Main tables from The Effects of Taxes and Benefits on Household Income publication for 1977 onwards.

Estimates of average weekday household person trips, vehicle trips, person miles traveled, and vehicle miles traveled (per day), for all Census tracts in the United States for 2009. For latest data (2017), see https://data.bts.gov/Research-and-Statistics/Local-Area-Transportation-Characteristics-by-House/va72-z8hz For methodology, see attachments

Data on living arrangements of persons in private households including stepfamily status and presence of grandparents, age group and gender, Canada, provinces and territories, census metropolitan areas and census agglomerations, 2021 and 2016 censuses.

tenure trends at national and regional levels; cross-tenure comparisons of characteristics of households and their accommodation; overcrowding and under-occupation; need for specially adapted accommodation. the demographic and economic characteristics of renters; accommodation characteristics; rents and Housing Benefit; types of letting. trends in ownership; types of purchase; recent first-time buyers; types of mortgage; mortgage payments; leaseholders; moves out of owner occupation; second homes. mobility among all households; length of residence; demographic characteristics of movers; movement between tenures; movement into and out of tenures; tenancy deposits.

This table contains figures on the satisfaction with the current dwelling and the living environment of private households in independent homes. The figures are presented for both owners and tenants and can be further divided into various characteristics of the household and the region. Figures at the municipal level are only provided for municipalities that had 100,000 inhabitants or more in 2018.

Data available from: 2002

Status of the figures: final

Changes as of 10 April 2025: Final figures for 2024 included.

Statistics Netherlands is switching to a new classification regarding migration background. From now on, it will be primarily decisive where someone was born, and additionally where his/her parents were born. The term migration background will no longer be used. The main classification Western/non-Western will be replaced by a classification based on continents and common immigration countries. This classification will be gradually introduced in tables and publications with population by origin. For this StatLine table, it has been decided that the classification of migration background will be stopped. As of reporting year 2024, the figures regarding migration background in this table will no longer be updated.

When will new figures be published? Figures over reporting year 2027 will be published in 2028.

Main tables from The Effects of Taxes and Benefits on Household Income publication for 1977 onwards.

Household characteristics, including household type, age group of the reference person (the person responsible for housing decisions), employment status of the reference person, visible minority status of the reference person, and degree of difficulty for the household to meet its financial needs, by tenure including first-time homebuyer status, Canada, provinces, population centres, select census metropolitan areas (CMAs) and census agglomerations (CAs).

Survey of Household Spending (SHS), dwelling characteristics and household equipment, percentage of households reporting and estimated number of households reporting.

Distribution of household total income in constant 2020 dollars by household type (couple family, one-parent family, non-census family households) and characteristics of household members (number of earners and number of people in different age groups).

Dataset derived from existing cross-sectional socio-economic data collected across marginalised rural areas throughout 31 states in Mexico between 1996 and 1999, the Survey of Household Socio-Economic Characteristics (Encuesta de Caracteristicas Socioeconomicas de los Hogares, ENCASEH). ENCASEH had been conducted to aid in the targeting of the PROGRESA welfare programme, collected data from all households in these communities and contains a rich cross-section of information on individual and household characteristics, along with locality data. The current dataset includes households with at least one 12-17 year old. This research will provide evidence for rural Mexico on whether smaller family sizes increase children's school enrolment and decrease children's work participation, which affect children's human capital accumulation and thus long-term poverty. All too often, policies to promote lower families in order to increase human capital accumulation are based on observed negative relationships between family size and education (parents with large families tend to invest less in education). These relationships can be misleading, because differences in education outcomes between large and small families may be for reasons other than family size. Pinning down whether or not whether family size actually affects education is important for formulating appropriate and effective policies in relation to family planning. To test whether family size has a causal effect on outcomes, one needs access to random variation in the propensity to have more children. Such variation is often provided by natural experiments. In this research, two natural experiments will be used - having twins in the second birth, and having children of the same sex in the first two births - both of which provide sources of random variation in family size. Dataset derived from Survey of Household Socio-Economic Characteristics (Encuesta de Caracteristicas Socioeconomicas de los Hogares, ENCASEH), 1996-199, a household survey targeting all households in marginalised rural villages in Mexico, 1996-1999. Sample of households with at least one 12-17 year old child. The dataset contains 225 variables, including socio-economic characteristics of households and characteristics of localities where they reside. Total of 550,163 unique households and 955,057 unique individuals.

This table contains data on household income, broken down by region and various background characteristics of the household and of the main breadwinner. The population consists of all private households with known income, where student households can be optionally excluded. The reference date for the population is January 1 of the year under review, and the reference date for the municipal subdivision is January 1, 2017. Data available from 2011 to 2015 inclusive. Status of the figures: The figures in this table are final. Changes as of November 12, 2018: No longer applicable. This table is followed by the table 'Income households; characteristics, region (2018 classification)'. See section 3. When will new figures be released? Not applicable anymore.

Distribution of household after-tax income in constant 2020 dollars by household type (couple family, one-parent family, non-census family households) and characteristics of household members (number of earners and number of people in different age groups).

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.

Survey of Household Spending (SHS), dwelling characteristics and household equipment, percentage of households reporting and estimated number of households reporting.

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 Documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

This table contains figures on the housing costs of private households in independent homes. Households living (temporarily) in a house free of charge are not included. The figures are presented for both owners and tenants and can be further divided into various characteristics of the household and the dwelling. Data available as of year: 2009 Status of the figures: final. Changes as of 4 April 2019: None, this table was stopped. When will new figures be published? This table is stopped. This table is stopped as a consequence of a revision of the income data in 2015. The housing costs are based on this income data. Therefore it is no longer possible to determine the housing costs for WoON 2018 in the same way as before. Consequently the housing costs for WoON 2012 and 2015 have also been revised. For WoON 2009 this however was not possible, since 2011 was the last year of the revision. Subsequently the housing costs for WoON 2012, 2015 and 2018 are included in the new table Housing costs of households; dwelling characteristics, region. See the link in paragraph 3.

This series of cross-tabulations will present a portrait of Canada based on various census topics. They will range in complexity and will be available for various levels of geography. A number of the tabulations will be available on the day of release for each topic, while others will follow several months later. Content varies form a simple overview of the country, then move to more complex cross-tabulations and will include current and previous census data.

Proportion of the population living: in a dwelling owned by some members of the household; in core housing need and; in suitable dwelling, proportion of the population living alone, poverty rate (MBM), prevalence of low income (LIM-AT) and (LIM-BT), knowledge of official languages, by visible minority and selected characteristics (gender, age group, first official language spoken, immigrant status, period of immigration, generation status and highest certificate, degree or diploma).

Survey on Equipment and Use of Information and Communication Technologies in Households: Summary of data on Dwellings by absolute value/percentage, characteristics of the household and type of equipment. National.

Key characteristics of survey households and household members.