Behavior problems among young children have serious detrimental effects on short and long-term educational outcomes. An especially promising prevention strategy may be one that focuses on strengthening the relationships among families in schools, or social capital. However, empirical research on social capital has been constrained by conceptual and causal ambiguity. This study attempts to construct a more focused conceptualization of social capital and aims to determine the causal effects of social capital on children’s behavior. Using data from a cluster randomized trial of 52 elementary schools, we apply several multilevel models to assess the causal relationship, including intent to treat and treatment on the treated analyses. Taken together, these analyses provide stronger evidence than previous studies that social capital improves children’s behavioral outcomes and that these improvements are not simply a result of selection into social relations but result from the social relations themselves.

Background: In the context of international large scale assessments, it is often not feasible to implement a complete survey of all relevant populations. For example, the OECD Program for International Student Assessment surveys both students and schools, but does not obtain information from teachers. In contrast the OECD Teaching and Learning International Survey assesses teachers and schools but does not assess students. Clearly, important information is missing from both assessments. One approach to obtaining information from both surveys is through data fusion-a variety of methods that can be used to create a synthetic data set containing information from both surveys. Methods: This paper presents an experimental evaluation of a representative group of data fusion methods using data from Iceland-the only OECD country that implemented both PISA and TALIS to all members of the relevant populations. Results: On the basis of a set of validity criterion we find that Bayesian bootstrap predictive mean matching and the EM-bootstrap methods perform best with respect to creating a usable synthetic data file for research purposes.

The child poverty rate in the United States is higher than in most similarly developed countries, making child poverty one of America’s most pressing social problems. This article provides an introduction of child poverty in the US, beginning with a short description of how poverty is measured and how child poverty is patterned across social groups and geographic space. I then examine the consequences of child poverty with a focus educational outcomes and child health, and three pathways through which poverty exerts its influence: resources, culture, and stress. After a brief review of the anti-poverty policy and programmatic landscape, I argue that moving forward we must enrich the communities in which poor families live in addition to boosting incomes and directly supporting children’s skill development. I conclude with emerging research questions.