VITAL SIGNS INDICATOR
Poverty (EQ5)

FULL MEASURE NAME
The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED
January 2023

DESCRIPTION
Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE
U.S Census Bureau: Decennial Census - http://www.nhgis.org
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

Pensioners' Incomes (PI) contains estimates of the levels, sources and distribution of pensioners' incomes. It also examines the position of single pensioners and pensioner couples, including any dependent children, within the income distribution of the population as a whole. This differs from Households Below Average Income (HBAI) (see SNs 5828 and 7196), where analysis is on a household basis, and includes the income of adults not in the pensioner unit but living in the same household. The PI undertakes a few extra steps beyond the FRS and HBAI data to derive pension income variables.

The PI series is a key source of information used to inform Government thinking on relevant policies and related programmes and projects. Researchers and analysts outside the government use statistics and data to examine topics such as ageing, the distributional impacts of fiscal policies, and pensioner groups' income profiles. The PI estimates are usually based on a sample of around 7,000 adults over State Pension age, who reside in private households in the United Kingdom, taken from the Family Resources Survey (FRS).

The gov.uk Pensioners' Incomes Statistics webpage contains annual reports, accompanying tables, research, and technical papers.

PI data are also available from 1994/95 onwards via the Department for Work and Pensions (DWP) Stat-Xplore online tool.

Secure Access PI data

The Secure Access version of the PI series (SN 9257) is available from 2007/08 onwards, whereas the standard End User Licence (EUL) data (SN 8503) are available from 2008/09. Unlike the EUL versions, the ages of the head of household and spouse have not been top-coded at 80 years in the Secure Access version. Prospective users of the Secure Access version must fulfil additional requirements beyond those associated with the EUL datasets. The Secure Access version of FRS is held under SN 9256, and the Secure Access version of HBAI is available under SN 7196.

Latest edition information

For the 7th edition (April 2025), data and documentation for 2023/24 were added to the study.

The Social Policy Simulation Database and Model (SPSD/M) is a tool designed to assist those interested in analyzing the financial interactions of governments and individuals in Canada. It can help one to assess the cost implications or income redistributive effects of changes in the personal taxation and cash transfer system. As the name implies, SPSD/M consists of two integrated parts: a database (SPSD), and a model (SPSM). The SPSD is a non-confidential, statistically representative database of individuals in their family context, with enough information on each individual to compute taxes paid to and cash transfers received from government. The SPSM is a static accounting model which processes each individual and family on the SPSD, calculates taxes and transfers using legislated or proposed programs and algorithms, and reports on the results. A sophisticated software environment gives the user a high degree of control over the inputs and outputs to the model and can allow the user to modify existing programs or test proposals for entirely new programs. The model comes with full documentation including an on-line help facility. Users and Applications The SPSD/M has been used in hundreds of sites across Canada. These sites have diverse research interests in the area of income tax-transfer and commodity tax systems in Canada as well as varied experience in micro-simulation. Our growing client base includes federal departments, provincial governments, universities, interest groups, corporate divisions, and private consultants. The diverse applications of the SPSD/M can be seen in the following examples of studies and published research reports: Costing out proposals for amendments to the Income Tax Act affecting the tax treatment of seniors and the disabled Estimating the fiscal viability of major personal tax reform options, including three flat tax scenarios The comparison low income (poverty) measures and their effect on the estimates of the number of poor An Analysis of the Distributional Impact of the Goods and Services Tax Married and Unmarried Couples: The Tax Question Taxes and Transfers in Rural Canada Equivalencies in Canadian Public Policy When the Baby Boom Grows Old: Impact on Canada's Public Sector Some potential uses of the model are illustrated by the following list of questions which may be answered using the SPSM: How large an increase in the federal Child Tax Benefit could be financed by allocating an additional $500 million to the program? Which province would have the most advantageous tax structure for an individual with $45,000 earned income, 2 children and $15,000 of investment income? What is the after-tax value of the major federal child support programs on a per child basis, and how are these benefits distributed across family types and income groups? How many individuals otherwise paying no tax would have to pay tax under various minimum tax systems, and what would additional government revenues be? How much money would be needed to raise all low income families and persons to Statistics Canada's low income cut-offs in 2014? How much would average household "consumable" income rise if a province eliminated its gasoline taxes? How much would federal government revenue rise by if there was an increase in the GST rate?

