A more recent web map on this same topic is available for ArcGIS Online subscribers here.This map shows the socioeconomic status of each census tract. Data come from the US Census Bureau's 2011-2015 American Community Survey. Neighborhood Socioeconomic Status, over and above individual socioeconomic status, is a predictor of many health outcomes. The Neighborhood Socioeconomic Status (NSES) Index is on a scale from 0 to 100 with 50 being the national average around 2010. The Index incorporates the following indicators (fields in this layer's attribute table):Median Household Income (from Table B19013)Percent of individuals with income below the Federal Poverty Line (from Table S1701)The educational attainment of adults (age 25+) (from Table B15003)Unemployment Rate (from Table S2301)Percent of households with children under the age of 18 that are "female-headed" (no male present) (from Table B11005)NSES = log(median household income) + (-1.129 * (log(percent of female-headed households))) + (-1.104 * (log(unemployment rate))) + (-1.974 * (log(percent below poverty))) + .451*((high school grads)+(2*(bachelor's degree holders)))To learn more about how the NSES Index was developed, please explore this journal articleMiles, Jeremy and Weden, Margaret; Lavery, Diana; Escarce, José; Kathleen Cagney; Shih, Regina. 2016. “Constructing a Time-Invariant Measure of the Socio-Economic Status of U.S. Census Tracts.” Journal of Urban Health, vol. 93, issue no.1, pp. 213-232. or this PPT presentation presented at the University of Texas at San Antonio's Applied Demography Conference in 2014.

Here are the raw data and R code used in the paper "A comparison of two neighborhood-level socioeconomic indices in the United States" by Boscoe and Li currently under review. The raw data and data dictionary are exactly as they were obtained from the National Historical Geographic Information System (NHGIS). The data comprise the 7 American Community Survey variables used to construct the Yost Index at the block group level for the period 2011-2015.

We extend our previous work with the Yost Index by adding 90% confidence intervals to the index values. These were calculated using the variance replicate estimates published in association with the American Community Survey of the United States Census Bureau.

In the file yost-tract-2015-2019.csv, the data fields consists of 11-digit geographic ID built from FIPS codes (2 digit state, 3 digit county, 6 digit census tract); Yost index, 90% lower confidence interval; 90% upper confidence interval. Data is provided for 72,793 census tracts for which sufficient data were available. The Yost Index ranges from 1 (lowest socioeconomic position) to 100 (highest socioeconomic position).

For those only interested in using the index as we have calculated it, the file yost-tract-2015-2019 is the only file you need. The other 368 files here are provided for anyone who wishes to replicate our results using the R program yost-conf-intervals.R. The program presumes the user is running Windows machine and that all files reside in a folder called C:/yostindex. The R program requires a number of packages, all of which are specified in lines 10-22 of the program.

Details of this project were published in Boscoe FP, Liu B, LaFantasie J, Niu L, Lee FF. Estimating uncertainty in a socioeconomic index derived from the American Community Survey. SSM-Population Health 2022; 18: 101078. Full text

Additional years of data following this format are planned to be added to this repository in time.

This spread sheet shows ABS geographic standards from 2006 across Australia and the % of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) IRSD group. The data used to create this information was the same as used in the research paper “Socio-Economic Indexes for Areas: Getting a handle on individual diversity within areas” by Phillip Wise and Rosalynn Mathews. It is advised that this paper is read to further develop an understanding of the concepts and caveats associated with the analytical output contained in the spreadsheet. Group 1 – Approx. most disadvantage 20% of the 15-64 year old population Group 2 – Approx. second most disadvantaged 20% of the 15- 64 population Group 3 – Approx. second least disadvantaged 30% of the 15-64 year old population Group 4 – Approx. least disadvantaged 30% of the 15-64 year old population

The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: (1) percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; (2) percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); (3) percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); (4) median maximum advertised download speeds; and (5) median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; (4) percent of noninstitutionalized civilian population with a disability: and (5) a brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.Data for the digital divide index (DDI) was compiled by Purdue Center for Regional Development and obtained from the 5-year American Community Survey (ACS) and Ookla Speedtest® open dataset.

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at: https://data.cityofchicago.org/api/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

Socio-Economic Indexes for Areas (SEIFA) is a product developed by the ABS that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census. SEIFA 2011 is the latest version of this product and consists of four indexes. The Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) summarises information about the economic and social conditions of people and households within an area, including both relative advantage and disadvantage measures. Data last updated: 28th March 2013. Users of this data are advised to carefully read the accompanying information on the SEIFA web page and in the Technical Paper. SEIFA Homepage SEIFA Technical Paper For further information about these and related statistics, contact the National Information and Referral Services on 1300 135 070. Periodicity: 5-Yearly.

