47 datasets found
  1. High income tax filers in Canada, specific geographic area thresholds

    • www150.statcan.gc.ca
    • open.canada.ca
    Updated Oct 28, 2024
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    Government of Canada, Statistics Canada (2024). High income tax filers in Canada, specific geographic area thresholds [Dataset]. http://doi.org/10.25318/1110005601-eng
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    Dataset updated
    Oct 28, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

  2. Distributional Financial Accounts

    • catalog.data.gov
    • gimi9.com
    • +1more
    Updated Dec 18, 2024
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    Board of Governors of the Federal Reserve System (2024). Distributional Financial Accounts [Dataset]. https://catalog.data.gov/dataset/distributional-financial-accounts
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    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Federal Reserve Systemhttp://www.federalreserve.gov/
    Federal Reserve Board of Governors
    Description

    The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

  3. T

    United States Personal Income

    • tradingeconomics.com
    • tr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 30, 2025
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    TRADING ECONOMICS (2025). United States Personal Income [Dataset]. https://tradingeconomics.com/united-states/personal-income
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    csv, json, xml, excelAvailable download formats
    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 28, 1959 - May 31, 2025
    Area covered
    United States
    Description

    Personal Income in the United States decreased 0.40 percent in May of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Income - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  4. N

    Income Distribution by Quintile: Mean Household Income in Florence, SC //...

    • neilsberg.com
    csv, json
    Updated Mar 3, 2025
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    Neilsberg Research (2025). Income Distribution by Quintile: Mean Household Income in Florence, SC // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/florence-sc-median-household-income/
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    csv, jsonAvailable download formats
    Dataset updated
    Mar 3, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Carolina, Florence
    Variables measured
    Income Level, Mean Household Income
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It delineates income distributions across income quintiles (mentioned above) following an initial analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the mean household income for each of the five quintiles in Florence, SC, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

    Key observations

    • Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 10,346, while the mean income for the highest quintile (20% of households with the highest income) is 208,770. This indicates that the top earners earn 20 times compared to the lowest earners.
    • *Top 5%: * The mean household income for the wealthiest population (top 5%) is 368,659, which is 176.59% higher compared to the highest quintile, and 3563.30% higher compared to the lowest quintile.
    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Income Levels:

    • Lowest Quintile
    • Second Quintile
    • Third Quintile
    • Fourth Quintile
    • Highest Quintile
    • Top 5 Percent

    Variables / Data Columns

    • Income Level: This column showcases the income levels (As mentioned above).
    • Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Florence median household income. You can refer the same here

  5. U.S. median household income 2023, by education of householder

    • statista.com
    Updated Sep 17, 2024
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    Statista (2024). U.S. median household income 2023, by education of householder [Dataset]. https://www.statista.com/statistics/233301/median-household-income-in-the-united-states-by-education/
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    Dataset updated
    Sep 17, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

  6. Income of individuals by age group, sex and income source, Canada, provinces...

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated May 1, 2025
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    Government of Canada, Statistics Canada (2025). Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas [Dataset]. http://doi.org/10.25318/1110023901-eng
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    Dataset updated
    May 1, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

  7. Low and Moderate Income Areas

    • catalog.data.gov
    Updated Mar 1, 2024
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    U.S. Department of Housing and Urban Development (2024). Low and Moderate Income Areas [Dataset]. https://catalog.data.gov/dataset/hud-low-and-moderate-income-areas
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    Dataset updated
    Mar 1, 2024
    Dataset provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    Description

    This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

  8. ACS Median Household Income Variables - Boundaries

    • hub.arcgis.com
    • heat.gov
    • +6more
    Updated Oct 22, 2018
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    Esri (2018). ACS Median Household Income Variables - Boundaries [Dataset]. https://hub.arcgis.com/maps/45ede6d6ff7e4cbbbffa60d34227e462
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    Dataset updated
    Oct 22, 2018
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  9. INCOME Percent Households by Income in 1999 CTs 2000

