The majority of people in Japan associated the family household with a place for family gatherings and peace of mind, according to a survey conducted between October and November 2022. Over 63 percent of respondents considered family a place to gather with family members, while 62 percent said it was a place for relaxation.

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

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

Abstract

The 2003 Family Income and Expenditure Survey (FIES) had the following primary objectives:

1) to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines;

2) to determine the sources of income and income distribution, levels of living and spending patterns, and the degree of inequality among families;

3) to provide benchmark information to update weights for the estimation of consumer price index; and

4) to provide information for the estimation of the country's poverty threshold and incidence.

Geographic coverage

The 2003 MS considers the country’s 17 administrative regions as defined in Executive Orders (EO) 36 and 131 as the sampling domains. A domain is referred to as a subdivision of the country for which estimates with adequate level of precision are generated. It must be noted that while there is demand for data at the provincial level (and to some extent municipal and barangay levels), the provinces were not treated as sampling domains because there are more than 80 provinces which would entail a large resource requirement. Below are the 17 administrative regions of the country: National Capital Region Cordillera Administrative Region Region I - Ilocos Region II – Cagayan Valley Region III – Central Luzon Region IVA – CALABARZON Region IVB – MIMAROPA Region V – Bicol Region VI – Western Visayas Region VII - Central Visayas Region VIII - Eastern Visayas Region IX - Zamboanga Peninsula Region X - Northern Mindanao Region XI - Davao Region XII - SOCCSKSARGEN Region XIII - Caraga Autonomous Region in Muslim Mindanao

Analysis unit

The reporting unit was the household which implied that the statistics emanating from this survey referred to the characteristics of the population residing in private households. Institutional population is not within the scope of the survey.

For FIES, the concept of family was used. A family consists of the household head, spouse, unmarried children, ever-married children, son-in-law/daughter-in-law, parents of the head/spouse and other relatives who are members of the household.

In addition, two or more persons not related to each other by blood, marriage or adoption are also considered in this survey. However, only the income and expenditure of the member who is considered as the household head are included.

Universe

The survey involved the interview of a national sample of about 51,000 sample households deemed sufficient to provide reliable estimates of income and expenditure at the national and regional level.

The 2003 FIES has as its target population, all households and members of households nationwide. A household is defined as an aggregate of persons, generally but not necessarily bound by ties of kinship, who live together under the same roof and eat together or share in common the household food. Household membership comprises the head of the household, relatives living with him such as his/her spouse, children, parent, brother/sister, son-in-law/daughter-in-law, grandson/granddaughter and other relatives. Household membership likewise includes boarders, domestic helpers and non-relatives. A person who lives alone is considered a separate household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2003 MS considers the country's 17 administrative regions as defined in Executive Orders (EO) 36 and 131 as the sampling domains. A domain is referred to as a subdivision of the country for which estimates with adequate level of precision are generated. It must be noted that while there is demand for data at the provincial level (and to some extent municipal and barangay levels), the provinces were not treated as sampling domains because there are more than 80 provinces which would entail a large resource requirement. Below are the 17 administrative regions of the country:

National Capital Region Cordillera Administrative Region Region I - Ilocos Region II - Cagayan Valley Region III - Central Luzon Region IVA - CALABARZON Region IVB - MIMAROPA Region V - Bicol Region VI - Western Visayas Region VII - Central Visayas Region VIII - Eastern Visayas Region IX - Zamboanga Peninsula Region X - Northern Mindanao Region XI - Davao Region XII - SOCCSKSARGEN Region XIII - Caraga Autonomous Region in Muslim Mindanao

As in most household surveys, the 2003 MS made use of an area sample design. For this purpose, the Enumeration Area Reference File (EARF) of the 2000 Census of Population and Housing (CPH) was utilized as sampling frame. The EARF contains the number of households by enumeration area (EA) in each barangay.

This frame was used to form the primary sampling units (PSUs). With consideration of the period for which the 2003 MS will be in use, the PSUs were formed/defined as a barangay or a combination of barangays with at least 500 households.

