Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q2 2025 about net worth, wealth, percentile, Net, and USA.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

Graph and download economic data for Net Worth Held by the Bottom 50% (1st to 50th Wealth Percentiles) (WFRBLB50107) from Q3 1989 to Q2 2025 about net worth, wealth, percentile, Net, and USA.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. 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. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. -...

In May 2025, the average hourly earnings of all employees in the United States was at 11.30 U.S. dollars. The data have been seasonally adjusted. The deflators used for constant-dollar earnings shown here come from the Consumer Price Indexes Programs. The Consumer Price Index for All Urban Employees (CPI-U) is used to deflate the data for all employees. A comparison of the rate of wage growth versus the monthly inflation since 2020 rate can be accessed here. Real wages are wages that have been adjusted for inflation.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about child, poverty, percent, and USA.

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

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as the number of units in the structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four-month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files. For more information on this study, see the SIPP Web site. Information about child care is located in the topical modules for Waves III, V, VI, and VIII. The Wave I Topical Module covers recipiency and employment history. The Wave II Topical Module includes work disability, education and training, household relationships, marital, migration, and fertility histories. The Wave III Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, other assets, and child well-being. The Wave IV Topical Module covers adult well-being, work schedule, taxes, child care, and annual income and retirement accounts. Data in the Wave V Topical Module describe child support agreements, school enrollment and financing, support for nonhousehold members, adult and child disability, and employer-provided health benefits. The Wave VI Topical Module covers medical expenses and utilization of health care, work-related expenses, child support paid and child care, poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stock and mutual fund shares, mortgages, and other financial investments. The Wave VII Topical Module covers retirement, pension plan coverage, annual income, taxes, and retirement accounts. Variables on informal caregiving, such as who received unpaid care and how long they were cared for, are located in a separate topical module file. The Wave VIII Topical Module covers welfare reform, child well-being, and child care.

Graph and download economic data for GINI Index for the United States (SIPOVGINIUSA) from 1963 to 2023 about gini, indexes, and USA.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four-month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files. The Wave 1 Topical Module covers recipiency and employment history. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care and vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets. The Wave 4 Topical Module covers disability, taxes, child care, and annual income and retirement accounts. Data in the Wave 5 Topical Module describe child support, school enrollment and financing, support for nonhousehold members, adult and child disability, and employer-provided health benefits. Data in the Wave 6 Topical Module provide information on medical expenses, work-related expenses and child support paid, assets and liabilities, real estate, shelter costs, dependent care and vehicles, value of business, interest-earning accounts, rental properties, stock and mutual fund shares, mortgages, other financial investments. Wave 7 Topical Module includes annual income and retirement accounts, home health care, retirement expectations and pension plan coverage, and taxes. Wave 8 Topical Module covers adult well-being and welfare reform. Wave 9 Topical Module is the same as Waves 3 and 6 Topical Modules. Wave 10 Topical Module focuses on work schedules, disablility, taxes, child care, and annual income and retirement. Wave 11 includes child support, support for nonhousehold members, and adult and child disability. Wave 12 Topical Module is the same as Waves 3, 6, and 9 but also includes child well-being.

A statistical method called a dissimilarity index was used to quantify how segregated the region is among its census tracts, first by race and ethnicity, and then by income. The indices use decennial census tract data for years 1990 and 2000. The U.S. Census Bureau’s American Communities Survey (ACS) five-year period estimates. The Racial/Ethnic Segregation Index is an indicator of how segregated the region's census tracts are, relative to the regional distribution of white, non-Latinx population and people of color and Latinx populations. In a given year, values show dissimilarity in the racial and ethnic makeup of tracts across the region, and demonstrates the percentage of the population that would, theoretically, need to relocate in order to match the region's racial and ethnic makeup. The higher the index value, the higher the segregation is among white, non-Latinx and other races/ethnicities. The Income Segregation Index is an indicator of how segregated the region's census tracts are by income, relative to the regional distribution of low-income households. Low-income is defined as households below 200% of the federal poverty rate. This index shows dissimilarity in income for census tracts across the region for each year displayed, and demonstrates what percentage of the population would need to relocate to another census tract in order to match the income distribution for the entire region. The higher the index value, the greater the geographic concentration of wealth or poverty.

This dataset was introduced in a competition on Zindi to challenge data professionals to predict whether members of the test population would be earning below or above $50,000 based on the variables taken into account in the analysis.