Socio-Economic Indexes for Areas (SEIFA) is a product developed by the ABS that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on …Show full descriptionSocio-Economic Indexes for Areas (SEIFA) is a product developed by the ABS that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census. SEIFA 2011 is the latest version of this product and consists of four indexes: The Index of Relative Socio-economic Disadvantage (IRSD); The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); The Index of Education and Occupation (IEO); The Index of Economic Resources (IER). Each index is a summary of a different subset of Census variables and focuses on a different aspect of socio-economic advantage and disadvantage.

Socio-Economic Index of 7 variables overlayed to compare with the physical blight index- Education, Median Household Income, Renter Occupied, Single Parent Households, Population Density, Poverty Rate, and Unemployment Rate. This map was used to help question what socio-economic factors correlate with the observance of blighted areas in order to better create strategic decisions on how to best prevent blight.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

Background: Socioeconomic status (SES) is an important determinant of health and potential modifier of the effects of environmental contaminants. There has been a lack of comprehensive indices for measuring overall SES in Canada. Here, a more comprehensive SES index is developed aiming to support future studies exploring health outcomes related to environmental pollution in Canada. Methods: SES variables (n=22, Census Canada 2006) were selected based on: cultural identities, housing characteristics, variables identified in Canadian environmental injustice studies and a previous deprivation index (Pampalon index). Principal component analysis with a single varimax rotation (factor loadings=¦60¦) was performed on SES variables for 52974 census dissemination areas (DA). The final index was created by averaging the factor scores per DA according to the three components retained. The index was validated by examining its association with preterm birth (gestational age<37 weeks), term low birth weight (LBW, <2500 g), small for gestational age (SGA, <10 percentile of birth weight for gestational age) and PM2.5 (particulate matter=2.5 µm) exposures in Edmonton, Alberta (1999–2008). Results: Index values exhibited a relatively normal distribution (median=0.11, mean=0.0, SD=0.58) across Canada. Values in Alberta tended to be higher than in Newfoundland and Labrador, Northwest Territories and Nunavut (Pearson chi-square p<0.001 across provinces). Lower quintiles of our index and the Pampalon’s index confirmed know associations with a higher prevalence of LBW, SGA, preterm birth and PM2.5 exposure. Results with our index exhibited greater statistical significance and a more consistent gradient of PM2.5 levels and prevalence of pregnancy outcomes. Conclusions: Our index reflects more dimensions of SES than an earlier index and it performed superiorly in capturing gradients in prevalence of pregnancy outcomes. It can be used for future research involving environmental pollution and health in Canada. These metadata can also be found on SAGE's searchable metadata website: http://sagemetadata.policywise.com/nada/index.php/catalog/14

Here are Yost indexes for census tracts and block groups in the United States for various years from 1990-2019. The Yost index is a composite index of socioeconomic status that consists of a percentile score from 1 (highest SES) to 100 (lowest SES). Data for 1990 and 2000 include the 50 US states plus the District of Columbia. For years after 2000, the data additionally include Puerto Rico. To rescale the index to geographic units smaller than the US, the score column may be used, where scores range from about -1.8 for the highest SES to 1.8 for the lowest SES.More about the Yost index can be found here: Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. ​Cancer Causes and Control​ 2001; 12(8): 703–711.

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 and Control​. 2014; 25(1): 81-92.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

This data is SA2 based SEIFA data on The Index of Relative Socio-economic Advantage and Disadvantage, 2016. Data is based upon 2016 ASGS boundaries. Socio-Economic Indexes for Areas (SEIFA) is an ABS product that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census of Population and Housing. SEIFA 2016 has been created from Census 2016 data and consists of four indexes: The Index of Relative Socio-economic Disadvantage (IRSD); The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); The Index of Education and Occupation (IEO); The Index of Economic Resources (IER). Each index is a summary of a different subset of Census variables and focuses on a different aspect of socio-economic advantage and disadvantage. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.

The Socio-Economic Indexes for Areas (SEIFA) rank areas according to their relative socio-economic advantage and disadvantage using 2021 Census data. This layer presents data by Statistical Area Level 1 (SA1), 2021. SEIFA 2021 consists of four indexes: The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) The Index of Relative Socio-economic Disadvantage (IRSD) The Index of Education and Occupation (IEO) The Index of Economic Resources (IER) Each index summarises different subsets of 2021 Census variables and focuses on a different aspect of socio-economic advantage and disadvantage.For detailed information on how to use the SEIFA data, please refer to the SEIFA 2021 Technical Paper.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, Data downloads Source: Australian Bureau of Statistics (ABS)