The 2003 MS considers the 17 regions of the country as the primary strata. Within each region, further stratification was performed using geographic groupings such as provinces, highly urbanized cities (HUCs), and independent component cities (ICCs). Within each of these substrata formed within regions, the PSUs were further stratified, to the extent possible, using the proportion of strong houses (PSTRONG), indicator of engagement in agriculture of the area (AGRI), and a measure of per capita income (PERCAPITA) as stratification factors.

The 2003 MS consists of a sample of 2,835 PSUs. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the total PSUs; a half sample contains one-half of the four sub-samples or equivalent to all PSUs in two replicates.

The final number of sample PSUs for each domain was determined by first classifying PSUs as either self-representing (SR) or non-self-representing (NSR). In addition, to facilitate the selection of sub-samples, the total number of NSR PSUs in each region was adjusted to make it a multiple of 4.

SR PSUs refers to a very large PSU in the region/domain with a selection probability of approximately 1 or higher and is outright included in the MS; it is properly treated as a stratum; also known as certainty PSU. NSR PSUs refers to a regular too small sized PSU in a region/domain; also known as non certainty PSU. The 2003 MS consists of 330 certainty PSUs and 2,505 non-certainty PSUs.

To have some control over the sub-sample size, the PSUs were selected with probability proportional to some estimated measure of size. The size measure refers to the total number of households from the 2000 CPH. Because of the wide variation in PSU sizes, PSUs with selection probabilities greater than 1 were identified and were included in the sample as certainty selections.

At the second stage, enumeration areas (EAs) were selected within sampled PSUs, and at the third stage, housing units were selected within sampled EAs. Generally, all households in sampled housing units were enumerated, except for few cases when the number of households in a housing unit exceeds three. In which case, a sample of three households in a sampled housing unit was selected at random with equal probability.

An EA is defined as an area with discernable boundaries within barangays consisting of about 150 contiguous households. These EAs were identified during the 2000 CPH. A housing unit, on the other hand, is a structurally separate and independent place of abode which, by the way it has been constructed, converted, or arranged, is intended for habitation by a household.

The 2003 FIES involved the interview of a national sample of about 51,000 sample households deemed sufficient to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines at the national and regional level. The sample households covered in the survey were the same households interviewed in the July 2003 and January 2004 round of the LFS.

Mode of data collection

face to face interview

Research instrument

Refer to the attached 2003 FIES questionnaire in pdf file (External Resources)

Cleaning operations

The 2003 FIES questionnaire contains about 800 data items and a summary for comparing income and expenditures. The questionnaires were subjected to a rigorous manual and machine edit checks for completeness, arithmetic accuracy, range validity and internal consistency.

The major steps in the machine processing are as follows: 1. Data Entry 2. Completeness Check 3. Matching of visit records 4. Consistency and Macro Edit (Big Edit) 5. Generation of the Public Use File 6. Tabulation

Steps 1 to 2 were done right after each visit. The remaining steps were carried out only after the second visit had been completed.

Steps 1 to 4 were done at the Regional Office while Steps 5 and 6 were completed in the Central Office.

After completing Steps 1 to 4, data files were transmitted to the Central Office where a summary file was generated. The summary file was used to produce the consistency tables as well as the preliminary and textual tables.

When the generated tables showed inconsistencies, selected data items were subjected to further scrutiny and validation. The cycle of generation of consistency tables and data validation were done until questionable data items were verified.

The FAME (FIES computer-Aided Consistency and Macro Editing), an interactive Windows-based application system was used in data processing. This system was used starting with the 2000 FIES round. The interactive module of FAME enabled the following

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

Abstract

The 1993 KMPS was carried out under the direction of researchers from the University of North Carolina at Chapel Hill, Paragon Research International, Inc., and the Institute of Sociology of the Russian Academy of Sciences.3 The government of the Kyrgyz Republic has recently established an open access policy in regards to the data collected in the KMPS (for details, see appendix A). The potential uses of this data set are quite broad given the multi-topic nature of the data and the fact that it was carried out at the national level.

The purpose of this paper is to provide detailed documentation of the KMPS in order to:

a) simplify its use for potential users thereby lowering start-up costs to analysts; b) ensure that the procedures used in the design, implementation and initial analysis of the survey are chronicled accurately.

Such documentation will serve both to facilitate use of the data set and to prevent misuse of the data due to misunderstandings of the sample and/or field work procedures.

Geographic coverage

The whole country.

Analysis unit

• Individuals
• Households
• Municipals

Universe

In this study, "household" was defined as a group of people who live together in a given domicile, who keep house together, and share common income and expenditures. Judging from the 1989 census, there were about 856'000 families containing 4'258'000 individuals living in Kyrgyzstan at that time and an average of about five members per family. The questionnaires are address to:

• Family in the households about their income and expenditures, and the health of all its members;
• Adult of the household from14 years and older, preferably privately;
• Children under the age of 14 years;
• Community abouts basic information on community services, infrastructure and economic structure;

Kind of data

Sample survey data [ssd]

Sampling procedure

According to the 1989 Census, there were about 856,000 families and 4,258,000 individuals living in the Kyrgyz Republic at that time (an average of about five members per family). Though the definition of 'household' used in the KMPS differs from the Census definition of 'family', this figure provided an estimate of the number of households from which the sample was to be drawn. Note that the sampling methodology assumes that any growth in the number of households since 1989 was equally distributed across regions. The target household sample size was 2,000. To allow for an estimated non-response rate of about five percent, a sample of 2,100 households was drawn. The actual number of completed household interviews was 1,938, reflecting a non response rate of 7.7 per cent. The response rate for individuals is more difficult to calculate, since some household members (eg. students under 18 studying elsewhere) could not be interviewed.

Sampling deviation

The sample is designed to be fully representative of all households in the Kyrgyz Republic in the second half of 1993. Stratification was based on information on the population provided in the 1989 Census (since results from the 1994 microcensus were not available at the time of the survey). A stratified, multi-stage sampling procedure was used, with the number of stages dependent on whether households were being drawn from urban or rural areas.13 The following is a brief description of the sampling process (summarized in table below).

                          Stages of the sampling process
                                            Non self-representing strata

Stage Self-representing strata Urban areas Rural areas 1st microcensus enumeration urban settlements rural settlements districts (cities) (villages) 2nd households microcensus household enumeration districts 3rd household

Mode of data collection

Face-to-face [f2f]

Research instrument

Explanation of the five questionnaires of this study:

• The household questionnaire was administered to the person who best knew the business and concerns of the family, its income and expenditures, and the health of all its members. This respondent may not necessarily have been the head of the household, however the household questionnaire was not to be administered to a child.
• The adult individual questionnaire was administered personally to every member of the household 14 years and older, preferably privately. Interviewers were not permitted to fill out an adult questionnaire based on answers provided by another member of the household.
• The child individual questionnaire was completed for every member of the household under the age of 14 years. The questionnaire was administered to the adult member of the household who was responsible for caring for the child.
• The "Survey of Availability and Prices of Food Products and Fue"l contains three sections of information relating to retail outlets7 selling food products in the 'local area'8 of the households participating in the survey.
• Basic information on community services, infrastructure and economic structure were collected in this survey. A community, or 'immediate place of residence' is defined as the microcensus enumeration district in urban areas and the settlement (village) in rural areas. The survey includes preliminary identification data and details the raion, settlement identifier, microcensus enumeration district, date of survey and also the name of the person conducting the survey.

Cleaning operations

• The local supervisors were required to examine the questionnaires to locate problems which could be remedied in the field. Such problems included missing key demographic information and problem with household and individual identification numbers. All questionnaires were then sent to Bishkek, where they were again checked for identification number problems and then to Moscow, where yet another ID check was performed.

• Open-ended questions (eg. occupation and nationality questions) were not immediately coded. Instead, the responses were entered into the data set in text, to be coded at a later date. Codes for all open-ended questions except occupation were made available in midFebruary. Occupation codes were made available in June 1994.

• Data entry and verification of the household questionnaires was completed by a private data entry firm by January 25. All other data entry was handled in-house using the SPSS data program. The first entry of the 10,000 child and adult questionnaires began on December 20, 1993; the verification pass began on January 20 and was completed by February 2. Entry of the community and price surveys began in late January and was completed in two weeks.

Response rate

To allow for an estimated non-response rate of about five percent, a sample of 2,100 households was drawn. The actual number of completed household interviews was 1,938, reflecting a non response rate of 7.7 per cent. The response rate for individuals is more difficult to calculate, since some household members (eg. students under 18 studying elsewhere) could not be interviewed.

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, 2015-2019 American Community Survey 5-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..Average family size is derived by dividing the number of related people in households by the number of family households..Housing unit weight is used throughout this table (only exception is the average household and family size cells)..The 2015-2019 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 delineation lists 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.

The dataset contains data from the 13th General Population Census relating to households. For the purposes of the census, a family means a group of people linked by marriage, kinship, affinity, adoption, protection or emotional ties, cohabitants and having habitual residence in the same municipality (even if they are not yet registered in the population register of the same municipality). A family can also be made up of one person. It is considered the holder of the family sheet, preferably, the person to whom the family card is registered in the registry. Household service staff (housekeepers, family assistants, etc.) who habitually reside in the dwelling shall constitute a family in its own right provided that there are no links of any kind between the members of the household and those covered by the definition referred to above. The following support tables are also available in the dataset: * Column descriptions * Decoding the codes contained in the columns. The table linking Census Sections and territorial divisions is available at the following link: https://dati.comune.milano.it/dataset/ds1635-census-1991-census-sections-connection-between-sections-and-divisions-territorials

This dataset provides Census 2021 estimates that classify households with usual residents in England and Wales by various household characteristics, including variations in tenure by household size, household family composition, multi-generational households, and household level information on the age, ethnic group, religion, employment status and occupation of household members. The estimates are as at Census Day, 21 March 2021.

These datasets are part of Household characteristics by tenure, England and Wales: Census 2021, a release of results from the 2021 Census for England and Wales. Figures may differ slightly in future releases because of the impact of removing rounding and applying further statistical processes.

Total counts for some household groups may not match between published tables. This is to protect the confidentiality of households' data. Household counts have been rounded to the nearest 5 and any counts below 10 were suppressed; this is signified by a 'c' in the data tables.

This dataset uses middle layer super output area (MSOA) and lower layer super output area (LSOA) geography boundaries as of 2021 and local authority district geography boundaries as of 2022.

In this dataset, the number of households in an area is broken down by different variables and categories. If you were to sum the counts of households by each variable and category, it may not sum to the total of households in that area. This is because of rounding, suppression and that some tables only include data for certain household groups.

In this dataset, variables may have different categories for different geography levels. When variables are broken down by more categories, they may not sum to the total of the higher level categories due to rounding and suppression.

Social rent is not separated into “housing association, housing co-operative, charitable trust, registered social landlord” and “council or local authority districts” because of respondent error in identifying the type of landlord. This is particularly clear in results for areas which have no local authority districts housing stock, but there are households responding as having a “council or local authority districts” landlord type. Estimates are likely to be accurate when the social rent category is combined.

The Census Quality and Methodology Information report contains important information on:

• the uses and users of the census data
• the strengths and limitations of the census data
• the quality characteristics of the census data
• the methods used to produce the census data

Quality notes can be found here

Housing quality information for Census 2021 can be found here

Household

A household is defined as one person living alone, or a group of people (not necessarily related) living at the same address who share cooking facilities and a living room, sitting room or dining area. This includes all sheltered accommodation units in an establishment (irrespective of whether there are other communal facilities) and all people living in caravans on any type of site that is their usual residence; this will include anyone who has no other usual residence elsewhere in the UK. A household must contain at least one person whose place of usual residence is at the address. A group of short-term residents living together is not classified as a household, and neither is a group of people at an address where only visitors are staying.

Usual resident

For Census 2021, a usual resident of the UK is anyone who, on Census Day, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

Household reference person (HRP)

A person who serves as a reference point, mainly based on economic activity and age, to characterize a whole household. The person is not necessarily the member of the household in whose name the accommodation is owned or rented.

Tenure

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; owned with a mortgage or loan; or 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; or lived in rent free, which is where the household does not own the accommodation and does not pay rent to live there, for example living in a relative or friend’s property or live-in carers or nannies. This information is not available for household spaces with no usual residents.

_Household size _

The number of usual residents in the household.

Household family composition

Households according to the relationships between members. Single-family households are classified by the number of dependent children and family type (married, civil partnership or cohabiting couple family, or lone parent family). Other households are classified by the number of people, the number of dependent children and whether the household consists only of students or only of people aged 66 years and over.

Multi-generational households

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.

_Household combination of resident age _

Classifies households by the ages of household members on 21 March 2021. Households could be made up of residents aged 15 years and under; residents aged 16 to 64 years; residents aged 65 years and over; or a combination of these.

Ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options. For more information, see ONS's Ethnic group, England and Wales: Census 2021 bulletin

Household combination of resident ethnic group

Classifies households by the ethnic groups household members identified with.

Religion

The religion people connect or identify with (their religious affiliation), whether or not they practice or have belief in it. This question was voluntary and includes people who identified with one of 8 tick-box response options, including 'No religion', alongside those who chose not to answer this question. For more information, see ONS's Religion, England and Wales: Census 2021 bulletin

Household combination of resident religion

Classifies households by the religious affiliation of household members who chose to answer the religion question. The classifications may include residents who did not answer the religion question.

Household combination of resident employment status

Classifies households by the employment status of household members aged 16 years and over between 15 and 21 March 2021. Households could be made up of employed residents (employee or self-employed); unemployed residents (looking for work and could start within two weeks, or waiting to start a job that had been offered and accepted); economically inactive residents (unemployed and had not looked for work between 22 February to 21 March 2021, or could not start work within two weeks); or a combination of these.

Occupation

"Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020. It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation. The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels. Occupation classifications include :

• manager, director or senior official occupations (such as Elected Representatives and Senior Police Officers)
• professional occupations (such as Doctors and Teachers)
• associate professional and technical occupations (such as Police Officers and Counsellors)
• administrative or secretarial occupations (such as Office Managers and Receptionists)
• skilled trade occupations (such as Electricians and Chefs)
• caring, leisure or other service occupations (such as Teaching Assistants and Home Carers)
• sales and customer service occupations (such as Cashiers and Shopkeepers)
• process, plant and machine operatives (such as Bus Drivers and Scaffolders)
• elementary occupations (such as Postal Workers and Waiters)"

Situation 2019
A household, within the meaning of the population census, designates all the people who share the same main residence, without these people necessarily being united by family ties. A household can consist of only one person. There is equality between the number of households and the number of main residences. Note: People living in mobile homes, boatmen, the homeless, and people living in communities (worker homes, retirement homes, university residences, detention centers, etc.) are considered to be living outside the household. It is only from 2011 that the data is comparable to 2006

Consult the metadata

Remittances are transfers of money by a person working in a foreign location to a person or family back home as household income. As per IMF, Remittances are typically transfers from a well-meaning individual or family member to another individual or household. They are targeted to meet specific needs of the recipients, and this tends to reduce poverty. This Dataset contains year and country-wise remittance inflows. It also has data related to Low and Middle income countries

Note: All numbers are in current (nominal) US Dollars.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Latest edition information

For the second edition (September 2023), the variables NSECM20, NSECMJ20, SC2010M, SC20SMJ, SC20SMN, SOC20M and SOC20O have been replaced with new versions. Further information on the SOC revisions can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

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..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 2014 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, 2014 American Community Survey 1-Year Estimates

This graph shows the average size of households in China from 1990 to 2023. That year, statistically about 2.8 people were living in an average Chinese household. Average household size in China A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations. The average number of people living in one household in China dropped from 3.96 in 1990 to 2.87 in 2011. Since 2010, the figure was relatively stable and ranged between 2.87 and 3.17 people per household. The average Chinese household still counts as rather large in comparison to other industrial countries. In 2023, an average American household consisted of only 2.51 people. Comparable figures have already been reached in the bigger cities and coastal areas of China, but in the rural provinces the household size is still much larger. According to the National Bureau of Statistics of China, the household size in China was diametrically correlated to its income. Birth rates and household sizes The receding size of Chinese households may be linked to the controversial one-child policy introduced in 1979. The main aim of the policy was to control population growth. While the fertility rate in China had been very high until the 1970s, it fell considerably in the following decades and resided at only 1.7 children per woman in 2018, nearly the same as in the United States or in the United Kingdom. A partial ease in the one-child policy was introduced in 2013, due to which couples where at least one parent was an only child were allowed to have a second child. In October 2015, the law was changed into a two-child policy becoming effective in January 2016.

This layer represents USDA Food Access Research Atlas data at the census tract geography. Low Income is defined as tracts with a poverty rate of 20% or higher, or tracts with median family income less than 80% of median family income of the state or metropolitan area. Low Access is defined as tracts where a significant number or share of residents is more than 1 mile (urban) or 10 miles (rural) from the nearest supermarket.http://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas.aspxFood accessLimited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some Americans to eat a healthy diet. There are many ways to measure food store access for individuals and for neighborhoods, and many ways to define which areas are food deserts—neighborhoods that lack healthy food sources. Most measures and definitions take into account at least some of the following indicators of access:Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area.Individual-level resources that may affect accessibility, such as family income or vehicle availability.Neighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation.In the Food Access Research Atlas, several indicators are available to measure food access along these dimensions. For example, users can choose alternative distance markers to measure low access in a neighborhood, such as the number and share of people more than half a mile to a supermarket or 1 mile to a supermarket. Users can also view other census-tract-level characteristics that provide context on food access in neighborhoods, such as whether the tract has a high percentage of households far from supermarkets and without vehicles, individuals with low income, or people residing in group quarters.Low-income neighborhoodsThe criteria for identifying a census tract as low income are from the Department of Treasury’s New Markets Tax Credit (NMTC) program. This program defines a low-income census tract as any tract where:The tract’s poverty rate is 20 percent or greater; orThe tract’s median family income is less than or equal to 80 percent of the State-wide median family income; orThe tract is in a metropolitan area and has a median family income less than or equal to 80 percent of the metropolitan area's median family income.Low-access census tractsIn the Food Access Research Atlas, low access to healthy food is defined as being far from a supermarket, supercenter, or large grocery store ("supermarket" for short). A census tract is considered to have low access if a significant number or share of individuals in the tract is far from a supermarket.In the original Food Desert Locator, low access was measured as living far from a supermarket, where 1 mile was used in urban areas and 10 miles was used in rural areas to demarcate those who are far from a supermarket. In urban areas, about 70 percent of the population was within 1 mile of a supermarket, while in rural areas over 90 percent of the population was within 10 miles (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Updating the original 1- and 10-mile low-access measure shows that an estimated 18.3 million people in these low-income and low-access census tracts were far from a supermarket in 2010.Three additional measures of food access based on distance to a supermarket are provided in the Atlas:One additional measure applies a 0.5-mile demarcation in urban areas and a 10-mile distance in rural areas. Using this measure, an estimated 52.5 million people, or 17 percent of the U.S. population, have low access to a supermarket;A second measure applies a 1.0-mile demarcation in urban areas and a 20-mile distance in rural areas. Under this measure, an estimated 16.5 million people, or 5.3 percent of the U.S. population, have low access to a supermarket; andA slightly more complex measure incorporates vehicle access directly into the measure, delineating low-income tracts in which a significant number of households are located far from a supermarket and do not have access to a vehicle. This measure also includes census tracts with populations that are so remote, that, even with a vehicle, driving to a supermarket may be considered a burden due to the great distance. Using this measure, an estimated 2.1 million households, or 1.8 percent of all households, in low-income census tracts are far from a supermarket and do not have a vehicle. An additional 0.3 million people are more than 20 miles from a supermarket.For each of the first three measures that are based solely on distance, a tract is designated as low access if the aggregate number of people in the census tract with low access is at least 500 or the percentage of people in the census tract with low access is at least 33 percent. For the final measure using vehicle availability, a tract is designated as having low vehicle access if at least one of the following is true:at least 100 households are more than ½ mile from the nearest supermarket and have no access to a vehicle; orat least 500 people or 33 percent of the population live more than 20 miles from the nearest supermarket, regardless of vehicle access.Methods used to assess distance to the nearest supermarket are the same for each of these measures. First, the entire country is divided into ½-km square grids, and data on the population are aerially allocated to these grids (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Then, distance to the nearest supermarket is measured for each grid cell by calculating the distance between the geographic center of the ½-km square grid that contains estimates of the population (number of people and other subgroup characteristics) and the center of the grid with the nearest supermarket.Once the distance to the nearest supermarket is calculated for each grid cell, the estimated number of people or housing units that are more than 1 mile from a supermarket in urban tracts, or 10 miles in rural census tracts, is aggregated at the census-tract level (and similarly for the alternative distance markers). A census tract is considered rural if the population-weighted centroid of that tract is located in an area with a population of less than 2,500; all other tracts are considered urban tracts.Food desertsThe Food Access Research Atlas maps census tracts that are both low income (li) and low access (la), as measured by the different distance demarcations. This tool provides researchers and other users multiple ways to understand the characteristics that can contribute to food deserts, including income level, distance to supermarkets, and vehicle access.Additional tract-level indicators of accessVehicle availabilityA tract is identified as having low vehicle availability if more than 100 households in the tract report having no vehicle available and are more than 0.5 miles from the nearest supermarket. This corresponds closely to the 80th percentile of the distribution of the number of housing units in a census tract without vehicles at least 0.5 miles from a supermarket (the 80th percentile value was 106 housing units). This means that about 20 percent of all census tracts had more than 100 housing units that were 0.5 miles from a supermarket and without a vehicle. This indicator was applied to both urban and rural census tracts.Overall, 8.8 percent of all housing units in the United States do not have a vehicle, and 4.2 percent of all housing units are at least 0.5 mile from a store and without a vehicle. Vehicle availability is defined in the American Community Survey as the number of passenger cars, vans, or trucks with a capacity of 1-ton or less kept at the home and available for use by household members. The number of available vehicles includes those vehicles leased or rented for at least 1 month, as well as company, police, or government vehicles that are kept at home and available for non-business use.Whether a vehicle is available to a household for private use is an important additional indicator of access to healthy and affordable food. For households living far from a supermarket or large grocery store, access to a private vehicle may make accessing these retailers easier than relying on public or alternative means of transportation.Group quarters populationUsers may be interested in highlighting tracts with large shares of people living in group quarters. Group quarters are residential arrangements where an entity or organization owns and provides housing (and often services) for individuals residing in these buildings. This includes college dormitories, military quarters, correctional facilities, homeless shelters, residential treatment centers, and assisted living or skilled nursing facilities. These living arrangements frequently provide dining and food retail solely for their residents. While individuals living in these areas may appear to be far from a supermarket or grocery store, they may not truly experience difficulty accessing healthy and affordable food. Tracts in which 67 percent of individuals or more live in group quarters are highlighted.General tract characteristicsPopulation, tract totalGeographic level: census tractYear of data: 2010Definition: Total number of individuals residing in a tract.Data sources: Data are from the 2012 report, Access to Affordable and Nutritious Food: Updated Estimates of Distances to Supermarkets Using 2010 Data. Population data are reported at the block level from the 2010 Census of Population and Housing. These data were aerially allocated down to ½-kilometer-square grids across the United States.Low-income tractGeographic level: census tractYear of data: 2010Definition: A tract with either a poverty rate of 20

Attitude to questions of raising children, education, marriage and family. Judgement on the occupational situation and the relationship between generations.

Topics: 1. Basic questions on marriage and family: meaning of family; ideal number of children and desire for children; reasons against further children; attitude to childless marriage; assessment of significance for development policy of a reduction in birth rate; appropriate measures against decreasing birthrates and assumed reasons for the decrease in births; adequate preparation of young people for marriage and family; basic conditions of a marriage; attitude to divorce; evaluation of measures for family policy.

1. Family life and family interaction: attitude to family; reasons for arguments in one´s family; person to confide in with problems; occupation with the children; researching one´s own family history; magazines and public advice centers as possible advisers for help with life.

2. Dominance or partnership: agreement between the spouses and reasons for differences of opinion; role distribution or dominating person in the marriage; equal rights and duties of the partners; adapting to the spouse; role distribution or dominance in raising children; conversations with partner about problems at work.

3. Education questions: education style and problems with education; possible help with education problems; type of advice centers used; judgement on and attitude to advice centers; attitude to bad school report cards and supervision of school work; desired occupation for the children; attitude to sex education; desired development possibilities for the children; judgement on the generation conflict.

4. Achievement orientation and education: attitude to education and assumed causes for inadequate education; judgement on the school as preparation for employment; desired studies for the children; general moral orientation.

5. Leisure time: leisure desires; reduction in leisure time from household and family; actual and desired activities on Sunday; vacation activities and vacation hindrances; behavior going out; evening activities and significance of television.

6. Relatives, friendship and neighborhood: contacts with friends and acquaintances; neighbors´ willingness to help; place of residence of parents.

7. Occupation and occupation stress: effects of occupation on family life; wish to quit or begin employment; interest in return to work and reasons for the desire for employment; attitude to the job of housewife or role as mother; work orientation.

8. Miscellaneous: self-assessment of social class and class consciousness; attitude to the abortion regulation; sources of political information.

Demography: age; sex; marital status; age and number of children; year of marriage; share of support of household; religious denomination; school education; occupation; employment in the civil service; employment; number of persons with their own income in household; size of household; composition of household; respondent is head of household; characteristics of head of household; self-assessment of social class and class-consciousness; party preference and party inclination; religiousness; membership in clubs, associations and parties; contribution payments; housing situation and residential status; size of residence; desired residence change; possession of durable economic goods; living together with partner; age of spouse.

Also encoded was: form of housing; type of city; living in new construction area; city size; state; identification of interviewer; ZIP (postal) code; district code.

Between April 2008 and March 2024, households from the Pakistani and Bangladeshi ethnic groups were the most likely to live in low income out of all ethnic groups, before and after housing costs.

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

Face-to-face interview: PAPI (Paper and Pencil Interview) Self-administered questionnaire: Paper

The aim of this study is to throw light on why inequality in the distribution of income in Sweden fell from the mid-1920s to the second part of the 1950s. For this reason the project decided to collect income information referring to different years from a sample of households for one Swedish city. A database was created by coding tax records and other documents for the city of Göteborg, the second largest city in Sweden.

The determination of which years to investigate was critical. For analysing changes over time it was thought as essential to have roughly equal numbers of years between years studied. Further, it was thought advisable to avoid years with too much macroeconomic turmoil as well as the years of the two World Wars. Balancing the resources for the data collection between the size of a sub sample and the number of subsamples, it was decided to assemble data for four years. The years 1925, 1936, 1947 and 1958 was chosen to investigate. It should be pointed out that the year 1947 was preferred to the following years as large social insurance reforms leading to increases in pension benefits and the introduction of child allowances were put in effect in 1948.

Household is defined from registers kept in the archives (Mantalslängder). A household is defined as persons with the same surname living in the same apartment or single-family house. This means that there can be people belonging to more than two generations in the same household; siblings living together can make up a household as well. Foster children are included as long as they are registred at the same address. Adult children are considered to be living in the household of their parents as long as they are registred at the same address. In almost all cases, servants and tenants not belonging to the household are treated as separate households.

Syfte:

Syftet med studien var att belysa varför ojämlikhet i inkomst minskade från mitten på 1920-talet till andra hälften av 1950-talet.