The Social Policy Simulation Database and Model (SPSD/M) is a tool designed to assist those interested in analyzing the financial interactions of governments and individuals in Canada. It can help one to assess the cost implications or income redistributive effects of changes in the personal taxation and cash transfer system. As the name implies, SPSD/M consists of two integrated parts: a database (SPSD), and a model (SPSM). The SPSD is a non-confidential, statistically representative database of individuals in their family context, with enough information on each individual to compute taxes paid to and cash transfers received from government. The SPSM is a static accounting model which processes each individual and family on the SPSD, calculates taxes and transfers using legislated or proposed programs and algorithms, and reports on the results. A sophisticated software environment gives the user a high degree of control over the inputs and outputs to the model and can allow the user to modify existing programs or test proposals for entirely new programs. The model comes with full documentation including an on-line help facility. Users and Applications The SPSD/M has been used in hundreds of sites across Canada. These sites have diverse research interests in the area of income tax-transfer and commodity tax systems in Canada as well as varied experience in micro-simulation. Our growing client base includes federal departments, provincial governments, universities, interest groups, corporate divisions, and private consultants. The diverse applications of the SPSD/M can be seen in the following examples of studies and published research reports: Costing out proposals for amendments to the Income Tax Act affecting the tax treatment of seniors and the disabled Estimating the fiscal viability of major personal tax reform options, including three flat tax scenarios The comparison low income (poverty) measures and their effect on the estimates of the number of poor An Analysis of the Distributional Impact of the Goods and Services Tax Married and Unmarried Couples: The Tax Question Taxes and Transfers in Rural Canada Equivalencies in Canadian Public Policy When the Baby Boom Grows Old: Impact on Canada's Public Sector Some potential uses of the model are illustrated by the following list of questions which may be answered using the SPSM: How large an increase in the federal Child Tax Benefit could be financed by allocating an additional $500 million to the program? Which province would have the most advantageous tax structure for an individual with $45,000 earned income, 2 children and $15,000 of investment income? What is the after-tax value of the major federal child support programs on a per child basis, and how are these benefits distributed across family types and income groups? How many individuals otherwise paying no tax would have to pay tax under various minimum tax systems, and what would additional government revenues be? How much money would be needed to raise all low income families and persons to Statistics Canada's low income cut-offs in 2014? How much would average household "consumable" income rise if a province eliminated its gasoline taxes? How much would federal government revenue rise by if there was an increase in the GST rate?

Graph and download economic data for Government social benefits: to persons: State and local: Public assistance: Family assistance (B1598C1A027NBEA) from 1933 to 2023 about assistance, social assistance, state & local, public, benefits, government, persons, GDP, and USA.

Abstract copyright UK Data Service and data collection copyright owner.

Pensioners' Incomes (PI) contains estimates of the levels, sources and distribution of pensioners' incomes. It also examines the position of single pensioners and pensioner couples, including any dependent children, within the income distribution of the population as a whole. This differs from Households Below Average Income (HBAI) (see SNs 5828 and 7196), where analysis is on a household basis, and includes the income of adults not in the pensioner unit but living in the same household. The PI undertakes a few extra steps beyond the FRS and HBAI data to derive pension income variables.

The PI series is a key source of information used to inform Government thinking on relevant policies and related programmes and projects. Researchers and analysts outside the government use statistics and data to examine topics such as ageing, the distributional impacts of fiscal policies, and pensioner groups' income profiles. The PI estimates are usually based on a sample of around 7,000 adults over State Pension age, who reside in private households in the United Kingdom, taken from the Family Resources Survey (FRS).

The gov.uk Pensioners' Incomes Statistics webpage contains annual reports, accompanying tables, research, and technical papers.