The RVI/CVI database is derived from the CanEcumene 3.0 GDB (Eddy, et. al. 2023) using a selection of socio-economic variables identified in Eddy and Dort (2011) that aim to capture the overall state of socio-economic conditions of communities as ‘human habitats’. This dataset was developed primarily for application in mapping socio-economic conditions of communities and regions for environmental and natural resource management, climate change adaptation, Impact Assessments (IAs) and Regional Assessments (RAs), and Cumulative Effects Assessment (CEA). The RVI/CVI is comprised of five sub-indicators: 1) population change, 2) age structure, 3) education levels, 4) employment levels, and 5) real estate values. Index values are based on percentile ranks of each sub-indicator, and averaged for each community, and for three ranked groups: 1) all of Canada, 2) by province, and 3) by population size. The data covers the Census periods of 2001, 2006, 2011 (NHS), 2016, and 2021. The index is mapped in two ways: 1) as ‘points’ for individual communities (CVI), and 2) as ‘rasters’ for spatial interpolation of point data (RVI). These formats provide an alternative spatial framework to conventional StatsCan CSD framework. (For more information on this approach see Eddy, et. al. 2020). ============================================================================================ Eddy, B.G., Muggridge, M., LeBlanc, R., Osmond, J., Kean, C., and Boyd, E. 2023. The CanEcumene 3.0 GIS Database. Federal Geospatial Platform (FGP), Natural Resources Canada. https://gcgeo.gc.ca/viz/index-en.html?keys=draft-3f599fcb-8d77-4dbb-8b1e-d3f27f932a4b Eddy B.G., Muggridge M, LeBlanc R, Osmond J, Kean C, Boyd E. 2020. An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene. One Ecosystem 5: e55881. https://doi.org/10.3897/oneeco.5.e55881 Eddy, B.G.; Dort, A. 2011. Integrating Socio-Economic Data for Integrated Land Management (ILM): Examples from the Humber River Basin, western Newfoundland. Geomatica, Vol. 65, No. 3, p. 283-291. doi:10.5623/cig2011-044.

This spread sheet shows all of the State Suburbs for the ACT and the % of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) IRSD group. The data used to create this information was the same as used in the research paper “Socio-Economic Indexes for Areas: Getting a handle on individual diversity within areas” by Phillip Wise and Rosalynn Mathews. It is advised that this paper is read to further develop an understanding of the concepts and caveats associated with the analytical output contained in the spreadsheet.

Group 1 – Approx. most disadvantage 20% of the 15-64 year old population Group 2 – Approx. second most disadvantaged 20% of the 15- 64 population Group 3 – Approx. second least disadvantaged 30% of the 15-64 year old population Group 4 – Approx. least disadvantaged 30% of the 15-64 year old population

Please also note that not all ACT CDs have been included as their populations are below the ABS population count threshold (<3), those removed are: - 8013801 in Reid - 8012805 in Turner - 8014302 in Kingston - 8021701 in Unclassified ACT - 8010110 in Bonner - 8023401 in Tharwa - 8010103 in Unclassified ACT - 8014305 in Kingston - 8014803 in Forrest

It should be noted that multiple Compact Discs of data make up this dataset, as a result the data for each suburb is distributed across several discs. This cccounts for the multiple rows of data that appear for each suburb.

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The Index of Educational Disadvantage for SA Government schools, each year from 2017 (not 2019).\r \r The Index of Educational Disadvantage is a socio-economic index, used by the Department for Education to allocate resources to schools to address educational disadvantage related to socio-economic status. \r \r The most disadvantaged schools have an index of 1, the least disadvantaged have an index of 7. \r \r More information on the Index of Educational Disadvantage is available at \r https://www.education.sa.gov.au/sites/g/files/net691/f/educational_disadvantage_index_explanation.pdf

The Census 2021 Relative Socio-Economic Index for SA2 data.Socio-Economic Indexes for Areas (SEIFA) in Australia are comprehensive measures that provide insights into the well-being of communities across the country. Developed by the Australian Bureau of Statistics (ABS), SEIFA indices compile census data to evaluate various socio-economic factors, including income, education, employment, and housing conditions. These indices rank areas in Australia according to their relative socio-economic advantage and disadvantage, offering a detailed snapshot that helps identify the varying levels of social and economic well-being in different regions. This crucial data assists government bodies, policymakers, and community organisations in understanding disparities across different areas, enabling them to tailor services, allocate funding, and develop initiatives that address specific community needs, ultimately aiming to enhance the quality of life and reduce inequalities across different Australian locales.

For further details on how ABS curates these indices please visit: Socio-Economic Indexes for Areas (SEIFA): Technical Paper, 2021 | Australian Bureau of Statistics (abs.gov.au)

This data is SA1 based SEIFA data on The Index of Economic Resources, 2016. Data is based upon 2016 ASGS boundaries. Socio-Economic Indexes for Areas (SEIFA) is an ABS product that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census of Population and Housing. SEIFA 2016 has been created from Census 2016 data and consists of four indexes: The Index of Relative Socio-economic Disadvantage (IRSD); The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); The Index of Education and Occupation (IEO); The Index of Economic Resources (IER). Each index is a summary of a different subset of Census variables and focuses on a different aspect of socio-economic advantage and disadvantage. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.