Analysis Objective.

The objective of this challenge is to create a machine learning model to predict whether an individual earns above or below a certain amount.

This solution can potentially reduce the cost and improve the accuracy of monitoring key population indicators such as income level in between census years. This information will help policymakers to better manage and avoid income inequality globally.

Data background.

This data has been collected from a random population.

There are ~200 000 individuals in train and ~100 000 individuals in the test file.

The train & test data will be used to create a machine learning model to predict if an individual earns above 50 000 of a specific currency.

The key variables are as follows: * Age. * Gender. * Education. * Class. * Education institute. * Marital status. * Race. * Is hispanic. * Employment commitment. * Unemployment reason. * Employment state. * Wage per hour. * Is part of labor union. * Working week per year. * Industry code. * Main Industry code. * Occupation code. * Main Occupation code. * Total employed. * Household stat. * Household summary. * Under 18 family. * Veterans adminquestionnaire. * Veteran benefit. * Tax status. * Gains. * Losses. * Stocks status. * Citizenship. * Migration year. * Country of birth own. * Country of birth father. * Country of birth mother. * Migration code change in msa. * Migration previous sunbelt. * Migration code move within registration. * Migration code change in registration. * Residence 1 year ago. * Old residence registration. * Old residence state. * Importance of record. * Income above limit.

Based on the variables set up and the data target requirements, the analysis can be assumed to be based on 20th century American population data where the median income was about $ 50,000.

Why Predict Income?

Income prediction extracts insights from individual and population-level data as it offers the ability to forecast income levels, assess financial risks, target marketing campaigns, and inform crucial decision-making in diverse spheres. However, ethical considerations, potential biases, and data privacy concerns demand careful attention alongside its undeniable benefits.

Applications in Specific Industries:

Finance:

• Credit scoring and loan approval: Predicting income to assess risk and determine loan eligibility.
• Fraud detection: Identifying suspicious financial activity based on income patterns.
• Targeted marketing: Tailoring financial products and services to individuals based on predicted income.
• Wealth management: Personalized investment strategies based on income potential and risk tolerance.

Healthcare:

• Predicting healthcare costs and resource allocation based on patient income and demographics.
• Identifying individuals at risk of financial hardship due to medical bills.
• Developing targeted health insurance plans based on income segments.
• Assessing eligibility for government healthcare programs.

Marketing and Retail:

• Customer segmentation and targeted advertising based on predicted income groups.
• Product pricing strategies tailored to different income segments.
• Predicting customer lifetime value and optimizing marketing spend.
• Identifying potential high-value customers based on income potential.

Human Resources:

• Salary benchmarking and compensation planning based on industry standards and predicted income trends.
• Predicting employee turnover risk based on job satisfaction and income-related factors.
• Identifying and attracting top talent by offering competitive compensation packages.
• Optimizing talent management strategies based on income potential and skillsets.

Public Policy:

• Predicting tax revenue and allocating government resources based on income distribution.
• Identifying individuals in need of social assistance programs based on income poverty.
• Evaluating the effectiveness of government policies aimed at income inequality.
• Designing progressive taxation systems based on predicted income levels.

Challenges and Considerations:

• Data privacy and security concerns related to income information.
• Potential for biases and discrimination based on income prediction models.
• Explainability and transparency of income prediction algorithms.
• The impact of income prediction on individual behavior and choices.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are series of supplemental questions asked during selected household visits. Topical modules were not created for the first or second waves of the 1985 panel. The topical module for Wave III contains information on assets and liabilities. Included are questions on loans, IRAs, medical bills, other debts, checking accounts, and savings bonds, as well as questions related to mortgages, royalties, and other investments, real estate property and vehicles, rental income, self-employment, and stocks and mutual fund shares. The Wave IV topical module contains information on fertility history, household relationships, marital history, migration history, support for non-household members, and work-related expenses. The topical module for Wave VI includes data on child care arrangements, child support agreements, support for non-household members, job offers, health status and utilization of health care services, long-term care, and disability status of children. Wave VII topical module contains information on assets and liabilities. Included are questions on pension plan coverage, lump sum distributions from pension plans, characteristics of job from which retired, and characteristics of home financing arrangements. Frequencies for each wave are also provided. Parts 27 and 28 of this study are the unedited research files for Wave V and Wave VIII Topical Modules, obtained from the Census Bureau. These files include data on annual income, retirement accounts, taxes, school enrollment, and financing. These two topical module files have not been edited nor imputed, although they have been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. 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 will be 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 Rico.Census tracts with no population 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.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2024 about households, median, income, and USA.