PI data are also available from 1994/95 onwards via the Department for Work and Pensions (DWP) Stat-Xplore online tool.

Secure Access PI data

The Secure Access version of the PI series (SN 9257) is available from 2007/08 onwards, whereas the standard End User Licence (EUL) data (SN 8503) are available from 2008/09. Unlike the EUL versions, the ages of the head of household and spouse have not been top-coded at 80 years in the Secure Access version. Prospective users of the Secure Access version must fulfil additional requirements beyond those associated with the EUL datasets. The Secure Access version of FRS is held under SN 9256, and the Secure Access version of HBAI is available under SN 7196.

Latest edition information

For the second edition (April 2025), data and documentation for 2023/24 were added to the study.

Main Topics:
Variables cover household and demographic information, with incomes from a range of sources (pensions, benefits, savings and investments, and any earnings from work).

This statistic shows the total personal income in the United States from 1990 to 2023. The data are in current U.S. dollars not adjusted for inflation or deflation. According to the BEA, personal income is the income that is received by persons from all sources. It is calculated as the sum of wage and salary disbursements, supplements to wages and salaries, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, less contributions for government social insurance. Personal income increased to about 23 trillion U.S. dollars in 2023.Personal income Personal income in the United States has risen steadily over the last decades from 5.07 trillion U.S. dollars in 1991 to 23 trillion U.S. dollars in 2023. Personal income includes all earnings including wages, investments, and other sources. Personal income also varied widely across the U.S., where those living in the District of Columbia, on the higher scale, earned an average of 96,873 U.S. dollars per capita and on the lower end of the spectrum, people in Mississippi earned 45,438 U.S. dollars per capita. In the District of Columbia, disposable income averaged some 81,193 U.S. dollars. In total, California earned the most personal income followed by Texas, receiving three trillion U.S. dollars and 1.76 trillion U.S. dollars, respectively. Income tends to vary widely between demographics in the United States. Those with higher education levels tend to earn more money. However, only 25.7 percent of persons with a disability that had a Bachelor's degree or higher were employed in 2020. The Social Security and Supplemental Security Income disability programs provide monetary benefits to the disabled and certain family members.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

Abstract copyright UK Data Service and data collection copyright owner.

The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data from respondents in Great Britain. Information is gathered on a range of subjects, commissioned both internally by the Office for National Statistics (ONS) and by external clients (other government departments, charities, non-profit organisations and academia).

One individual respondent, aged 16 or over, is selected from each sampled private household to answer questions. Data are gathered on the respondent, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. Each regular OPN survey consists of two elements. Core questions, covering demographic information, are asked together with non-core questions that vary depending on the module(s) fielded.

The OPN collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living. The OPN has expanded to include questions on other topics of national importance, such as health and the cost of living.

For more information about the survey and its methodology, see the gov.uk OPN Quality and Methodology Information (QMI) webpage.

Changes over time

Up to March 2018, the OPN was conducted as a face-to-face survey. From April 2018 to November 2019, the OPN changed to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for module customers.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held under Secure Access conditions in SN 8635, ONS Opinions and Lifestyle Survey, 2019-2023: Secure Access. (See below for information on other Secure Access OPN modules.)

From August 2021, as coronavirus (COVID-19) restrictions were lifted across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remained sustainable.

Secure Access OPN modules

Besides SN 8635 (which includes the COVID-19 Module), other Secure Access OPN data includes sensitive modules run at various points from 1997-2019, including Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See the individual studies for further details and information on how to apply to use them.

For the second edition, January 2012, corrected weights were added to all data files.

