This statistic shows the share of people living in a multigenerational household in the United States between 1950 and 2016. In 2016, one fifth of Americans were living in a multigenerational household, which rose from only 12 percent in 1980.

Data on household type of private households and structural type of dwelling for private dwellings occupied by usual residents, Canada, provinces and territories, census metropolitan areas and census agglomerations, 2021, 2016 and 2011 censuses.

This statistic shows the number of people living in a multigenerational household in the United States in 2012 and 2016, by type. In 2016, **** million people were living in a multigenerational household with two adult generations, up from **** million in 2012.

This map shows the location of multi-generational households in the United States in 2010. A multigenerational household is a household in with three or more generations reside within a single household. This is shown by using color to represent the count of multigenerational households as a percentage of total households. The size of the symbols represent the count of all multigenerational households within an area.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Dependent children living in multi-generational family households and overcrowding of multi-generational family households.

This dataset provides Census 2021 estimates that classify households in England and Wales by number of multi-generational households by household tenure. The estimates are as at Census Day, 21 March 2021.

There is evidence of people incorrectly identifying their type of landlord as ”Council or local authority” or “Housing association”. You should add these two categories together when analysing data that uses this variable. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Multiple generations in household

Households where people from across more than two generations of the same family live together. This includes households with grandparents and grandchildren whether or not the intervening generation also live in the household.

Tenure of household

Whether a household owns or rents the accommodation that it occupies.

Owner-occupied accommodation can be:

• owned outright, which is where the household owns all of the accommodation
• with a mortgage or loan
• part-owned on a shared ownership scheme

Rented accommodation can be:

• private rented, for example, rented through a private landlord or letting agent
• social rented through a local council or housing association

This information is not available for household spaces with no usual residents.

Household low-income status using low-income measures (before and after tax) by household type (multigenerational, couple, lone parent, with and without children), age of members, number of earners, and year.

This dataset provides Census 2021 estimates that classify households in England and Wales by number of multi-generational households by bedroom occupancy rating. The estimates are as at Census Day, 21 March 2021.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Multiple generations in household

Households where people from across more than two generations of the same family live together. This includes households with grandparents and grandchildren whether or not the intervening generation also live in the household.

Occupancy rating for bedrooms

Whether a household's accommodation is overcrowded, ideally occupied or under-occupied. This is calculated by comparing the number of bedrooms the household requires to the number of available bedrooms.

The number of bedrooms the household requires is calculated according to the Bedroom Standard, where the following should have their own bedroom:

1. adult couple
2. any remaining adult (aged 21 years or over)
3. two males (aged 10 to 20 years)
4. one male (aged 10 to 20 years) and one male (aged 9 years or under), if there are an odd number of males aged 10-20
5. one male aged 10-20 if there are no males aged 0-9 to pair with him.
6. repeat steps 3-5 for females
7. two children (aged 9 years or under) regardless of sex
8. any remaining child (aged 9 years or under)

An occupancy rating of:

• -1 or less implies that a household’s accommodation has fewer bedrooms than required (overcrowded)
• +1 or more implies that a household’s accommodation has more bedrooms than required (under-occupied)
• 0 suggests that a household’s accommodation has an ideal number of bedrooms

Description of the INSPIRE Download Service (predefined Atom): Development plan “VEP Multigenerational Living Saarfelser Straße” of the district town of Merzig — The link(s) for downloading the records is/are generated dynamically from a DataURL link of a WMS layer

In 2024, 34.59 percent of all households in the United States were two person households. In 1970, this figure was at 28.92 percent. Single households Single mother households are usually the most common households with children under 18 years old found in the United States. As of 2021, the District of Columbia and North Dakota had the highest share of single-person households in the United States. Household size in the United States has decreased over the past century, due to customs and traditions changing. Families are typically more nuclear, whereas in the past, multigenerational households were more common. Furthermore, fertility rates have also decreased, meaning that women do not have as many children as they used to. Average households in Utah Out of all states in the U.S., Utah was reported to have the largest average household size. This predominately Mormon state has about three million inhabitants. The Church of the Latter-Day Saints, or Mormonism, plays a large role in Utah, and can contribute to the high birth rate and household size in Utah. The Church of Latter-Day Saints promotes having many children and tight-knit families. Furthermore, Utah has a relatively young population, due to Mormons typically marrying and starting large families younger than those in other states.

The China Multi-Generational Panel Dataset - Shuangcheng (CMGPD-SC) provides longitudinal individual, household, and community information on the demographic and socioeconomic characteristics of a resettled population living in Shuangcheng, a county in present-day Heilongjiang Province of Northeastern China, for the period from 1866 to 1913. The dataset includes some 1.3 million annual observations of over 100,000 unique individuals descended from families who were relocated to Shuangcheng in the early 19th century. These families were divided into 3 categories based on their place of origin: metropolitan bannermen, rural bannermen, and floating bannermen. The CMGPD-SC, like its Liaoning counterpart, the CMGPD-LN (ICPSR 27063), is a valuable data source for studying longitudinal as well as multi-generational social and demographic processes. The population categories had salient differences in social origins and land entitlements, and landholding data are available at a number of time periods, thus the CMGPD-SC is especially suitable to the study of stratification processes.

The LIFE-M project combines of U.S. vital records (birth, marriage, death certificates) with census information into longitudinal and intergenerational micro-data. Using cutting-edge, machine learning techniques, the resulting dataset consists of four generations and millions of high-quality links for 20th century Americans. For more details about the project, check out the website (https://life-m.org/). Additionally, these data can be linked to the LIFE-M Ohio Causes of Death Project (https://doi.org/10.3886/E149841).

