Which Neighborhoods in the United States Offer the Best—and Worst—Opportunities for Children to Achieve Upward Social Mobility?

The study asks which neighborhoods in the United States give children the best chances of rising out of poverty. Using Census and IRS data on more than 20 million children born between 1978 and 1983, the researchers link childhood Census tracts to adult outcomes such as income, incarceration, and teen birth rates. They find large neighborhood gaps: for children from families earning $27,000, adult household income differs by about $12,850 across nearby tracts. Quasi-experimental evidence shows that about 60% of this variation reflects true causal effects of place. Moving a low-income child from a 25th- to a 75th-percentile tract raises expected lifetime earnings by roughly $387,000.

Why This Article Was Selected for The Policy Scientist

This article addresses a central policy issue: how place shapes long-term economic mobility at a scale relevant to national, state, and local decision-making. That question matters broadly because governments routinely target neighborhoods without reliable evidence about which areas actually limit or expand opportunity. Chetty, Hendren, and coauthors are among the leading scholars in applied microeconomics. The article is timely given ongoing debates about housing, affordability, and spatial inequality. It offers a significant contribution by combining exceptionally large, high-quality Census-IRS linked data with credible quasi-experimental designs that approximate causal effects. Although not an RCT, the mover-based identification strategy is rigorous and widely accepted. The dataset’s national scope supports generalizability to other U.S. jurisdictions, though international extrapolation would require caution.

Full Citation and Link to Article

Chetty, R., Friedman, J. N., Hendren, N., Jones, M. R., & Porter, S. R. (Forthcoming 2025). The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility. American Economic Review.  https://www.aeaweb.org/articles?id=10.1257/aer.20200108 

Central Research Question

The article asks which specific neighborhoods in the United States offer the highest and lowest levels of upward mobility for children, and to what degree those place-based differences reflect causal effects rather than compositional sorting. This question addresses a long-standing gap in the mobility literature: prior work has documented that place matters, but it has not produced comprehensive, tract-level estimates of children’s long-term outcomes across the entire country. The authors investigate whether granular geographic variation in adult earnings, incarceration, and related outcomes can be reliably measured, decomposed, and used to inform policy design and household decision-making.

Previous Literature

Earlier research on intergenerational mobility has shown that where children grow up has lasting effects on their outcomes, but the evidence has tended to rely on either experimental studies in narrow geographic settings or large-scale observational studies at relatively coarse geographic levels. The Moving to Opportunity (MTO) experiment provided direct causal evidence that relocating to higher-opportunity areas improves children’s long-term outcomes, but its sample sizes and geographic scope limit generalizability. Other influential work by Chetty and Hendren (2018a, 2018b) estimated county-level and commuting-zone-level opportunity gradients, showing that mobility varies dramatically across regions and that much of this variation is attributable to causal place effects. Parallel work has explored mechanisms—such as school quality, exposure to violence, peer composition, and environmental toxins—and developed frameworks for conceptualizing “place effects” as an important component of human capital formation. However, previous national analyses lacked the granularity necessary for neighborhood-level targeting. By constructing a tract-level, nationally representative dataset, this paper bridges the strengths of small-scale experiments and large-scale observational studies, extending the literature by providing both comprehensive coverage and policy-relevant detail.

Data

The authors use an exceptionally large dataset combining de-identified administrative records from the U.S. Census Bureau, decennial Census data from 2000 and 2010, federal income tax returns, and the American Community Survey (ACS). Their primary sample includes 20.5 million children born between 1978 and 1983 who were either born in the United States or immigrated as authorized minors. These children are observed into adulthood using linked IRS and ACS records, allowing measurement of individual earnings, incarceration, and other outcomes. Children are assigned to neighborhoods on the basis of residence histories during childhood, weighted by the proportion of time spent in each Census tract. The resulting national dataset spans more than 70,000 tracts, producing detailed measures by race, gender, and parental income. Because the dataset covers nearly the entire U.S. population, sampling error is minimized, and the geographic scale of the estimates enables local-level comparisons with high precision. The richness of the linked administrative data also allows construction of outcomes that are rarely available in public-use samples, such as adult earnings conditional on parental income for subpopulations within each tract. The scope and quality of the dataset represent a substantial methodological advance, enabling the authors to estimate the long-term outcomes associated with specific neighborhoods rather than relying on proxies such as poverty rates, crime statistics, or school-level indicators.

