This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

Abstract

The Opportunity Atlas has collected contextual data by county and tract. Rather than providing contextual socioeconomic data of where people currently live, the data represents average socioeconomic indicators (e.g., earnings) of where people grew up.

Documentation

A core element of Population Health Science is that health outcomes can only be fully understood when they are studied within their context. Therefore, we have a copy of The Opportunity Atlas, a dataset that provides socioeconomic data by county and tract.

Several studies have shown that especially childhood neighborhoods drive adult outcomes and that residential areas lived in through adulthood have much smaller effects. The focus of the Opportunity Atlas is therefore on contextual data of where people grew up:

%3E Traditional measures of poverty and neighborhood conditions provide snapshots of income and other variables for residents in an area at a given point in time. But to study how economic opportunity varies across neighborhoods, we really need to follow people over many years and see how one’s outcomes depend upon family circumstances and where on grew up. The Opportunity Atlas is the first dataset that provides such longitudinal information at a detailed neighborhood level. Using the Atlas, you can see not just where the rich and poor currently live – which was possible in previously available data from the Census Bureau – but whether children in a given area tend to grow up to become rich of poor. This focus on mobility out of poverty across generations allows us to trace the roots of outcomes such as poverty and incarceration back to where kids grew up, potentially permitting much more effective interventions.

As such, The Opportunity Atlas data provides a rich source of data for researchers who wish to overlay health data with contextual data.

Methodology

Three sources of Census Bureau are linked to compute the data

1. The 2000 and 2010 Decennial Census short form
2. Federal income tax returns for 1989, 1994, 1995, 1998-2015
3. The 2000 Decennial Census long form and the 2005-2015 American Community Surveys (ACS).

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20.5 million Americans born between 1987-1983 are sampled from these data and mapped back to the Census tracts they lived in through age 23. After that step, a range of outcomes are then estimated for each of the 70,000 tracts. In order to comply with federal data disclosure standards and protect the privacy of individuals no estimates in tracts with 20 or fewer children are published and noise (small random numbers) is added to all the estimates.

For more information on the data collection and methodology, please visit:

Website

Documentation

Data availability

Some variables are available for counties only. The table below gives you an overview. Open the table in a new tab for a larger view.

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Abstract

The Opportunity Atlas uses anonymous data following 20 million Americans from childhood to their mid-thirties to answer this question of which neighborhoods in America offer children the best chance at a better life than their parents.

The Opportunity Atlas (OA) is an innovative data set that ranks neighborhoods according to children’s adult outcomes in several domains, including income. Conceptually, outcomes offer new evidence about neighborhood effects when measured in isolation from neighborhood sorting. This paper shows that neighborhood sorting contributes to OA estimates. We document cases in which small sample sizes and changes over time can explain disagreements between OA rankings and those based on contemporaneous variables. Our results suggest caution for interpretations of the OA data set at a granular level, particularly for predictions about the outcomes of black children in high-income neighborhoods. This working paper has been revised by WP 20-36R .

All code and input files used in k-means clustering analysis of Opportunity Atlas data. This dataset is associated with the following publication: Zelasky, S., C. Martin, C. Weaver, L. Baxter, and K. Rappazzo. Identifying groups of children's social mobility opportunity for public health applications using k-means clustering. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 9(9): E20250, (2023).

The table All Outcomes by Census Tract, Race, Gender2 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 2505 variables.

Shapefiles of the Aquaculture Opportunity Area (AOA) study developed during 2021 for the Southern California Bight. Included in this dataset are: (1) Study areas in the Southern California Bight developed based on depth and jurisdictional boundaries. Four study areas were identified (North, Central North, Central South, South). (2) Suitability modeling results for the North, Central North, Central South, and South Southern California Bight study areas are presented as categories (“Unsuitable,†“Low,†“Moderate,†“High†) (3) High-High clusters (HH) from the Aquaculture Opportunity Atlas for Southern California. Clusters were identified within each of the four study areas (North, Central North, Central South, and South). (4) Refined High-High clusters (HH) from the Aquaculture Opportunity Atlas for Southern California. Clusters were identified within each of the four study areas (North, Central North, Central South, and South). (5) Options from the Aquaculture Opportunity Atlas for Southern California. Options were identified within two of the study areas, North and Central North.

The table HII for Children from Low-Income Households by Census Tract is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 65 variables.

This map is visualizing the changes in average household income (in 2023 dollars) for individuals at the county level, based on their parents' income level (see table below). Changes are defined by the mean household income earned by individuals born in 1978 and individuals born in 1992 (measured at age 27). Income is an important measure of economic mobility, which is the ability to improve economic status over time. The data is sourced from the Opportunity Atlas, a comprehensive dataset developed through a collaboration between researchers at the U.S. Census Bureau and Opportunity Insights at Harvard University. It includes data from the 2000 and 2010 decennial Census, Federal Income Tax returns, and the 2005-2015 American Community Surveys (ACS).Parent income percentileAverage household income (2023 dollars)Lowest (1st percentile)$1,150Low (25th percentile)$33,320Middle (50th percentile)$69,520High (75th percentile)$122,040Highest (100th percentile)$1,840,000 The table outlines the approximate dollar values for each parent percentile group that are referenced in the datasets. See more information on the Opportunity Insights FAQ page.

