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 ** percent in 1980.

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

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.

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 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.

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.

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

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.

Abstract This article aims at getting to know the impact of longevity on the multigenerational family, and it is a result of the survey "The multigenerational family in Caxias do Sul", carried out in the Working Group on Aging of the University of Caxias do Sul, from 2004 to 2006. Sampling consisted of 27 multigenerational families in the municipality of Caxias do Sul, RS, Brazil, with the participation of three people from different generations of the same family, representing 81 people who make up an intentional, oriented and convenient sampling. Methodology used was content analysis and dialectical approach. Survey results intend to contribute to the discussion of the problems the family faces when living with elderly people, and the difficulties the latter have in living together with their family. To know these challenges and propose alternatives to face them in order to qualify people's relationships individually and in the family itself as an institution is a necessity regarded as relevant.

Most intergenerational mobility studies rely on either snapshot or time-averaged measures of earnings, but have yet to examine resemblance of earnings trajectories over the life course of successive generations. We propose a linked trajectory mobility approach that decomposes the progression of economic status over two generations into associations in four life-cycle dimensions: initial position, growth rate, growth deceleration, and volatility. Using father-son dyad data from the Panel Study of Income Dynamics, we show that men resemble their fathers not only in the overall level of earnings but also in the pattern by which their earnings develop over time. The intergenerational persistence of earnings varies substantially across life stages of both generations; it is strongest for fathers’ early-career and sons’ mid-career, with an intergenerational elasticity (IGE) as high as .6. This result can be explained by the concurrence of the parent’s early career and the offspring’s early childhood. Our findings suggest the intergenerational economic association between parents and offspring is not age-constant but is contingent on the respective life stages of both generations and, most importantly, the period during which they overlap.

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 unique multigenerational data base, Uppsala Birth Cohort Multigeneration Study (UBCoS), was established in 2004 by combining existing data on a representative and well-defined cohort of 14,192 males and females born in Uppsala from 1915-1929 with information on descendants of the original cohort members obtained from routine data registers.

To date, the study has been further developed by additional data collection in school archives and records from Census 1930 and the period of follow-up extended till end of year 2010. Further data collection is currently ongoing.

The study is unique in investigating intergenerational effects as "forward in time" processes, starting at the beginning of the last century (i.e. well before any of the routine registers were in place). Intergenerational associations can be currently investigated in more than 140,000 study subjects from families spanning up to five generations, including the 14,192 original cohort members, their 22,559 children, 38,771 grandchildren and 25,471 great grandchildren.

The main research objectives are to: (i) Address questions of the extent to which and the mechanisms whereby social advantage and disadvantage are transmitted from one generation to the next, giving rise to continuity in social disadvantage both over the life cycle and across generations. (ii) Explore how early social and biological factors are transmitted from the parent generation to offspring generation(s). (iii) Integrate the understanding of broader social mechanisms with the understanding of disease specific aetiology to answer the question of how, and to what extent, health inequalities are reproduced into each new generation.

Purpose:

The aim of the study is to investigate life course and intergenerational determinants of social inequalities in health.

Number of participants: 14,192 original cohort together with >140,000 family members.

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.

