National Estimates of School Readiness: A Look at the Poverty Gaps Among Children in the United States

Background

School readiness is considered essential for lifelong success. The physical, cognitive, social, and emotional aspects of development in early childhood predict later academic success and well-being. For example, these skills measured at age four have been linked to GPA and standardized test scores in 5th grade, as well as adult educational attainment, income, and occupational outcomes.

The National Survey of Children’s Health (NSCH) “Healthy and Ready to Learn” initiative represents the first nationwide effort to systematically assess the school readiness of U.S. children before kindergarten. Prior analyses using NSCH data have documented wide variability in school readiness across domains and demographic and socioeconomic factors—including income, parent education, household language, and family structure. Differences across states have also been observed.

However, the role of within-state income disparities in shaping school readiness outcomes remains understudied. It is unclear whether similar patterns exist across U.S. states or how family and child demographics contribute to these gaps. We address these research questions: (1) How do the poverty gaps in school readiness vary across states? (2) To what extent is state-level variation in the poverty gaps in school readiness explained by child and family demographic factors?

Methods

We analyzed data from 12,055 U.S. households with children ages 3 to 5 from the 2023 NSCH. Parents rated children’s school readiness in early learning skills, social-emotional learning, and self-regulation. Household poverty was measured using the U.S. Census Bureau’s thresholds, and a binary variable identified families below (<100%) and at/above (≥100%) the federal poverty level (FPL).

We first ran regression models separately for each state using the state Federal Information Processing System (FIPS) code. Standardized school readiness scores were regressed onto the household poverty variable to obtain the raw poverty gaps in school readiness. We then added child sex and race/ethnicity as covariates to obtain the adjusted poverty gaps in school readiness, controlling for child demographics. In the third set of models, we included parent education, household structure, and household language in to examine change in the poverty gaps associated with family demographics.

Results

Children in households <100% of the FPL scored lower in school readiness (early learning skills: -0.40 SD, self-regulation: -0.24 SD, social-emotional learning: -0.20 SD) compared to children in households ≥100% of the FPL. After adjusting for child demographics, the poverty gaps in early learning skills (-0.38 SD) and self-regulation skills (-0.23 SD) remained significant. After adjusting for child and family demographics, the poverty gaps were no longer significant. The poverty gaps also varied by state, with income playing a more predominant role in early learning skills across states than the school readiness domains. Adjusting for child demographics slightly reduced these gaps, and adding family demographics nearly diminished them.

Conclusions

State policies aimed at supporting the development of children in poverty may provide even greater benefits when they also take into consideration the broader family context. Two-generational approaches, which pair early childhood education with human capital interventions, may be most effective for boosting school readiness.