Effect of Homeownership on Resource Generation and Ties to Neighbors: A Multilevel Analysis

Purpose: The evolution of social capital as an increasingly important social science research topic has prompted concern about how best to define and measure social capital. In response, Lin (1999) proposed a “network theory” that views social capital as an individual pool of resources embedded in personal networks. This study aims to examine the effect of low- and moderate-income (LMI) homeownership on social capital. Following Lin's theory, we define social capital as an individual's access to social resources, and thus focus on two outcomes: ties to neighbors and resource generation.

Method: The study outcomes are included in a newly developed instrument for social capital measurement, the Resource Generator (Van der Gaag & Snijders, 2005). The data for this study come from the 2007 Self-Help Community Advantage Program panel survey using a quasi-experimental design of LMI homeowners (n=2,013) and a comparison panel of LMI renters (n= 878), nested within 2,099 census tract areas.

Given the nested data, we use hierarchical linear modeling (HLM) to assess the differences between homeowners and renters on three social capital outcomes: overall resource generation (alpha=.81); resource generation within neighborhood (alpha=.80); and ties to neighbors (alpha=.71)

Individual-level covariates (level 1) include demographic characteristics as well as household composition, household income, neighborhood boundaries, and new neighborhood status. Census tract covariates (level 2) include neighborhood disadvantage scale (alpha=.91), and population density. All predictors were grand mean centered for analysis.

Results: After controlling for differences in household and neighborhood characteristics, we show that homeownership is associated with higher scores for both overall resource generation (p<.01) and resource generation within neighborhoods (p<.01). Second, we demonstrate that neighborhood characteristics (level 2 variables) are significantly related to the two study outcomes of resource generation and ties to neighbors. For example, neighborhood distress is associated with higher resource generation within neighborhoods (p<.01) and ties to neighbors (p<.01). Third, we identify four factors that are significant predictors for the study outcomes: neighborhood boundaries, a move to a new neighborhood, education level, and Hispanic ethnicity.

Conclusions: This study makes three unique contributions over earlier studies. First, the sample comes from LMI households; a population segment underinvestigated by social capital researchers, although it is the population served by social workers. Second, our focus on the effect of LMI homeownership on social capital adds important findings to the scarce research on the impact of LMI homeownership on social outcomes. Third, we apply HLM to control for differences of neighborhood level characteristics in addition to individual characteristics; this step corrects for biases generated when researchers use uncorrected models (i.e., ordinary-least squares) that cannot account for nested data. Finally, we discuss the importance of using experimental and quasi-experimental designs and sophisticated statistical methods, such as HLM models, to advance the rigor of social work research.

References

Lin, N. (1999). Building a network theory of social capital. Connections, 22(1), 28-51.

Van der Gaag, M., & Snijders, T. (2005). The resource generator. Social Networks, 27(1), 1-29.