This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows children in grandparent households by age. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only children in households in which the grandparent is the householder are included here. This is different from children in multigenerational households. This layer is symbolized to show the percentage of children who are in the care of grandparents. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B10001, B10002, B09001 (Not all lines of ACS table B09001 are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National 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. For more information about ACS layers, visit the FAQ. 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) 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 RicoCensus tracts with no population that occur in areas of water, such as oceans, 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., -4444...) have been set to null, with the exception of -5555... which has been set to zero. 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.

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.

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...

Urban and regional planners rely on Average Household Size as a foundational indicator for many of their models, calculations, and plans. Average household size (also known as "people per household") is a reflection of many dynamics at play, for example:Age of the population, as many older people tend to live in smaller households (one-person or two-person households)Housing prices in the area, proximity to colleges and universities, and how likely people are to live with roommatesFamily norms and traditions (e.g., multigenerational families are more common in some areas and with some population groups)This feature layer contains the Average Household Size and Population Density for states, counties, and tracts. Data from U.S. Census Bureau's 2014-2018 American Community Survey's 5-year estimates, Tables B25010 and B01001. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. See the field description for the formula used.This layer is symbolized to show the average household size. Population density, as well as average household size breakdown by housing tenure is presented in the pop-up. Click the Data tab -> Fields list to see all available attributes and their definitions.

In 2023, there were close to 65.1 thousand new condominium units in Japan. This constituted a decrease from around 73 thousand units in the previous year. In the past decade, the supply of new condominium units was the highest in 2014 at 83.2 thousand. The history of condominiums in Japan During the period of rapid economic growth in the 1960s, public housing apartment buildings made of concrete, called Danchi, gained increasing popularity in Japan. This also meant a shift away from multi-generational households to nuclear family households. As a part of this development, construction companies started to build the first condominiums, followed by the first condominium boom in the mid-1960s. Today, the number of residential condominiums in Japan is still growing. The condominium market in Japan Greater Tokyo and Greater Osaka together accounted for almost two-thirds of the supply of new condominiums in Japan. Condominium prices have been on an upward trend recently. The average price per square meter of new apartment units was the highest in the Greater Tokyo Area. Amid rising property prices across Japan, pre-owned condominiums also saw a price increase.

ITW03 - Intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Intergenerational wealth transfers...

IntroductionThe new rural pension scheme (NRPS) can improve the quality of life for rural older adult individuals; however, can it have a spillover effect on rural household human capital investments through intergenerational interactions?MethodsBased on data from the China Family Panel Studies (CFPS) in 2010, 2012, 2014, 2016, and 2018 and from the perspective of intergenerational interactions, the spillover effect and influencing mechanism of the new rural insurance policy on rural household human capital investments are empirically tested.ResultsThe results show that the participation of families in the new rural insurance policy can significantly promote the human capital investments of rural families, and they are robust. Moreover, the spillover effect of this new policy is significantly different due to the gender, insurance phase, and family income of the insured. Through intergenerational interactions, the new rural insurance policy has an impact on the human capital investments of rural families from the material level of intergenerational economic support, housework and childcare for children and the nonmaterial level of old-age care cognition.DiscussionTherefore, continuing to promote the coverage of the new rural insurance policy and scientifically improving rural social security through publicity and education to promote benign intergenerational family interactions can improve the accumulation of human capital in rural areas.