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.

This table includes children's outcomes and neighborhood characteristics in adulthood by neighborhood where they grew up by US counties.

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).

This table includes children's outcomes and neighborhood characteristics in adulthood by neighborhood where they grew up by US tracts.

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.

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).

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.

The table All Outcomes by County, Race, Gender and Parental Income3 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 3219 rows across 2505 variables.

The table All Outcomes by Census Tract, Race, Gender1 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 2501 variables.

This dataset contains replication files for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples" by Raj Chetty and John Friedman. For more information, see https://opportunityinsights.org/paper/differential-privacy/. A summary of the related publication follows. Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the Opportunity Atlas – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the Harvard Privacy Tools Project and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.

The table All Outcomes by Census Tract, Race, Gender3 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 2505 variables.

Comprehensive dataset of insider trading activities for Atlas Venture Associates Opportunity II, LLC, including Form 4 filings and transaction visualizations across multiple companies.

These datasets provide measures of Social Capital for counties, ZIP codes, high schools, and colleges in the United States based on data from Facebook. The Social Capital Atlas dataset is the result of a collaboration between Meta, Opportunity Insights, and researchers from Harvard, New York University, and Stanford. We use methods from the differential privacy literature to add noise to these aggregate statistics to protect privacy while maintaining a high level of statistical reliability. See the accompanying README.pdf and papers published in Nature for more details on data construction, methods, and results.

We construct three measures of social capital:

1. Connectedness - The extent to which people with different characteristics are friends with each other. This includes our main economic connectedness measure.

2. Cohesiveness - The degree to which friendship networks are clustered into cliques and whether friendships tend to be supported by mutual friends. This includes our clustering and support ratio measures.

3. Civic Engagement - Indices of trust or participation in civic organizations. This includes our volunteering rate measure.

A visualization of this data is available here: www.socialcapital.org

Research publications for the Social Capital Atlas dataset are available here:

• Social Capital I: Measurement and Associations with Economic Mobility

• Social Capital II: Determinants of Economic Connectedness

Any researcher using the data should reference/cite the following papers:
Chetty, Raj, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, Drew Johnston, Martin Koenen, Eduardo Laguna-Muggenberg, Florian Mudekereza, Tom Rutter, Nicolaj Thor, Wilbur Townsend, Ruby Zhang, Mike Bailey, Pablo Barber ́a, Monica Bhole, and Nils Wernerfelt (2022a). “Social Capital I: Measurement and Associations with Economic Mobility.” Nature 608 (7921): 108-121, 2022.

Download Citation here: https://citation-needed.springer.com/v2/references/10.1038/s41586-022-04996-4?format=refman&flavour=citation

Chetty, Raj, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, Drew Johnston, Martin Koenen, Eduardo Laguna-Muggenberg, Florian Mudekereza, Tom Rutter, Nicolaj Thor, Wilbur Townsend, Ruby Zhang, Mike Bailey, Pablo Barber ́a, Monica Bhole, and Nils Wernerfelt (2022b). “Social Capital II: Determinants of Economic Connectedness.” Nature 608 (7921): 122-134, 2022.

Download Citation here: https://citation-needed.springer.com/v2/references/10.1038/s41586-022-04997-3?format=refman&flavour=citation

The table All Outcomes by Commuting Zone, Race, Gender3 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 741 rows across 2505 variables.

Factors influencing adoption of oral health promotion by ANC providers in Moyo district, North-Western Uganda.

This layer contains Census Tracts that have been designated as Qualified Opportunity Zones and contains additional data determined by the EPA to be of interest to users who are seeking revitalization-oriented information about these tracts. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. The data in this layer was updated in January 2021. For more information on Opportunity Zones, please visit: https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

EPA has added these indicators to the QOZ tracts list:

1. Count of Superfund facilities from EPA National Priorities List (NPL). Count was generated by performing spatial join of Tract boundaries to NPL points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/superfund/superfund-data-and-reports

2. Count of Brownfields properties from EPA Assessment, Cleanup and Redevelopment Exchange System (ACRES). Count was generated by performing spatial join of Tract boundaries to ACRES points--yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_ACRES.csv