Main Topics:

The non-core questions for this month were:
Tobacco consumption (Module 210): this module was asked on behalf of HM Revenue and Customs to help estimate the amount of tobacco consumed as cigarettes. Due to the potentially sensitive nature of the data within this module, cases for respondents aged under 18 have been removed.
Disability monitoring (Module 363): this module was asked on behalf of the Department for Work and Pensions (DWP) which is interested in information on disability and includes two questions that ask about awareness of the Disability Discrimination Act. The module aims to identify the scale of problems those with long-term illnesses or disabilities have accessing goods, facilities and services. This version of the data does not contain variables M363_3M, M363_6AM, M363_6bM, M363_7M, M363_26, M363_27, M363_28, and M363_29.
Attitudes to charities (Module MAR): this module was asked on behalf of the Home Office, which is specifically interested in examining levels of trust and confidence in charities. The questions are designed to collect data on the public’s perception of charities.
Legal services (Module MBT): this module was asked on behalf of the Ministry of Justice. It is designed to find out about people’s use of legal services for personal matters only.
Social norms (Module MBU): this module was asked on behalf of University College London (UCL) and is designed to collect information regarding people’s views on eating fruits and vegetables.

In total, about 59.9 percent of U.S. households paid income tax in 2022. The remaining 40.1 percent of households paid no individual income tax. In that same year, about 47.1 percent of U.S. households with an income between 40,000 and 50,000 U.S. dollars paid no individual income taxes.

Abstract copyright UK Data Service and data collection copyright owner.The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds data from 1972 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies. Further information may be found on the gov.uk National Travel Survey webpage. End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation. Changes to the methodology in 2002 mean that there are some inconsistencies with data for previous years. Most notably, an under-recording of short walks in 2002 and 2003 affects trends over this period, particularly in the number of trips per person.2020 and 2021 Disclaimer:Due to changes in the methodology of data collection, changes in travel behaviour, and a reduction of data collected during 2020 and 2021 as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing it to other years due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the eighteenth edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. Main Topics: The NTS data includes: attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. In one half of the sample, the attitudinal questions were asked as part of the household questionnaire (as has been in the case in previous years) and in the other half one randomly selected adult per household was asked the attitudinal questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination; Please see the Lookup Tables documentation for the full list of variables. Multi-stage stratified random sample Telephone interview Diaries Face-to-face interview Self-completion 2002 2023 ACCESS TO FACILITIES ACCESS TO HEALTH SE... AGE AIR TRANSPORT ATTITUDES BICYCLES BUSES BUSINESS TRIPS CAR PARKING AREAS CAR SHARING CARBON DIOXIDE EMIS... CARS COMMUTING COMPANY CARS COMPUTERS COSTS CYCLE LANES DELIVERY SERVICES DIARIES DISABLED PERSONS DISTANCE MEASUREMENT DRIVING DRIVING LICENCES ECONOMIC ACTIVITY EMPLOYEES ETHNIC GROUPS EXPENDITURE England FOSSIL FUELS FULL TIME EMPLOYMENT GENDER HEADS OF HOUSEHOLD HOME BASED WORK HOSPITAL SERVICES HOUSEHOLD HEAD S EC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING HOUSING TENURE INCOME LEISURE TIME LOCAL COMMUNITY FAC... MARITAL STATUS MEDICAL CENTRES MOBILITY SCOOTERS MOTOR VEHICLE HIRE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL STATUS OCCUPATIONS ONLINE SHOPPING PART TIME EMPLOYMENT PEDESTRIAN FACILITIES PETROL PETROL CONSUMPTION PHARMACIES PHYSICAL DISABILITIES PHYSICAL MOBILITY POST OFFICES PUBLIC TRANSPORT RAILWAY STATIONS RAILWAY TRANSPORT RESIDENTIAL MOBILITY ROAD VEHICLE MAINTE... RURAL AREAS SATISFACTION SCHOOLS SELF EMPLOYED SHOPPING SHOPS SOCIAL ACTIVITIES L... SOCIO ECONOMIC STATUS STATUS IN EMPLOYMENT STUDENT TRANSPORTATION SUPERVISORY STATUS TRAINS TRANSPORT TRANSPORT FARES TRANSPORT INFRASTRU... TRANSPORT SAFETY TRAVEL TRAVEL CONCESSIONS TRAVEL PASSES TRAVELLING TIME Transport and travel UNDERGROUND RAILWAYS UNEMPLOYED URBAN AREAS VELOCITY WALKING WALKING AIDS WORKPLACE

Low income cut-offs (LICOs) before and after tax by community size and family size, in current dollars, annual.

