This dataset provides annual numbers for each state in the United States for 2013-2018. Includes the following data: total population, median income, and number of people living at or below the poverty level.

Helpful information on using U.S. Census data is found at https://censusreporter.org/

This map compares the number of people living above the poverty line to the number of people living below. Why do this?There are people living below the poverty line everywhere. Nearly every area of the country has a balance of people living above the poverty line and people living below it. There is not an "ideal" balance, so this map makes good use of the national ratio of 6 persons living above the poverty line for every 1 person living below it. Please consider that there is constant movement of people above and below the poverty threshold, as they gain better employment or lose a job; as they encounter a new family situation, natural disaster, health issue, major accident or other crisis. There are areas that suffer chronic poverty year after year. This map does not indicate how long people in the area have been below the poverty line. "The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs." Source: U.S. Census BureauIn the U.S. overall, there are 6 people living above the poverty line for every 1 household living below. Green areas on the map have a higher than normal number of people living above compared to below poverty. Orange areas on the map have a higher than normal number of people living below the poverty line compared to those above in that same area.The map shows the ratio for counties and census tracts, using these layers, created directly from the U.S. Census Bureau's American Community Survey (ACS)For comparison, an older layer using 2013 ACS data is also provided.The layers are 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. Poverty status is based on income in past 12 months of survey. Current Vintage: 2014-2018ACS Table(s): B17020Data downloaded from: Census Bureau's API for American Community Survey National 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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., -555555...) have been set to null. 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. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

People living in households with very low work intensity are people aged 0-64 living in households where the adults (aged 18-64) worked less than 20% of their total work potential during the past year. Students, those who are retired or who receive any pension (except survivors pension) are excluded. The MIP auxiliary indicator is expressed as a percentage of the population aged 0 to 64. In the table, values are also presented as changes over a three-year period (in percentage points). The data source is the EU Statistics on Income and Living Conditions (EU-SILC).

The average American household consisted of 2.51 people in 2023.

Households in the U.S.

As shown in the statistic, the number of people per household has decreased over the past decades.

The U.S. Census Bureau defines a household as follows: “a household includes all the persons who occupy a housing unit as their usual place of residence. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live and eat separately from any other persons in the building and which have direct access from outside the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated persons who share living arrangements. (People not living in households are classified as living in group quarters.).”

The population of the United States has been growing steadily for decades. Since 1960, the number of households more than doubled from 53 million to over 131 million households in 2023.

Most of these households, about 34 percent, are two-person households. The distribution of U.S. households has changed over the years though. The percentage of single-person households has been on the rise since 1970 and made up the second largest proportion of households in the U.S. in 2022, at 28.88 percent.

In concordance with the rise of single-person households, the percentage of family households with own children living in the household has declined since 1970 from 56 percent to 40.26 percent in 2022.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

Statistics on the number of elderly people living alone in Changhua County

Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.500 % in 2021. This records a decrease from the previous number of 12.300 % for 2020. Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 12.200 % from Dec 2003 (Median) to 2021, with 19 observations. The data reached an all-time high of 14.400 % in 2013 and a record low of 10.500 % in 2021. Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

People in the age of 0 to 64 years living in households where the adults worked a working time equal or less than 20 % of their total combined work-time potential during the previous year.

As adults count people in the age of 18 to 64 years. Students aged 18 to 24 years; people who are retired according to their self-defined current economic status or who receive any pension (except survivor’s pension); and people in the age bracket 60 to 64 who are inactive and living in a household where the main income is pensions, are not taken into account.

People living in households with very low work intensity are people aged 0-64 living in households where the adults (aged 18-64) worked less than 20% of their total work potential during the past year. Students are excluded. Data are expressed both in % of population aged 0-64 and in change over 3 years (in % points). The source of the data is EU Statistics on Income and Living Conditions (EU SILC).