Income, consumption and wealth (ICW) statistics are experimental statistics computed by Eurostat through the statistical matching of three data sources: the EU Statistics on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These statistics enable us to observe at the same time the income that households receive, their expenditures and their accumulated wealth.

The annual collection of EU-SILC was launched in 2003 and is governed by Regulation 1700/2019 (previously: Regulation 1177/2003) of the European Parliament and of the Council. The EU-SILC collects cross-sectional and longitudinal information on income. HBS is a survey conducted every 5 years on the basis of an agreement between Eurostat, the Member States and EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. HFCS collects information on assets, liabilities, and to a limited extent income and consumption, of households. The survey is run by National Central Banks and coordinated by the European Central Bank.

This page focuses on the main issues of importance for the use and interpretation of ICW statistics. Information on the primary data sources can be found on the respective EU-SILC and HBS metadata pages and following the links provided in the sections 'related metadata' and 'annexes' below.

Experimental ICW statistics cover six topics: household economic resources, affordability of essential services, saving rates, poverty, household characteristics and taxation. Each topic contains several indicators with a number of different breakdowns, mainly by income quantile, by the age group of the household reference person, by household type, by the educational attainment level of the reference person, by the activity status of the reference person and by the degree of urbanization of the household. The indicators provide information on the joint distribution of income, consumption and wealth and the links between these three economic dimensions. They help to describe households' economic vulnerability and material well-being. They also help to explain the dynamics of wealth inequalities.

All indicators are to be understood to describe households, not persons. Breakdowns by age group, educational attainment level and activity status refer to the household reference person, which is the person with the highest income. The only exception are the tables icw_pov_01, icw_pov_10, icw_pov_11 and icw_pov_12 for which the income, consumption and wealth of households have been equivalised such that equal shares were attributed to each household member. Values in tables icw_aff are calculated for households reporting non-zero values only.

Note on table icw _res_01 and icw_res_02: The indicator “Households” [HH] in icw_res_01 shows the share of households in the selection, which hold the corresponding shares of total disposable income [INC_DISP], consumption expenditure [EXPN_CONS] and net wealth [WLTH_NET] of the entire population. In theory, turning two of the three dimensions [quant_inc, quant_expn, quant_wlth] to TOTAL and the third one to any quintile, should result into a share of 20% of households. Nevertheless, this share is often below or above 20% of the total population of households in the country. The reason for this is that our figures are based on sample surveys. This means that the share of households corresponds indeed to 20% of households in the sample, however when we multiply each household of the sample with its sampling weight, the resulting shares of households in the total population differ from the 20%. If, for example, we disregard the income and wealth of households in our sample, the first consumption quintile contains the 20% of households with lowest consumption in the sample. However, multiplying this selection of households with their corresponding sampling weights may result into a different share of the total population. The “Households” [HH] indicator indicates the real share of households in the population that make up the theoretical quintile.

Content

Marriage

This folder contains data behind the story Marriage Isn’t Dead — Yet.

Source for all data is Decennial Census (years 1960 to 2000) and American Community Survey (years 2001-2012), via IPUMS USA.

Except in the divorce file, figures represent share of the relevant population that has never been married (MARST == 6 in the IPUMS data). Note that in the story, charts generally show the share that have ever been married, which is simply 1 - n. In the divorce file, figures are share of the relevant population that is currently divorced, conditional on having ever been married.

Variable names are as follows. Number in variable names are age ranges, so all_2534 is the marriage rate for everyone ages 25 to 34.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

In 2024, about 44.7 percent of White households in the United States had an annual median income of over 100,000 U.S. dollars. By comparison, only 26.8 percent of Black households were in this income group. Asian Americans, on the other hand, had the highest median income per household that year.

Congo, Democratic Republic of Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 78.900 % in 2020. This records an increase from the previous number of 69.700 % for 2012. Congo, Democratic Republic of Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 78.900 % from Dec 2004 (Median) to 2020, with 3 observations. The data reached an all-time high of 91.500 % in 2004 and a record low of 69.700 % in 2012. Congo, Democratic Republic of Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Congo, Democratic Republic of – Table CD.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;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).