3. Technical Assistance Communities from EPA Office of Community Revitalization (OCR). 13 layers were merged into one; count was generated by performing spatial join of Tract boundaries to combined point layer—yielding per tract counts. Please note that technical assistance communities are often serving areas larger than a single Census tract. Please contact OCR with questions. Spatial Extent: all US states and territories. Source: https://epa.maps.arcgis.com/home/item.html?id=b8795575db194340a4ad1c251e4d6ca1

4. Lead Paint Index from Environmental Justice Screening and Mapping Tool (EJSCREEN). Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

5. Air Toxics Respiratory Index from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

6. Demographic Index Indicator from EJSCREEN. Block group-level values were population weighted and summed to produce a tract-level estimate. The “raw” values were converted to tract-level percentiles. Spatial Extent: all US states and Puerto Rico. Source: https://gaftp.epa.gov/EJSCREEN/2019/

7. Estimated Floodplain Indicator from EPA EnviroAtlas. Floodplain raster was converted to polygon feature class; Y/N indicator was generated by performing a spatial join of Tract boundaries to the Floodplain polygons. Spatial Extent: Continental US. Source: https://gaftp.epa.gov/epadatacommons/ORD/EnviroAtlas/Estimated_floodplain_CONUS.zip

8. National Walkability Index from EPA Smart Location Tools. The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. Tract values assigned by averaging values from block group-level table. Spatial Extent: all US states and territories. Source: EPA Office of Policy—2020 NWI update

9. Impaired Waters Indicator from EPA Office of Water (OW). Y/N indicator was generated by performing spatial joins of Tract boundaries to 3 separate impaired waters layers (point, line and polygon). Y was assigned for all intersected geographies. Extent: all US states and Puerto Rico. Source: https://watersgeo.epa.gov/GEOSPATIALDOWNLOADS/rad_303d_20150501_fgdb.zip

10. Tribal Areas Indicator from EPA. Y/N indicator was generated by performing spatial joins of Tract boundaries to 4 separate Tribal areas layers (Alaska Native Villages, Alaska Allotments, Alaska Reservations, Lower 48 Tribes). Y as assigned for all intersected geographies. Spatial Extent: Alaska and Continental US. Source: https://edg.epa.gov/data/PUBLIC/OEI/OIAA/TRIBES/EPAtribes.zip

11. Count of Resource Conservation and Recovery Act (RCRA) Corrective Action facilities. Count was generated by performing spatial join of Tract boundaries to Corrective Action points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://www.epa.gov/cleanups/cimc-web-map-service-and-more

12. Count of Toxics Release Inventory facilities from EPA. Count was generated by performing spatial join of Tract boundaries to TRI points—yielding per tract counts. Spatial Extent: all US states and territories. Source: https://edap-oei-data-commons.s3.amazonaws.com/EF/GIS/EF_TRI.csv

13. Social Vulnerability Index (SVI) Housing/Transportation Index from CDC, published in 2018. The Housing/Transportation Index includes ACS 2014-2018 data on crowding in housing and no access to vehicle, among others. County values assigned to tracts by joining Tracts to county-level table. For detailed documentation: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdfSpatial Extent: all US states. Source: https://epa.maps.arcgis.com/home/item.html?id=cbd68d9887574a10bc89ea4efe2b8087

14. Low Access to Food Store Indicator from USDA Food Access Atlas. Y/N indicator was generated by performing a table join of Tracts to the Food Access table records meeting the test criteria. Spatial Extent: all US states. Source: https://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data/

15. Overall Social Vulnerability Index (SVI) from CDC. Values (RPL_THEMES) assigned by joining the Tract boundaries to source Tract-level table. Spatial Extent: All US states. Source: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

16. Rural Communities Indicator from USDA Economic Research Service (ERS). Source tract-level table was flagged as rural where RUCA Codes in 4-10 or 2 and 3 where area >= 400 sq. miles and pop density

The table All Outcomes by Commuting Zone, Race, Gender2 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 741 rows across 2505 variables.

This publication highlights three promising opportunitites for DRC's development. First, DRC's new government can take advantage of opportunities for long-term conservation and sustainable use of forests. Second, the development assistance community can play an important role in support of measures to conserve forests. Third, the government and the development assistance community can collaborate to get forest policy high on DRC's development agenda.

No description is available. Visit https://dataone.org/datasets/%7B31F1C368-635A-4F35-B187-DCEADCCE76F3%7D for complete metadata about this dataset.