Acute exposure to warming temperatures increases minimum energetic requirements in ectotherms. However, over and within multiple generations, increased temperatures may cause plastic and evolved changes that modify the temperature sensitivity of energy demand and alter individual behaviours. Here, we aimed to test whether populations recently exposed to geothermally elevated temperatures express an altered temperature sensitivity of metabolism and behaviour. We expected that long-term exposure to warming would moderate metabolic rate, reducing the temperature sensitivity of metabolism, with concomitant reductions in boldness and activity. We compared the temperature sensitivity of metabolic rate (acclimation at 20 versus 30°C) and allometric slopes of routine, standard, and maximum metabolic rates, in addition to boldness and activity behaviours, across eight recently divergent populations of a widespread fish species (Gambusia affinis). Our data reveal that warm-source populations express a reduced temperature sensitivity of metabolism, with relatively high metabolic rates at cool acclimation temperatures and relatively low metabolic rates at warm acclimation temperatures compared to ambient-source populations. Allometric scaling of metabolism did not differ with thermal history. Across individuals from all populations combined, higher metabolic rates were associated with higher activity rates at 20°C and bolder behaviour at 30°C. However, warm-source populations displayed relatively bolder behaviour at both acclimation temperatures compared to ambient-source populations, despite their relatively low metabolic rates at warm acclimation temperatures. Overall, our data suggest that in response to warming, multigenerational exposure (e.g., plasticity, adaptation) may not result in trait change directed along a simple “pace-of-life syndrome” axis, instead causing relative decreases in metabolism and increases in boldness. Ultimately, our data suggest that multigenerational warming may produce a novel combination of physiological and behavioural traits, with consequences for animal performance in a warming world. Methods Fish acclimation
Fish from eight populations were acclimated into 16 20L tanks (2 tanks per population) in the laboratory, with each tank containing fish from a single population. We randomly allocated ~12 fish from a population to a tank (n = 198 fish total; details in S1). In each tank, we separated males and females using dividers to minimise sexually antagonistic interactions that can affect survival; however, mosquitofish females store sperm, so most females were pregnant during the time of trait measurements, as they would be in nature. Each population was acclimated to two experimental temperatures (20 ± 0.5 and 30 ± 0.5°C) over four months. Tank temperatures were initially set to the collection temperature for a given population and then adjusted by increasing or decreasing the set temperature of aquarium heaters by a maximum of 1°C every two days until the target temperature was reached. We started with water from the appropriate field site in each aquarium combined with treated tap water to remove chlorine (API Stress Coat) and progressively replaced it with treated water over two weeks. We fed fish twice daily by hand to satiation with freeze-dried Daphnia and Nutrafin MAX small tropical fish micro-granules and maintained a light cycle of 12:12 throughout the experiment. Each aquarium had artificial macrophytes and stones to provide refuge. Water was continuously filtered using sponge air filters, which we cleaned every second day. Fish mortality was low in most populations (see Table S1). We fasted individuals for 24 hours before measuring behavioural and metabolic traits to control for food digestion. Metabolic Rate We measured metabolism as maximum metabolic rate (MMR), standard metabolic rate (SMR), and routine metabolic rate (RMR). MMR is the maximum metabolic rate of an individual and sets the upper limit on organismal metabolic performance (Fry, 1971). In contrast, SMR is the minimum metabolic rate, measured after rest, with no digestion cost, on non-stressed fish and sets the lower requirement of an animal to sustain life. RMR was measured under similar conditions as SMR but allowed for some activity and sits between SMR and MMR. As RMR incorporates variation in activity between individuals, it may closely relate to behavioural traits (Mathot & Dingemanse, 2015). We measured RMR and MMR using static respirometry and SMR using intermittent flow-through respirometry at each fish’s acclimation temperature (Steffensen, 1989; Clark, Sandblom, & Jutfelt, 2013). We used respirometers comprising 40 mL acrylic chambers with magnetic stir bars in the chamber base to ensure water mixing throughout our oxygen measures in all assays. We measured metabolic rate as oxygen consumption (MO2) using a FireSting four-channel oxygen logger with optical oxygen sensors (PyroScience, Germany). Respirometers were placed into 80 L aquaria, filled with treated tap water, fitted with a UV filtration system, an aerator, and a 100W aquarium heater. Immediately following behavioural trials (see below), we measured RMR by placing individuals into chambers and measuring oxygen consumption over 15 minutes. Chambers were then connected to a recirculating pump and slowly flushed with oxygenated water for five minutes before beginning SMR measurements. Oxygen consumption measurements for SMR were taken overnight over an approximately 18-hour period. A computer-controlled aquarium pump intermittently flushed chambers for five minutes to ensure a complete turnover of water inside the chambers, then an oxygen measurement period of 15 minutes began after a 30 second wait period. We controlled oxygen flow and data logging through a PC using the software ‘AquaResp’ (Svendsen, 2017). Following SMR measurements, we measured MMR using an exhaustive chase protocol to induce maximum oxygen consumption (Clark et al., 2013; Norin & Clark, 2016). Fish were removed from chambers one by one and placed into a circular tank; in this tank, we used an aquarium net to chase the fish until exhaustion (defined as the lack of ability for burst swimming) (Norin & Clark, 2016). Fish were then immediately placed into a static respirometer, and oxygen consumption was measured for 5 minutes. We chose to measure MMR after SMR measurement to ensure our SMR measurement accuracy as metabolic rates may remain elevated for long periods after exhaustive exercise. We immediately euthanised the fish following the measurement of MMR using clove oil. Fish were then measured for mass, length, sex, and volume, then dried at 60°C for 48 hours and re-weighed for dry mass. We controlled for microbial oxygen consumption in our metabolism assay water by subtracting the oxygen consumption in blanks (respirometers with water only), which were run before and after every trial. We assumed a linear increase in microbial oxygen consumption between measurements in blanks. We calculated each SMR, MMR, and RMR as; 〖MO〗_2=(V_r-V_f )×〖ΔC〗_wO2/Δt
Where: MO2 is oxygen consumption rate, Vr is respirometer volume, Vf is fish volume, ΔCwO2 is the change in oxygen concentration, Δt is the change in time. We calculated SMR using the mean of the lowest 10 % of all measurements, excluding any outliers (± two standard deviations [SD] from the mean), aerobic scope as MMR-SMR, and factorial aerobic scope as MMR/SMR (Clark et al., 2013; Chabot, Steffensen, & Farrell, 2016). Behaviour Immediately before measuring metabolism, we conducted behavioural assays on individuals in a 60 L aquarium with a water depth of 20 cm and temperature set to the acclimation temperature. We fit the aquarium with an air pump and a UV filtration system to maintain high oxygen saturation and control microbial respiration. We measured individual’ boldness’ as latency to exit a refuge and individual ’‘activity' as time spent exploring a novel environment (Cote et al., 2010; Wilson, Godin, & Ward, 2010). For these behavioural measures, we placed individuals into a small enclosed and darkened area (‘refuge,’ 10cm × 30cm) at one end of the 60 L aquarium. The aquarium was covered on all but one side to allow for observation. In the refuge, we provided artificial macrophytes and river stones. Fish were left in the refuge for 10 minutes before a 4 × 4 cm door was opened remotely, allowing fish to exit and explore the remainder of the tank (‘open area’). In the open area, we placed macrophytes opposite the refuge opening as a visual cue for exploration. We measured boldness using a stopwatch as the time it took the fish to leave the refuge. Fish that did not leave were assigned a maximum latency time of 600 seconds and were not measured for activity as forced tests may measure anxiety or fear traits (Brown, Burgess, & Braithwaite, 2007). Once the fish began exploring, we video-recorded their movement and later measured activity as time spent moving (versus remaining stationary) over five minutes following their emergence from the refuge