In 2022, nearly *** in five of the Generation Alpha were living in low-income households in the United States, with ** percent of Gen Alpha living in families who earn less annually than twice the value of the federal poverty level. In comparison, only ** percent of Baby Boomers and ** percent of Generation X were living in low-income households in that year.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

This dataset uses U.S. Census table B17020 - Poverty Status by Age The data shows the number of people per locality, the overall number of people living below the poverty level per locality, and then the number of people under age 18 living below the poverty level per locality. This last data element is broken down into three segments - aged <6 years, 6-11 years, and 12-17 years, which when added together equal the total number of children under age 18 living below the poverty level per locality.

Table contains count and percentage of county residents living alone. Data are presented for people of all ages and those 65 years and older. The measure is summarized at county, city, zip code and census tract. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B09019, B09020; data accessed on July 20, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.MATEDATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationt_pop_livingalone (Numeric): Number of people (all ages) living alonepct_pop_livingalone (Numeric): Percent of people (all ages) living alonet_p65plus (Numeric): People ages 65 and oldert_pop65_livingalone (Numeric): Number of people ages 65 and older living alonepct_pop65_livingalone (Numeric): Percent of people ages 65 and older living alone

Overview

This data set is a modified version of the Cost of Living Comparison dataset submitted by @stephenofarrell. One of the tasks listed for this dataset was to include data scientist salaries, which is incorporated into this dataset. The salaries included are normalized to annual amounts in USD/year. If there is not a value, then there was no data available via Glassdoor's dataset for that location.

Finland - People living in households with very low work intensity was -0.80% in December of 2020, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - People living in households with very low work intensity - last updated from the EUROSTAT on December of 2025. Historically, Finland - People living in households with very low work intensity reached a record high of 2.50% in December of 2011 and a record low of -2.50% in December of 2008.

France - People living in households with very low work intensity was 0.70% in December of 2020, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - People living in households with very low work intensity - last updated from the EUROSTAT on November of 2025. Historically, France - People living in households with very low work intensity reached a record high of 0.70% in December of 2020 and a record low of -1.80% in December of 2013.

Number of People living in an Area by County

Provides data on people moving through space, including total number observed, gender breakdown, group size, and age groups. The City of Seattle Department of Transportation (SDOT) is providing data from the public life studies it has conducted since 2017. These studies consist of measuring the number of people using public space and the types of activities present on select sidewalks across the city, as well as several parks and plazas. The data set is continually updated as SDOT and other parties conduct public life studies using Gehl Institute’s Public Life Data Protocol. This dataset consists of four component spreadsheets and a GeoJSON file, which provide public life data as well as information about the study design and study locations: 1 Public Life Study: provides details on the different studies that have been conducted, including project information. https://data.seattle.gov/Transportation/Public-Life-Data-Study/7qru-sdcp 2 Public Life Location: provides details on the sites selected for each study, including various attributes to allow for comparison across sites. https://data.seattle.gov/Transportation/Public-Life-Data-Locations/fg6z-cn3y 3 Public Life People Moving: provides data on people moving through space, including total number observed, gender breakdown, group size, and age groups. 4 Public Life People Staying: provides data on people staying still in the space, including total number observed, demographic data, group size, postures, and activities. https://data.seattle.gov/Transportation/Public-Life-Data-People-Staying/5mzj-4rtf 5 Public Life Geography: A GeoJSON file with polygons of every location studied. https://data.seattle.gov/Transportation/Public-Life-Data-Geography/v4q3-5hvp Please download and refer to the Public Life metadata document - in the attachment section below - for comprehensive information about all of the Public Life datasets.

Hungary - People living in households with very low work intensity was -1.60% in December of 2020, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - People living in households with very low work intensity - last updated from the EUROSTAT on November of 2025. Historically, Hungary - People living in households with very low work intensity reached a record high of 2.50% in December of 2008 and a record low of -6.20% in December of 2017.

Quarterly and historical data on people living in UK households by housing tenure and combined economic activity status of household members.