Graph and download economic data for Households; Net Worth, Level (BOGZ1FL192090005Q) from Q4 1987 to Q1 2025 about net worth, Net, households, and USA.

This table shows the distribution of wealth of households. The figures in this table are broken down to components of wealth and different household characteristics.

Data available from: 2006. The population consists of all private households with income on January 1st of the reporting year.

Status of the figures: The figures for 2006 to 2022 are final. The figures for 2023 are preliminary.

The compilation of the figures has been changed in a number of parts from reporting year 2011 compared to previous years: From 2011, more complete information on bank and saving credits and securities is available. All small amounts are also observed from that moment on. As a result, there are more households with these assets. From 2011, more complete information on debts is available. Education loans and loans from banks are fully observed from that moment on. As a result, there are more households with other loans.

Changes as of 1 November 2024: Update with final figures for 2022 and provisional figures for 2023

When will new figures be published? New figures for 2024 will be published in the fall of 2025.

The United States is leading the ranking by number of high networth individuals , recording **** million individuals. Following closely behind is China with **** million individuals, while Lesotho is trailing the ranking with * thousand individuals, resulting in a difference of **** million individuals to the ranking leader, the United States. High Net Worth Individuals are here defined as persons with investible assets of at least *********** U.S. dollars in current exchange rate terms.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

A variety of datasets for analysis of High Wealth individuals to assist HMRC's High Net Worth Unit in maintaining and refining its population. Matches 10 years of Inheritance Tax Data to the relevant in-life SA data. Updated: ad hoc.

Utilizing Esri Updated Demographics Categories (boundaries from the 2020 U.S. Census Bureau Data). This layer was created using Esri's Enrich tool to display some of the categories below at a Block Group level for Idaho. Esri Updated Demographics categories include the following:PopulationAge—By Generations, Age Dependency RatiosRace and Ethnicity—Diversity IndexSchool-Educational attainmentWork—Labor Force, Economic Dependency RatiosIncome—Total Income, Income by AgeHouseholds—Total Households, Tenure, FamiliesFamiliesHousing and Wealth—Total Housing Units, Housing Affordability Index, Percent of Income for Mortgage, Wealth Index, Contract RentHistorical Time Series—Population, Households, and Housing Units for each year between 2010 and current yearMethodology 2023/2028 Demographics2023-2028 Data Catalog

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

The 2018 edition of Woods and Poole Complete U.S. Database provides annual historical data from 1970 (some variables begin in 1990) and annual projections to 2050 of population by race, sex, and age, employment by industry, earnings of employees by industry, personal income by source, households by income bracket and retail sales by kind of business. The Complete U.S. Database contains annual data for all economic and demographic variables for all geographic areas in the Woods & Poole database (the U.S. total, and all regions, states, counties, and CBSAs). The Complete U.S. Database has following components: Demographic & Economic Desktop Data Files: There are 122 files covering demographic and economic data. The first 31 files (WP001.csv – WP031.csv) cover demographic data. The remaining files (WP032.csv – WP122.csv) cover economic data. Demographic DDFs: Provide population data for the U.S., regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), and counties. Each variable is in a separate .csv file. Variables: Total Population Population Age (breakdown: 0-4, 5-9, 10-15 etc. all the way to 85 & over) Median Age of Population White Population Population Native American Population Asian & Pacific Islander Population Hispanic Population, any Race Total Population Age (breakdown: 0-17, 15-17, 18-24, 65 & over) Male Population Female Population Economic DDFs: The other files (WP032.csv – WP122.csv) provide employment and income data on: Total Employment (by industry) Total Earnings of Employees (by industry) Total Personal Income (by source) Household income (by brackets) Total Retail & Food Services Sales ( by industry) Net Earnings Gross Regional Product Retail Sales per Household Economic & Demographic Flat File: A single file for total number of people by single year of age (from 0 to 85 and over), race, and gender. It covers all U.S., regions, states, CSAs, MSAs and counties. Years of coverage: 1990 - 2050 Single Year of Age by Race and Gender: Separate files for number of people by single year of age (from 0 years to 85 years and over), race (White, Black, Native American, Asian American & Pacific Islander and Hispanic) and gender. Years of coverage: 1990 through 2050. DATA AVAILABLE FOR 1970-2019; FORECASTS THROUGH 2050

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

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

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Families of tax filers; Distribution of total income by census family type and age of older partner, parent or individual (final T1 Family File; T1FF).