    • catalog.data.gov
    • gstore.unm.edu
    • +1more
    Updated Dec 2, 2020
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    U.S. Department of Commerce, Bureau of the Census, Geography Division (Point of Contact) (2020). INCOME Percent Households by Income in 1999 CTs 2000 [Dataset]. https://catalog.data.gov/dataset/income-percent-households-by-income-in-1999-cts-2000
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    Dataset updated
    Dec 2, 2020
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

  10. T

    HOUSEHOLDS DEBT TO INCOME by Country Dataset

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Dec 29, 2015
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    TRADING ECONOMICS (2015). HOUSEHOLDS DEBT TO INCOME by Country Dataset [Dataset]. https://tradingeconomics.com/country-list/households-debt-to-income
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    csv, json, excel, xmlAvailable download formats
    Dataset updated
    Dec 29, 2015
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Area covered
    World
    Description

    This dataset provides values for HOUSEHOLDS DEBT TO INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

  11. T

    United States Wages and Salaries Growth

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Wages and Salaries Growth [Dataset]. https://tradingeconomics.com/united-states/wage-growth
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    csv, json, xml, excelAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1960 - May 31, 2025
    Area covered
    United States
    Description

    Wages in the United States increased 4.72 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  12. Japan JP: Income Share Held by Highest 10%

    • ceicdata.com
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    CEICdata.com, Japan JP: Income Share Held by Highest 10% [Dataset]. https://www.ceicdata.com/en/japan/poverty/jp-income-share-held-by-highest-10
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    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2008
    Area covered
    Japan
    Description

    Japan JP: Income Share Held by Highest 10% data was reported at 24.700 % in 2008. Japan JP: Income Share Held by Highest 10% data is updated yearly, averaging 24.700 % from Dec 2008 (Median) to 2008, with 1 observations. Japan JP: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

  13. Households by annual income India FY 2021

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Households by annual income India FY 2021 [Dataset]. https://www.statista.com/statistics/482584/india-households-by-annual-income/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

  14. INCOME Percent Households by Income in 1999 COS 2000

    • s.cnmilf.com
    • datasets.ai
    • +3more
    Updated Dec 2, 2020
    + more versions
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    U.S. Department of Commerce, Bureau of the Census, Geography Division (Point of Contact) (2020). INCOME Percent Households by Income in 1999 COS 2000 [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/income-percent-households-by-income-in-1999-cos-2000
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    Dataset updated
    Dec 2, 2020
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

  15. INCOME Percent Families by Income in 1999 BGs 2000

    • gstore.unm.edu
    • datasets.ai
    + more versions
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    U.S. Department of Commerce, Bureau of the Census, Geography Division, INCOME Percent Families by Income in 1999 BGs 2000 [Dataset]. http://gstore.unm.edu/apps/rgis/datasets/40115fbb-94ef-4d59-b9ee-5a278861f1ad/metadata/ISO-19115:2003.html
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    Dataset provided by
    United States Department of Commercehttp://www.commerce.gov/
    United States Census Bureauhttp://census.gov/
    Time period covered
    Dec 31, 2000
    Area covered
    West Bound -109.050781 East Bound -103.002449 North Bound 37.000313 South Bound 31.332279
    Description

    TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

  16. c

    Housing Affordability

    • data.ccrpc.org
    csv
    Updated Oct 17, 2024
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    Champaign County Regional Planning Commission (2024). Housing Affordability [Dataset]. https://data.ccrpc.org/dataset/housing-affordability
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    csv(2343)Available download formats
    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Champaign County Regional Planning Commission
    Description

    The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

    How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

    The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

    Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

    Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

    As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

    Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

    For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

    [1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

    [2] Ibid.

    Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

  17. Data from: Gangs in Rural America, 1996-1998

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Mar 12, 2025
    + more versions
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    National Institute of Justice (2025). Gangs in Rural America, 1996-1998 [Dataset]. https://catalog.data.gov/dataset/gangs-in-rural-america-1996-1998-9527e
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justicehttp://nij.ojp.gov/
    Area covered
    United States
    Description

    This study was undertaken to enable cross-community analysis of gang trends in all areas of the United States. It was also designed to provide a comparative analysis of social, economic, and demographic differences among non-metropolitan jurisdictions in which gangs were reported to have been persistent problems, those in which gangs had been more transitory, and those that reported no gang problems. Data were collected from four separate sources and then merged into a single dataset using the county Federal Information Processing Standards (FIPS) code as the attribute of common identification. The data sources included: (1) local police agency responses to three waves (1996, 1997, and 1998) of the National Youth Gang Survey (NYGS), (2) rural-urban classification and county-level measures of primary economic activity from the Economic Research Service (ERS) of the United States Department of Agriculture, (3) county-level economic and demographic data from the County and City Data Book, 1994, and from USA Counties, 1998, produced by the United States Department of Commerce, and (4) county-level data on access to interstate highways provided by Tom Ricketts and Randy Randolph of the University of North Carolina at Chapel Hill. Variables include the FIPS codes for state, county, county subdivision, and sub-county, population in the agency jurisdiction, type of jurisdiction, and whether the county was dependent on farming, mining, manufacturing, or government. Other variables categorizing counties include retirement destination, federal lands, commuting, persistent poverty, and transfer payments. The year gang problems began in that jurisdiction, number of youth groups, number of active gangs, number of active gang members, percent of gang members who migrated, and the number of gangs in 1996, 1997, and 1998 are also available. Rounding out the variables are unemployment rates, median household income, percent of persons in county below poverty level, percent of family households that were one-parent households, percent of housing units in the county that were vacant, had no telephone, or were renter-occupied, resident population of the county in 1990 and 1997, change in unemployment rates, land area of county, percent of persons in the county speaking Spanish at home, and whether an interstate highway intersected the county.

  18. c

    Poverty Rate

    • data.ccrpc.org
    • data.cuuats.cloud.ccrpc.org
    csv
    Updated Oct 17, 2024
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    Champaign County Regional Planning Commission (2024). Poverty Rate [Dataset]. https://data.ccrpc.org/dataset/poverty-rate
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    csv(393)Available download formats
    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Champaign County Regional Planning Commission
    Description

    This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

    The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

    The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

    Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

    As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

    Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

    For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

    *According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

    Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

  19. T

    United States Corporate Profits

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 26, 2025
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    TRADING ECONOMICS (2025). United States Corporate Profits [Dataset]. https://tradingeconomics.com/united-states/corporate-profits
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 31, 1947 - Mar 31, 2025
    Area covered
    United States
    Description

    Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  20. w

    INCOME Percent Families by Income in 1999 COS 2000

    • data.wu.ac.at
    • datadiscoverystudio.org
    • +3more
    csv, excel, geojson +9
    Updated Jun 25, 2014
    + more versions
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    Earth Data Analysis Center, University of New Mexico (2014). INCOME Percent Families by Income in 1999 COS 2000 [Dataset]. https://data.wu.ac.at/schema/data_gov/ZjMxZTZmYmQtYTc0OC00MjhkLWE1ODItOGI5NzhjYTI3N2Zm
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    geojson, excel, html, csv, kml, wms, xml, wfs, gml, json, zip, shpAvailable download formats
    Dataset updated
    Jun 25, 2014
    Dataset provided by
    Earth Data Analysis Center, University of New Mexico
    Area covered
    57c082cb1cc125820b676adbdd7243ce1add24f6
    Description

    TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

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Government of Canada, Statistics Canada (2024). High income tax filers in Canada, specific geographic area thresholds [Dataset]. http://doi.org/10.25318/1110005601-eng
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High income tax filers in Canada, specific geographic area thresholds

1110005601

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Dataset updated
Oct 28, 2024
Dataset provided by
Statistics Canadahttps://statcan.gc.ca/en
Area covered
Canada
Description

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

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