US cities face a growing demographic challenge. Families are having fewer children, and child populations in core cities have fallen precipitously over the past five years. If these trends continue, such shifts (paired with projections of future immigration decline) threaten core cities’ long-term population stability, economic growth, and fiscal health. This report introduces new, localized metrics to measure where families form and raise children, examining both fertility and child migration dynamics. It also identifies how the local conditions associated with stronger family formation and retention differ from each other, across cities versus suburbs, and between socioeconomic strata, offering a clearer framework for policymakers seeking to build inclusive family-friendly communities.

Why This Matters

Family formation and retention shape future workforce supply, school system viability, and regional equity. Cities that lose families risk population aging, shrinking tax bases, and increasing inequality. Tracking family and child retention alongside fertility provides an early indicator of long-term economic health. This research is especially relevant for local and state policymakers, planners, and education leaders working to align housing, infrastructure, and public services with the needs of families across metropolitan regions.

What We Found

Families are not absent from core cities, but they do have a higher chance of moving to suburbs upon having children than childless couples. Where and how families choose to live reflects a complex interaction of housing, amenities, and economic constraints.

• Suburbs still outperform core cities, on average, but variation is large. Suburban and exurban areas have higher fertility rates (general fertility rates of 58 versus 52) and child populations (child retention scores of 1.18 versus 1.01), on average, yet some core cities outperform suburbs in other regions, challenging the notion that families consistently avoid or leave urban cores.
• Fertility and child retention respond to different local factors. Family-sized housing and child care density are strongly associated with retaining families with older children, while fertility is negatively associated with features linked to young adult–oriented environments (e.g., bar density and an influx of young adults). Increasing total child populations requires addressing both family formation and child rearing simultaneously.
• Core cities and suburbs require different policy strategies. In core cities, education quality and commute patterns are more strongly linked to family outcomes, while in suburbs, child care availability plays a larger role. The share of college-educated residents is negatively associated with both fertility and retention in core cities but not in suburbs, underscoring differences in cost, lifestyle, and labor market dynamics.
• Socioeconomic inequality limits family choice. High-socioeconomic-status (high-SES) families’ decisions about where to have and raise children are strongly shaped by housing, schools, and amenities, while low-SES families show weaker responses to these factors. These patterns suggest that low-SES families face more constraints on their ability to access family-friendly environments.

Together, these findings suggest that boosting child populations in a way that expands upward social mobility across entire labor markets requires coordinated investments in housing, education, and caregiving infrastructure that are tailored to both geography (core versus suburban) and families’ life stages and economic circumstances.

How We Did It

We analyze American Community Survey microdata (2019–2023) at the Public Use Microdata Area level across 43 major metropolitan areas. The study introduces three key metrics: a general fertility rate, a child retention score based on child age distributions adjusting for cohort births and deaths, and a combined child population index. We pair these with neighborhood-level data on housing, amenities, and opportunity (e.g., the Child Opportunity Index and the National Neighborhood Data Archive) and use regression analysis to identify controlled correlations between local conditions and family outcomes across core and suburban geographies.

Portions of this overview were developed with the assistance of generative AI. Please see the Urban Institute’s generative AI policy.