Lead and delinquency rates; A spatio-temporal perspective.

Juvenile delinquency has significant social costs for perpetrators, victims, and communities. To understand the distribution of delinquency offenses this study considers the spatial clustering of juvenile delinquency with lead, race, and neighborhood deprivation using a longitudinal ecological design (N = 4390) and a hierarchical model implemented in a Bayesian methodology that allows space-time interaction. The results show lead exposure is positively related to delinquency offense rates, and over time delinquency rates have become more concentrated in areas with higher levels of lead exposure and shares of Black or African American residents. The study emphasizes the isolation of neighborhoods with social problems and the importance of monitoring patterns of lead and crime at local levels as communities implement lead exposure mitigation programs.

Social capital and the nonprofit infrastructure; an ecological study of child maltreatment.

Child maltreatment is a significant social problem that responds to neighborhood conditions, including disorder and support. Using administrative sources with the census response rate and geocoded nonprofit tax forms in a cross-sectional ecological design (N = 443), this article explores two understudied supportive factors in neighborhoods: aggregate social capital and nonprofit organizations. A series of Poisson models show aggregate social capital and nonprofit density are negatively related to child maltreatment rates, while the relationship between social capital and child maltreatment rates varies by the number of nonprofits present in the neighborhood. The results provide new insights into the ecology of child maltreatment and illustrate the importance of norms and formal organizations when addressing collective action problems.

Exploring data use in nonprofit organizations.

Organizations around the world have increasingly employed data for a variety of purposes, and nonprofit organizations are no exception. This article reviews the use of data in nonprofit organizations, including the types of data collected and accessed, the motivations for data capture, and the barriers to systematic use of data. The literature shows that nonprofit organizations capture a variety of data, including public and financial data, performance measures, program evaluation data, and volunteer information. Organizations use these with diverse motivations such as program or organizational improvement, marketing, and accountability. Prominent barriers faced by organizations include challenges in identifying meaningful information, lack of technical ability, inability to prioritize data work, as well as external influences. The article highlights the challenges in synthesizing the available literature, with a high degree of fragmentation, including research from distinct intellectual traditions resulting in many disconnected constructs, measurements, and theories. Finally, the paper discusses challenges in the study of nonprofit data use and strives to provide guidance for future inquiry.