*Names in bold indicate Presenter
Several public social safety net programs in the U.S. have the explicit or implicit goal of reducing poverty’s negative effect on families and children: cash welfare (Temporary Assistance to Needy Families [TANF]), food assistance (the Supplemental Nutrition Assistance Program [SNAP], the Special Supplemental Nutrition Program for Women, Infants, and Children [WIC], and the Child and Adult Care Food Program [CACFP]), medical assistance (Medicaid and the Child Health Insurance Program [CHIP]), and the child care subsidy program (Child Care and Development Fund [CCDF]). Although a substantial body of research has identified links between participation in each of these safety net programs and child health and development, to our knowledge no studies have explored how families with young children combine these programs across their children’s early years, what the correlates of those combinations might be, and whether certain combinations, more than others, reduce school readiness gaps between poor and non-poor children.
To address this unanswered question, we use data from the Early Childhood Longitudinal Study – Birth Cohort (ECLS-B), the first nationally representative U.S. study to follow a cohort of children from birth to school entry. At each of the study’s 4 waves (when children were 9-months, 2 years, 4 years, and 5 or 6 years old), parents were interviewed about their participation in safety net programs and children were directly assessed. Rich data on standard family background characteristics such as maternal education, and on mothers’ overall health, depression, and stress, were also collected. The current study will identify correlates and consequences of different combinations of safety net packaging for children’s kindergarten school readiness outcomes, for all children and for children who are especially at-risk because their mothers have low levels of education, poor health, or high levels of depression or stress.
Preliminary results suggest that sample sizes are sufficient to identify profiles of safety net packaging across programs and data waves. We will use latent class analysis to generate clusters of children who fit different profiles of program use, multinomial logistic regression to predict cluster membership, and OLS regression with lagged dependent variables to predict kindergarten cognitive and social outcomes from safety net cluster. We will also consider multiple treatment group propensity score matching to address non-random selection into clusters. The authors have already published other papers with the ECLS-B and these statistical techniques, making this plan feasible prior to the meeting. Findings are expected to inform the targeting of public investments aimed to close school readiness gaps between poor and non-poor children.