The survey on attitudes towards political fields of duty was conducted by Kantar on behalf of the Press and Information Office of the Federal Government. Persons aged 14 and older were surveyed in telephone interviews (CATI) on the following topics: attitudes towards political fields of duty and assessment of the federal government in these areas. Respondents were selected using a multistage random sample including landline and mobile phone numbers (dual-frame sample).
Assessment of the work of the federal government of SPD, Bündnis 90/Die Grünen and FDP as a whole; importance of various political tasks (fighting unemployment, creating framework conditions for economic growth, ensuring price stability, regulating the immigration of foreigners, distributing the tax burden fairly, improving conditions for families with children, modernising the health system, ensuring social justice, promoting new technologies, securing old-age pensions in the long term, limiting national debt, ensure internal security, represent German interests abroad, ensure a clean environment and protect the climate, provide good educational opportunities, ensure affordable electricity prices, improve conditions for nursing care, promote a change in transport, better protect the data of citizens and companies, speed up the energy transition, integrate refugees into German society, provide affordable housing); assessing the work of the Federal Government in the aforementioned various political task areas.

Demography: sex; age; education; occupation; household size; number of persons in the household aged 14 and over; party preference; voter eligibility; net household income.

Additionally coded: ID; month; consecutive respondent number, weighting factor; date of interview; BIK locality size; political locality size; federal state; west/east; survey by mobile phone vs. fixed network.

The Household, Income and Labour Dynamics in Australia (HILDA) Survey is a nationally representative longitudinal study of Australian households which commenced in 2001. Funded by the Australian Government Department of Social Services (DSS), the HILDA Survey is managed by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne. The HILDA Survey provides longitudinal data on the lives of Australian residents. Its primary objective is to support research questions falling within three broad and inter-related areas of income, labour market and family dynamics. The HILDA Survey is a household-based panel study of Australian households and, as such, it interviews all household members (15 years and over) of the selected households and then re-interviews the same people in subsequent years. This dataset is the 23rd release of the HILDA data, incorporating data collected from 2001 through 2023 (Waves 1-23). The special topic module in Wave 22 is wealth, and includes questions on employment-related discrimination, updates to citizenship and permanent residency and material deprivation

Panama PA: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 22.000 % in 2023. This records an increase from the previous number of 21.500 % for 2021. Panama PA: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 23.900 % from Dec 1979 (Median) to 2023, with 29 observations. The data reached an all-time high of 30.200 % in 1989 and a record low of 21.500 % in 2021. Panama PA: 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 Panama – Table PA.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).

U.S. Census Bureau QuickFacts statistics for Iowa. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

In September 2024, the disposable personal income in the United States increased by 0.3 percent from the previous month. The data are in current U.S. dollars, seasonally adjusted at annual rates. Disposable personal income in the United States According to the BEA, personal income is the income that is received by persons from all sources. It is calculated as the sum of wage and salary disbursements, supplements to wages and salaries, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, minus contributions for government social insurance. In simple terms, disposable personal income is the total remaining income after taxes paid; it is the income available to persons for spending or saving. It is useful to economists because it measures the amount of money available for spending in a specific area. Disposable personal income is a significant indicator of an economy’s health. Personal income determines an individual’s ability to consume goods and services, i.e. personal consumption expenditure, and industries producing consumer goods and services contribute heavily to United States gross domestic product. The retail trade industry, for example, contributed 1.38 trillion chained U.S. dollars to the GDP of the United States in 2021. Total real GDP amounted to about 22.99 trillion U.S. dollars that year. The arts, entertainment, recreation, accommodation and food services industry contributed 839.6 billion U.S. dollars to the GDP in 2021. Personal income in the United States was 21.06 trillion U.S. dollars in 2021, the highest value in over ten years.

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.