Shapefiles of the Aquaculture Opportunity Area (AOA) study developed during 2021 for the Gulf of Mexico. Included in this dataset are: (1) Study areas in the Gulf of Mexico developed based on depth, jurisdictional boundaries, and Level III biogeographical breaks. (2) Compiled observations of Harmful Algal Blooms (Karina brevis) from 2000 to 2018 in the Gulf of Mexico and eastern Florida. (3) Suitability modeling results for the West, Central, East, and Southeast Gulf of Mexico study areas are presented as categories (“Unsuitable,†“Low,†“Moderate,†“High†) based on ocean use and conservation concerns, including: national security, natural and cultural resources, industry, navigation, transportation, aquaculture, and fishing. (4) High-High clusters (HH) identified as the most suitable areas from LISA (Local Index of Spatial Association) analysis. (5) Refined HH clusters that could accommodate at least one 500-acre AOA option. (6) Highest ranking options for each of the refined HH clusters representing a 500- to 2000-acre area between 50 to 150 meters depth that has relatively high suitability for generalized marine aquaculture based on a within cluster model evaluating logistics, vessel traffic, commercial fishing, and oceanography data. (7) Location and areal extent of options identified for each study region meeting a dispersion rule (greater than 30 nautical miles distance between locations).

The table All Outcomes by Census Tract, Race, Gender4 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 330 variables.

OLS regression coefficients and standard errors from regressions of census tract level adult household income ranks (Opportunity atlas) and diabetes diagnoses (500 cities) on two census tract HOLC classifications.

Geospatial data representing the Aquaculture Opportunity Area (AOA) study developed during 2024 for the State of Alaska. Included in this dataset are: (1) Study areas in State of Alaska waters developed based on maximum sea ice coverage, distance from coastal populated places, existing and active aquaculture sites, and the Alaska state water boundary. Ten distinct study areas were identified (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell). (2) Suitability modeling results for the ten study areas. Results between each study area should not be compared relative to outputs of another study area, as each study area was run as a discrete model and thus the outputs of the analysis cannot be compared between study areas. (3) Precision siting outputs from the Aquaculture Opportunity Atlas for the State of Alaska. Clusters were identified within each of the ten study areas (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell) for two scenarios: intertidal and subtidal mariculture. (4) Final AOA Options for the state of Alaska based on selection of highest ranking polygons from the precision siting outputs for each of the ten study areas for both scenarios.

This map is visualizing the changes in average household income (in 2023 dollars) for individuals at the commuting zone level, based on their parents' income level (see table below). Changes are defined by the mean household income earned by individuals born in 1978 and individuals born in 1992 (measured at age 27). Income is an important measure of economic mobility, which is the ability to improve economic status over time. The data is sourced from the Opportunity Atlas, a comprehensive dataset developed through a collaboration between researchers at the U.S. Census Bureau and Opportunity Insights at Harvard University. It includes data from the 2000 and 2010 decennial Census, Federal Income Tax returns, and the 2005-2015 American Community Surveys (ACS).Parent income percentileAverage household income (2023 dollars)Lowest (1st percentile)$1,150Low (25th percentile)$33,320Middle (50th percentile)$69,520High (75th percentile)$122,040Highest (100th percentile)$1,840,000The table outlines the approximate dollar values for each parent percentile group that are referenced in the datasets. See more information on the Opportunity Insights FAQ page.

The table HII for Children from Low-Income Households by County is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 3219 rows across 64 variables.

The table All Outcomes at the National Level by Race, Gender is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 100 rows across 3914 variables.

The table All Outcomes by County, Race, Gender and Parental Income2 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 3219 rows across 2505 variables.

Absolute Mobility, Air Pollution, and Demographic Characteristics of 70,185 US Census Tracts. Absolute Mobility from the Opportunity Atlas dataset. Demographic variables from the Census and ACS. Air pollution data from Colmer et al. 2023. Meterological variables from Daymet.

View the diversity of challenges and opportunities across America's counties within different types of rural regions and communities. Get statistics on people, jobs, and agriculture.

This paper studies how design features influence the success of Housing Mobility Programs (HMPs) in reducing racial segregation. Targeting neighborhoods based on previous residents' outcomes does not allow for targeting race-specific outcomes, generates uncertainty when targeting income-specific outcomes, and generates bias in ranking neighborhoods' effects. Moreover, targeting opportunity bargains based on previous residents' outcomes selects tracts with large disagreements in current and previous residents' outcomes, with such disagreements predicted by sorting since 1990. HMP success is aided by the ability to port vouchers across jurisdictions, access to cars, and relaxing supply constraints, perhaps by targeting lower-ranked neighborhoods. This paper incorporates content from two previous working papers, one under the same title ( WP 20-36 ) and the other circulated as “Neighborhood Sorting Obscures Neighborhood Effects in the Opportunity Atlas ( WP 20-37 ).