Methods

The authors first estimate observational tract-level outcome measures by fitting univariate regressions of children’s adult outcomes on parental income within detailed demographic cells. This approach allows them to map nonparametric relationships between parental income ranks and adult outcomes for each tract, conditional on race and gender. These baseline statistics serve two purposes: they provide descriptive indicators of opportunity and they facilitate policy targeting. To identify causal effects, the authors leverage established mover-based designs that exploit variation in the timing and destination of residential moves during childhood. These movers’ exposure designs compare siblings or cohorts who move at different ages or to different types of neighborhoods, allowing estimation of how exposure duration affects later-life outcomes. The identification strategy relies on the quasi-experimental assumption that, conditional on a rich set of controls, the timing of a child’s move is uncorrelated with potential outcomes. The authors also draw on results from previous experimental and quasi-experimental studies—such as MTO and national mover-based designs—to validate and scale their estimates. Throughout the analysis, the authors apply privacy-preserving noise infusion and disclosure avoidance techniques consistent with Census Bureau standards. While not a randomized controlled trial, the design meets the field’s current standards for credible causal inference at large scale and is widely used in applied microeconomics. The combination of descriptive mapping, quasi-experimental estimation, and triangulation with prior work yields a credible set of place-based causal estimates.

Findings/Size Effects

The study finds substantial variation in children’s adult outcomes across neighborhoods, even among children from families with identical income levels. For children whose parents earn $27,000, the within-county standard deviation of mean adult household income across tracts is about $10,420. Only half of this variation is explained by conventional neighborhood indicators such as poverty rates, racial composition, or job growth. Using mover-based causal estimates, the authors conclude that approximately 60 percent of observed between-tract variation in outcomes reflects true causal effects rather than selection. Exposure effects are sizable: moving a child from a tract at the 25th percentile of opportunity to one at the 75th percentile increases expected lifetime earnings by roughly $387,000. The paper documents comparable patterns for other outcomes: incarceration rates differ sharply across neighborhoods for boys, even conditional on parental income, and teenage birth rates for girls vary dramatically at similar margins. The authors also show that high-opportunity areas are not necessarily high-income areas; many affordable neighborhoods generate strong long-term outcomes for children. Conversely, some neighborhoods with strong labor markets for adults offer relatively weak mobility prospects for children. This disconnect implies that economic opportunity for children may depend on factors other than those that drive local labor demand. The findings highlight the potential for both targeted place-based interventions and residential mobility programs to shift long-term outcomes at reasonable cost.

Conclusion

The article provides the first comprehensive, tract-level estimates of the childhood roots of economic mobility across the United States. It shows that neighborhoods exert large causal effects on children’s long-term outcomes and that traditional metrics used for policy targeting only partially capture these differences. By constructing the Opportunity Atlas—a publicly accessible mapping tool and dataset—the authors create a durable policy infrastructure for identifying low-opportunity areas, guiding public investment, and supporting mobility programs. The findings reinforce and extend the emerging consensus in the literature that place-based childhood environments play a central causal role in shaping adult earnings, incarceration, and related outcomes. The study’s national scope, strong data foundation, and quasi-experimental methods together provide policymakers and researchers with a detailed empirical basis for designing interventions. Although the results pertain to the U.S. institutional context, the conceptual framework is applicable to other countries with similarly granular administrative data. The article thus provides both a methodological and substantive contribution, advancing the field’s understanding of how childhood environments shape economic opportunity and offering a scalable tool for future policy design and academic research.