Neighborhood social capital and crime victimization: Comparison of spatial regression analysis and hierarchical regression analysis

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g. Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a Spatial Durbin Model with an inverse-distance weighting matrix that assigned each respondent a unique level of “exposure” to social capital based on all other residents’ perceptions. The study is based on a postal questionnaire sent to 20–69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the Spatial Durbin Model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.

► This study examines the effect of community social capital on crime victimization in Japan using the Spatial Durbin Model. ► In the Spatial Durbin Model, we use an inverse-distance spatial-weighting matrix to define influences of neighbors. ► We compare the results of Spatial Durbin Model to those of Hierarchical Linear Model using administrative boundary units. ► In Spatial Durbin Model, we find significant associations between neighborhood social capital and crime victimization. ► In Hierarchical Linear Model, we find generally null associations between neighborhood social capital and crime.