Net income for persons registered in the national population register during the whole year by region, sex, age, observations and year

Net-Income Time Series for Banco do Brasil S.A.. Banco do Brasil S.A., together with its subsidiaries, provides banking products and services for individuals, companies, and public sectors in Brazil and internationally. The company's Banking segment offers various products and services, including deposits, loans, and other services to retail, wholesale, and public sectors. This segment also engages in the business with micro-entrepreneurs and low-income population undertaken through banking correspondents. Its Investments segment engages in the domestic capital markets; intermediation and distribution of debts in the primary and secondary markets; and provision of equity investments and financial services. The company's Fund Management segment is involved in the purchase, sale, and custody of securities and portfolio management; and management of investment funds and clubs. Its Insurance, Pension and Capitalization segment provides life, property, and automobile insurance products, as well as private pension and capitalization plans. The company's Payments Method segment provides funding, transmission, processing, and settlement services for electronic transactions. Its Other segment engages in the provision of credit recovery and consortium management services. This segment is also involved in developing, manufacturing, selling, leasing, and integrating of digital electronic equipment and systems, peripherals, programs, inputs, and computing supplies. The company was incorporated in 1808 and is headquartered in Brasília, Brazil.

The data is a gridded dataset of global exposure (population, gross domestic product and net fixed asset value) at multiple spatial scales, spanning years 1850 to 2100 at annual resolution, including five future trajectories consistent with the Shared Socio-economic Pathways (SSPs).

Due to large file sizes, only a selection of resolutions and timesteps is provided in this repository: a spatial resolution of 30 arc seconds (approximately 0.93 km at the equator) and at 30 arc min (approximately 56km at the equator). The Python code and input data provided alongside the dataset enable users to generate different resolutions and timesteps as required by their research needs. See the description in documentation/ folder and the readme file in the code/ folder.

Note: the input data is divided into multiple zip files. They all need to be downloaded and unzipped together. In addition to the files here, running the model requires the following external datasets:

• selected Global Human Settlement Layer files (population and build-up surface at 30” resolution, 1975-2030, Total RES+NRES, WGS84 coordination system) from https://human-settlement.emergency.copernicus.eu/download.php
• HYDE 3.2 population count (‘popc’, decennial 1850 AD-1980 AD) in the ‘baseline’ scenario, https://geo.public.data.uu.nl/vault-hyde/HYDE%203.2%5B1710494848%5D/original/
• gridded population projections from Wang et al. (2022), https://doi.org/10.6084/m9.figshare.19608594.v2

The dataset is provided as Deliverable 3.1 of the European Union’s HORIZON project COMPASS

We present here a new dataset of per capita disposable income for 42 European countries (and more than 120,000 administrative units at the subnational level), over the 2010-2020 period (with few additional years for some countries). This dataset was created by harmonizing disparate income data (net earnings, gross income, disposable income, etc.) gathered from national statistical institutes across Europe. Disposable income was converted to constant 2015 EU27 PPP€ to adjust for the costs of living and inflation across countries and to allow comparability over time. Total population and a measure of income inequality (Gini index) are also provided for subnational administrative units. Users can download the aggregated dataset covering the whole years (Disposable_Inc_DB.gpkg) or yearly files.

This dataset provides quarterly personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis . Data includes the following estimates: personal income, per capita personal income, proprietors' income, farm proprietors' income, compensation of employees and private nonfarm earnings, compensation, and wages and salaries for wholesale trade. Personal income, proprietors' income, and farm proprietors' income available beginning 1997; per capita personal income available beginning 2010; and all other data beginning 1998.

Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s midquarter population estimates.

Proprietors' income is the current-production income (including income in kind) of sole proprietorships, partnerships, and tax-exempt cooperatives. Corporate directors' fees are included in proprietors' income. Proprietors' income includes the interest income received by financial partnerships and the net rental real estate income of those partnerships primarily engaged in the real estate business.

Farm proprietors’ income as measured for personal income reflects returns from current production; it does not measure current cash flows. Sales out of inventories are included in current gross farm income, but they are excluded from net farm income because they represent income from a previous year’s production.

Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee.

Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government.

Private nonfarm wages and salaries is wages and salaries excluding farm and government. Wages and salaries is the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans.

More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

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.

We present here a new dataset of per capita disposable income for 42 European countries (and more than 120,000 administrative units at the subnational level), over the 2010-2020 period (with few additional years for some countries). This dataset was created by harmonizing disparate income data (net earnings, gross income, disposable income, etc.) gathered from national statistical institutes across Europe. Disposable income was converted to constant 2015 EU27 PPP€ to adjust for the costs of living and inflation across countries and to allow comparability over time. Total population and a measure of income inequality (Gini index) are also provided for subnational administrative units. Users can download the aggregated dataset covering the whole years (Disposable_Inc_DB.gpkg) or yearly files.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2011 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2011 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from 2 sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form: The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form: These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form: There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire: These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire: These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.