We examine multi-generational impacts of positive in utero health interventions using a new research design that exploits sharp increases in prenatal Medicaid eligibility that occurred in some states. Our analyses are based on U.S. Vital Statistics Natality files, which enables linkages between individuals’ early life Medicaid exposure and the next generation’s health at birth. We find evidence that the health benefits associated with treated generations’ early life program exposure extend to later offspring. Our results suggest that the returns on early life health investments may be substantively underestimated.

This layer is symbolized to show the approximate percentage of households that are multigenerational households. Multigenerational households are households with three or more generations. These households include either (1) a householder, a parent or parent-in-law of the householder, and an own child of the householder, (2) a householder, an own child of the householder, and a grandchild of the householder, or (3) a householder, a parent or parent-in-law of the householder, an own child of the householder, and a grandchild of the householder. The householder is a person in whose name the home is owned, being bought, or rented, and who answers the survey questionnaire as person 1.Other fields included are estimates of mothers - females 18 to 64 with own children (biological, adopted, or step children) - by various race/ethnic groups, and by age group of children. Age groups were defined by the COVID vaccine age groups: 12 to 17, 5 to 11, and 0 to 4. We also included estimates for mothers of children in more than one of these groups.Data prep steps:Data downloaded on 4/5/22 from FTP site.All fields were calculated from the Census Bureau's 2016-2020 5-year American Community Survey Public Use Microdata Sample (PUMS) using this SAS program.Using the SAS-ArcGIS Bridge, the data table created in SAS was read into ArcGIS Pro and joined to this layer is PUMA, obtained from Living Atlas. According to the U.S. Census Bureau, a Public Use Micro-sample Area (PUMA) is a "non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each." The resulting layer in Pro was then published to ArcGIS Online.Disclaimer: All estimates here contain a margin of error. While they are not explicitly calculated and provided on this layer currently, we can and will add additional fields to provide the margins of error if the need arises.

A dataset of a survey of intergenerational relations among 2,044 adult members of some 300 three- (and later four-) generation California families: grandparents (then in their sixties), middle-aged parents (then in their early forties), grandchildren (then aged 16 to 26), and later the great-grandchildren as they turn age 16, and further surveys in 1985, 1988, 1991, 1994, 1997 and 2001. This first fully-elaborated generation-sequential design makes it possible to compare sets of parents and adult-children at the same age across different historical periods and addresses the following objectives: # To track life-course trajectories of family intergenerational solidarity and conflict over three decades of adulthood, and across successive generations of family members; # To identify how intergenerational solidarity, and conflict influence the well-being of family members throughout the adult life course and across successive generations; # To chart the effects of socio-historical change on families, intergenerational relationships, and individual life-course development during the past three decades; # To examine women''s roles and relationships in multigenerational families over 30 years of rapid change in the social trajectories of women''s lives. These data can extend understanding of the complex interplay among macro-social change, family functioning, and individual well-being over the adult life-course and across successive generations. Data Availability: Data from 1971-1997 are available through ICPSR as Study number 4076. * Dates of Study: 1971-2001 * Study Features: Longitudinal * Sample Size: ** 345 Three-generational families ** 2,044 Adults (1971 baseline) Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04076

ITW04 - Intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Intergenerational wealth transfers...

This is information on the current status of urban residential housing such as officetels (business facilities), multi-family houses, and multi-family houses located throughout Bucheon-si, Gyeonggi-do, and provides key information on the relevant buildings such as building name, location, purpose, building area, total floor area, land area, road name address, and number of floors. This data can contribute to the utilization of various building information by converting it into detailed data on the relevant housing type as the number of one- or two-person households such as single-person households and newlyweds increases. ※Please understand that the city does not manage the postal codes of officetels.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

Abstract (en): The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities. Possible applications of the dataset include the study of relationships between demographic behavior, family organization, and socioeconomic status across the life course and across generations, the influence of region and community on demographic outcomes, and development and assessment of quantitative methods for the analysis of complex longitudinal datasets. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Smallest Geographic Unit: Chinese banners (8) The data are from 725 surviving triennial registers from 29 distinct populations. Each of the 29 register series corresponded to a specific rural population concentrated in a small number of neighboring villages. These populations were affiliated with the Eight Banner civil and military administration that the Qing state used to govern northeast China as well as some other parts of the country. 16 of the 29 populations are regular bannermen. In these populations adult males had generous allocations of land from the state, and in return paid an annual fixed tax to the Imperial Household Agency, and provided to the Imperial Household Agency such home products as homespun fabric and preserved meat, and/or such forest products as mushrooms. In addition, as regular bannermen they were liable for military service as artisans and soldiers which, while in theory an obligation, was actually an important source of personal revenue and therefore a political privilege. 8 of the 29 populations are special duty banner populations. As in the regular banner population, the adult males in the special duty banner populations also enjoyed state allocated land free of rent. These adult males were also assigned to provide special services, including collecting honey, raising bees, fishing, picking cotton, and tanning and dyeing. The remaining populations were a diverse mixture of estate banner and servile populations. The populations covered by the registers, like much of the population of rural Liaoning in the eighteenth and nineteenth centuries, were mostly descendants of Han Chinese settlers who came from Shandong and other nearby provinces in the late seventeenth and early eighteenth centuries in response to an effort by the Chinese state to repopulate the region. 2016-09-06 2016-09-06 The Training Guide has been updated to version 3.60. Additionally, the Principal Investigator affiliation has been corrected, and cover sheets for all PDF documents have been revised.2014-07-10 Releasing new study level documentation that contains the tables found in the appendix of the Analytic dataset codebook.2014-06-10 The data and documentation have been updated following re-evaluation.2014-01-29 Fixing variable format issues. Some variables that were supposed to be s...

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in 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..An ''-'' entry in the estimate column indicates that 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..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2011-2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..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 roughly 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..Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates