 ** 0.037 $40,000-$49,999 1.092 ** 0.037 [greater than or equal to]$50,000 1.077 * 0.039 Perceived neighborhood size (A block or street) Several blocks or streets 1.020 0.019 Within a 15-minute walk 1.018 0.017 Larger than 15-minute walk 1.049 ** 0.020 Moved into a new neighborhood 0.982 0.021 Neighborhood disadvantage 1.015 0.009 Neighborhood stability 1.017 * 0.008 Wald [chi square] 322.03 *** BIC 8,105.30 n 1,918 With PSW: Full Model Variable IRR SE Intercept 5.053 *** 0.392 Tenure 1.052 * 0.022 Male 0.929 *** 0.020 Age 1.000 0.001 Race/ethnicity (White) Black 1.017 0.028 Hispanic 0.910 * 0.041 Others 0.939 0.053 Education (Less than high school graduate) High school graduate 1.099 ** 0.039 Some college 1.143 *** 0.044 BA and more 1.166 *** 0.045 Employed 1.088 ** 0.028 Marital status Partnered 0.912 ** 0.029 (Married) Separated/divorced/widowed 0.988 0.031 Single 1.011 0.026 Number of adults 1.004 0.012 Number of children 0.994 0.010 Household income (<$10,000) $10,000-$19,999 0.984 0.040 $20,000-$29,999 1.001 0.038 $30,000-$39,999 1.024 0.040 $40,000-$49,999 1.050 0.044 [greater than or equal to]$50,000 1.036 0.045 Perceived neighborhood size (A block or street) Several blocks or streets 1.017 0.023 Within a 15-minute walk 1.000 0.024 Larger than 15-minute walk 1.020 0.028 Moved into a new neighborhood 0.964 0.028 Neighborhood disadvantage 1.006 0.015 Neighborhood stability 1.026 * 0.012 Wald [chi square] 172.35 *** BIC 10,140.46 n 1,918 With PSW: Reduced Model Variable IRR SE Intercept 5.098 *** 0.399 Tenure 1.054 * 0.023 Male 0.932 ** 0.021 Age 0.999 0.001 Race/ethnicity (White) Black 1.016 0.025 Hispanic 0.902 * 0.040 Others 0.940 0.050 Education (Less than high school graduate) High school graduate 1.099 ** 0.039 Some college 1.145 *** 0.045 BA and more 1.161 *** 0.045 Employed 1.088 ** 0.028 Marital status Partnered 0.909 ** 0.029 (Married) Separated/divorced/widowed 0.986 0.032 Single 1.007 0.026 Number of adults 1.005 0.012 Number of children 0.994 0.010 Household income (<$10,000) $10,000-$19,999 0.986 0.040 $20,000-$29,999 1.003 0.038 $30,000-$39,999 1.024 0.041 $40,000-$49,999 1.049 0.044 [greater than or equal to]$50,000 1.038 0.046 Perceived neighborhood size (A block or street) Several blocks or streets 1.010 0.024 Within a 15-minute walk 1.000 0.024 Larger than 15-minute walk 1.020 0.029 Moved into a new neighborhood 0.964 0.028 Neighborhood disadvantage - - Neighborhood stability - - Wald [chi square] 170.63 *** BIC 10,130.02 n 1,918 Note: PSW= propensity score weighting; IRR=incidence rate ratio; BIC= Bayesian information criterion. * p<0.05. ** p<.01. *** p<.001.
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|Author:||Grinstein-Weiss, Michal; Yeo, Yeong Hun; Manturuk, Kim R.; Despard, Mathieu R.; Holub, Krista A.; Gr|
|Publication:||Social Work Research|
|Date:||Mar 1, 2013|
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