IntroductionBased on questions about impairments and activity limitations, the American Community Survey shows that roughly 13% of the U.S. population is experiencing disability. As most people live in households with other persons, this study explores disability at the household level. Considering the literature on household decision-making, solidarity, and capabilities in disability, this analysis of the household context of disability takes into account residential settings, household composition, and urban–rural differences.MethodThe 2015–2019 ACS Public Use Microdata Sample (PUMS), which shows persons with disability (PwD) and persons without disability (PwoD), also indicates household membership, used here to separately identify PwoD as those living in households with persons with disability (PwoD_HHwD) and those in households without any household member with disability (PwoD_HHwoD). Relationship variables reveal the composition of households with and without disabilities. An adaption of Beale's rural–urban continuum code for counties is used to approximate rural–urban differences with ACS PUMS data.ResultsSolo living is two times as common among persons with disability than among persons without disability, and higher in rural than urban areas. In addition to 43 million PwD, there are another 42 million PwoD_HHwD. Two times as many persons are impacted by disability, either of their own or that of a household member, than shown by an analysis of individual-level disability. For family households, differences in the composition of households with and without disabilities are considerable with much greater complexities in the makeup of families with disability. The presence of multiple generations stands out. Adult sons or daughters without disability play an important role. Modest urban–rural differences exist in the composition of family households with disability, with a greater presence of multigenerational households in large cities.DiscussionThis research reveals the much wider scope of household-level disability than indicated by disability of individuals alone. The greater complexity and multigenerational makeup of households with disability imply intergenerational solidarity, reciprocity, and resource sharing. Household members without disability may add to the capabilities of persons with disabilities. For the sizeable share of PwD living solo, there is concern about their needs being met.

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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...

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).

There has been strong interest in intergenerational arts practice in the United Kingdom since the 1980s; however, there is a generally weak evidence base for the effectiveness of intergenerational practice regardless of the domain. The aim of this study was to investigate the outcomes of an intergenerational arts project on participants’ social and psychological well-being using a mixed-methods, short-term longitudinal design. Generations Dancing brought together community artists with students (n = 25) and older adults (n = 11) living in Bedford. Over an 11-week period, participants worked together to produce a new dance performance and photography exhibition. Focus groups were conducted with the participants to explore their feelings about the collaboration across generations and communities. Participants also completed a battery of questionnaires preproject and postproject, to assess any change in their levels of well-being. Results indicate that the older adults showed increased confidence and willingness to connect with others; they got immense enjoyment from talking about their experience with others. Furthermore, the project helped to address negative stereotypes that the older adults had of working with the young people. The older adults enjoyed the students’ company and felt encouraged and supported by the young people. While a small number of challenges were identified, including difficulties in traveling to the workshops for vulnerable participants, most challenges were overcome through the older adults’ engagement in the project. For example, initial anxieties regarding the performance seemed insufficient to affect the participants’ overall enjoyment of the project. The findings were supported by the increased scores in relatedness, affect, and social well-being over time, but were not statistically significant. The results of this study indicate that intergenerational dance and arts projects can have wide-reaching positive impacts on both social and psychological well-being. However, there were a number of methodological challenges, including difficulty in recruiting sufficient numbers of both experimental and control groups for a robust quantitative evaluation of the data. These challenges highlight that “real life” settings and scenarios can influence the amount, nature, validity, and reliability of data collected. Going forward we encourage researchers to continue to consider innovative ways to address such methodological challenges.