Available Files

The data are stored in a text file and provided here as Windows self-extracting ZIP (executable) and gzip file.

2008-2017 data in Decennial Census 2010 census tract boundaries

EXE (EXE, 30.0 MB)
GZIP (GZ, 40.9 MB)
Data Dictionary

2018-2022 data in Decennial Census 2020 census tract boundaries

EXE (EXE, 19.3 MB)
GZIP (GZ, 25.0 MB)
Data Dictionary

The time-dependent census tract attributes include a socioeconomic status (SES) index, the seven tract-level SES attributes that were used to construct the index and additional attributes. The attributes and the index are estimated at various time points using a series of rolling American Community Survey (ACS) 5-year estimates from 2006 to 2024. Data are available in two census tract geography definitions. Data for 2008-2017 are in Decennial Census 2010 Census Tract boundaries, matching those used in 2006-2010 through 2015-2019 ACS 5-year estimates. Data for 2018-2022 are in Decennial Census 2020 census tract boundaries used in the 2016-2020 ACS. At each time point, an SES index is constructed from these attributes using a factor analysis (Yu et al. 2014).

Variable Descriptions

The selection of attributes for constructing the SES index is based on Yost et al. (2001). Below are the ACS data tables and formulas used to calculate the base variables used in constructing the SES score. The tables are from the 2020-2024 ACS 5-year estimates. Similar tables were used from the 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022 and 2019-2023 ACS 5-year estimates.

Median Household Income

Median household income is taken from table B19013: Median Household Income in the Past 12 Months (in 2024 inflation adjusted dollars). The inflation adjustment is different for each of the data sources (i.e., 2014-18 ACS median income is in 2018 inflation adjusted dollars).

Median House Value

Median house value is taken from table B25077: Median Value (Dollars).

Median Gross Rent

Median gross rent is taken from table B25064: Median Gross Rent (Dollars)

Percent Below 150% Poverty

The percent below 150% of poverty line is calculated using table C17002: Ratio of Income to Poverty Level in the Past 12 Months. The formula for this is:

  • Below 150% poverty: ((C17002e02 + C17002e03 + C17002e04 + C17002e05) / C1702e01) * 100

Education Index

The education index is calculated using table B15002: Sex by Educational Attainment for the Population 25 Years and Over. The percent with less than high school graduate, high school only and more than high school are calculated, as follows:

  • Less than HS grad: ((B15002e03 + ... + B15002e10 + B15002e20 + ... + B15002e27) / B15002e01) * 100
  • HS Only: ((B15002e11+B15002e28)/B15002e01) * 100
  • More than HS grad: ((B15002e12 + ... + B15002e18 + B15002e29 + ... + B15002e35) / B15002e01) * 100
Using the percentages calculated above, the education index is calculated as follows:
  • Education Index: (Less than HS grad * 9) + (HS only *12) + (More than HS grad * 16)

Working Class

The percent working class is calculated using table C24010: Sex by Occupation for the Civilian Employed Population 16 Years and Over. The formula for this is:

  • Working Class: ((C24010e20 + C24010e24 + C24010e25 + C24010e26 + C24010e27 + C24010e30 + C24010e34 + C24010e56 + C24010e60 + C24010e61 + C24010e62 + C24010e63 + C24010e66 + C24010e70)) / C24010e01) * 100

Percent Unemployed

The percent of persons ages 16 and over who are unemployed is calculated using table B23025: Employment Status for the Population 16 Years and Over. The percent unemployed is calculated for civilians in the labor force. Persons in the armed forces or not in the labor force are not included in the calculation. The formula used is:

  • Unemployed: (B23025e05 / B23025e03) * 100

Variables Included but Not Used in Creating the Index

Percent Persons Below Poverty

For each timepoint the percent of persons below poverty is calculated for all races, White alone, Black or African American alone, American Indian and Alaska Native alone, Asian alone + Native Hawaiian and Other Pacific Islander alone*, Some other race alone + two or more races, White alone (not Hispanic or Latino) and Hispanic or Latino. Tables B17001, B17001A-B17001I: Poverty Status in the Past 12 Months by Sex and Age. The formulas used are:

  • All races: ((B17001e02/B17001e01) * 100
  • White alone: ((B17001Ae02/B17001Ae01) * 100
  • Black or African American alone: ((B17001Be02/B17001Be01) * 100
  • American Indian and Alaska Native alone: ((B17001Ce02/B17001Ce01) * 100
  • Asian alone + Native Hawaiian and Other Pacific Islander alone: (((B17001De02+ B17001Ee02)/( B17001De01+ B17001Ee01)) * 100
  • Some other race alone + two or more races: (((B17001Fe02+ B17001Ge02)/( B17001Fe01+ B17001Ge01)) * 100
  • White alone (not Hispanic or Latino): ((B17001He02/B17001He01) * 100
  • Hispanic or Latino: ((B17001Ie02/B17001Ie01) * 100

NAACCR Poverty Indicator

This variable is created from the percent of persons below poverty for all races above. The variable has four categories: less than 5 percent, 5 percent to less than 10 percent, 10 percent to less than 20 percent and 20 percent or more.

Rural-Urban Commuting Area (RUCA) Codes

Two Rural-Urban Commuting Area variables are constructed from the 2010 and 2020 data available from the U.S. Department of Agriculture Economic Research Service. The data were used to create a variable with 4 categories (urban, large rural, small rural and isolated rural) and a variable with 2 categories (urban and rural). The same 2010 values are used for all years in the 2008-2017 attribute file, and the 2020 values are used in the 2018-2022 attribute file.

Census-based Urban Rural Indicator Codes (URIC)

The Urban Rural Indicator Code (URIC) is an urban-rural classification variable with four categories: 100% urban (All urban), ≥50% but <100% urban (Mostly urban), >0% but <50% urban (Mostly rural), and 100% rural (All rural) tracts. Rural population encompasses all population not included within an urban area. This measure reflects the rural nature of the immediate environment and may be most relevant for studies that focus on behaviors and risk, e.g., cancer prevention and screening studies.

In addition to the main variable, two variables with collapsed categories are available. A variable with three categories: urban, mixed, and rural and a variable with two categories: urban and rural tracts.

The variables are constructed from the 2010 and 2020 U.S. Census’s percent of the population living in non-urban areas. The same 2010 values are used for all years in the 2008-2017 file and the same 2020 values are used for all years in the 2018-2022 file.

Cancer Reporting Zone

Cancer reporting zone is a field identifying custom geographic areas with similar populations, and large enough to support stable cancer rates and minimize suppression. This field is defined for cancer registries that have defined zones for their catchment areas. A crosswalk of census tract to cancer reporting zone is available for these areas and is used to assign cancer reporting zone based on geocoded census tract. Cancer reporting zone identifies the unique cancer reporting zone across registries consisting of the 2-digit state FIPS code followed by an 8 character cancer reporting zone unique to a registry’s catchment area.

A single Cancer Reporting Zone variable is available in the 2008-2017 tract attribute file. In the 2018-2022 tract attribute file there are two variables. One in the 2010 definition and one in the 2020.

Census Tract Required for Cancer Reporting Zone

This variable can be used in conjunction with census tract certainty when selecting cases for cancer zone analyses. This includes cases that have high tract-level certainty (1, 2, 6) or that have low tract-level certainty but do not need a tract to assign a Cancer Reporting Zone.

Similar to the Cancer Reporting Zone, The 2008-2017 tract file has one variable and the 2018-2022 file has two variables.

CDC Standardized Sub-County Geographies

The Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Program created standardized sub-county geographies that are comparable over time, place, and outcomes. These standardized sub-county geographies use census tracts as the foundation, have a hierarchical structure, and generally nest within county boundaries. They were created using the Geographic Aggregation Tool to merge based on the nearest population-weighted centroid until a specified population threshold was reached. There are three subcounty variables, a 5,000 population, 20,000 population, and a 50,000 population version. The values are used for all years.

Currently, CDC Standardized Sub-County Geographies is only available in the 2008-2017 tract attribute file. It will be added to the 2018-2022 tract attribute file in the future.

Persistent Poverty Census Tracts

Persistent poverty census tracts are defined by the U.S. Department of Agriculture as areas where 20 percent or more of the residents were poor as measured by each of four timepoints. Persistent Poverty for 2010 uses the 1980, 1990, and 2000 censuses, and 2007-2011 American Community Survey 5-year average. For 2020, the 1990 and 2000 censuses, and the 2007-2011 and 2017-2021 American Community Survey 5-year averages are used. The same 2010 variable is used for all years in the 2008-2017 tract attribute file. Likewise, the 2020 value is used for all years in the 2018-2022 tract file. These are the official ERS measures available from USDA.

CDC/ATSDR Social Vulnerability Index

The Agency for Toxic Substances and Disease Registry (ATSDR) uses data from U.S. census variables to develop the Social Vulnerability Index (SVI). The 2018 SVI data are included in the 2008-2017 tract attribute file and the 2022 SVI data are included in the 2018-2022. The overall index is created from four related themes. The 2018 SVI themes are Socioeconomic Status, Household Composition & Disability, Minority Status & Language and Housing Type & Transportation. The 2018 state and U.S. based values for the four themes and the overall index are used for all years in the 2008-2017 tract attribute file. The 2022 SVI themes are Socioeconomic Status, Houshehold Characteristics, Racial & Ethnic Minority Status and Housing Type & Transportation. The 2022 state and U.S. based values for the four themes and the overall index are used for all years in the 2018-2022 tract attribute file.

Note that the 2018 and 2022 SVI themes are different and are constructed from different individual variables.

Congressional District

Congressional districts are identified by a 2-character numeric state FIPS code and a 2-character district code. This generates congressional district IDs that are numbered uniquely across the U.S. The district code used by the Census Bureau for at large states is 00. The District of Columbia and the Commonwealth of Puerto Rico have the district code of 98, which identifies their status with respect to representation in Congress:

  • 01 to N*—Congressional district codes
  • 00—At large (single district for state)
  • 98—Nonvoting delegate

In Connecticut, Illinois, Michigan (2010) and New Hampshire (2020) the state participant did not assign the congressional districts to cover all of the state or equivalent area. The code “ZZ” has been assigned to areas with no congressional district defined (usually large water bodies). These unassigned areas are treated within state as a single congressional district for purposes of data presentation. The same 118th Congressional District values are used for all years in the 2008-2017 file and the 119th congressional District values are used for all years in the 2018-2022 file.

References

Yost K, Perkins C, Cohen R, Morris C, Wright W (2001) Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control 12(8):703–711. doi:10.1023/a:1011240019516.

Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. Cancer Causes & Control. 2014;25(1):81-92.

Werner AK, Strosnider H, Kassinger C, Shin M; Sub-County Data Project Workgroup. Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project. J Public Health Manag Pract. 2018;24(5):E20-E27. doi:10.1097/PHH.0000000000000686

Werner AK, Strosnider HM. Developing a surveillance system of sub-county data: Finding suitable population thresholds for geographic aggregations. Spat Spatiotemporal Epidemiol. 2020 Jun;33:100339. doi: 10.1016/j.sste.2020.100339.