This layer shows the average household size in Indonesia in 2022, in a multiscale map (Country, Province, County, District, and Subdistrict). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by 15-year age incrementsCount of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in Dominican Republic in 2018, in a multiscale map (Country, Region, Province, and Municipality). Nationally, the average household size is 3.5 people per household. It is calculated by dividing the household population by total households.

This map shows the average household size in Kazakhstan in 2022, in a multiscale map (Country and Region). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age incrementsCounts of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows the average household size in Mozambique in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 4.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows the average household size in Morocco in 2023, in a multiscale map (Country, Region, and Province). Nationally, the average household size is 4.2 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in Saudi Arabia in 2022, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 5.2 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in United Kingdom in 2023, in a multiscale map (United Kingdom, Country, Region, County, District, Lower Super Output Area, and Census Output Area). Nationally, the average household size is 2.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of household by typeCount of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in February, 2024 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Based on the 2021 census conducted in the Philippines, the household population in Metro Manila or the National Capital Region (NCR) reached 3.51 million, indicating an increase from the previous year. The number of households in the region gradually increased since the 2009 census.

This feature set contains household and population projections from Projections 2040 for the San Francisco Bay Region. This forecast represents household and population projections resulting from Plan Bay Area 2040. Numbers are provided by 2010 Census Tract. Household and population numbers are included for 2010 (two versions), 2015, 2020, 2025, 2030, 2035, and 2040. For 2010, two data points are provided:A tabulation (base year A) from the 2010 model simulation (base year A); and(Preferred) A tabulation (base year B) from the 2010 pre-run microdata, designed to approximate (but may still differ from) Census 2010 counts.Projection data is included for total households, total population, household population, and group quarters population.This feature set was assembled using unclipped Census Tract features. For those who prefer Projections 2040 data using jurisdiction features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region 2010 Census Tracts (clipped). Clipping the Census Tract features does result in the removal of some water tracts, which are usually empty, so there is a difference in the number of features between the two services.Other Projections 2040 feature sets:Households and population per countyHouseholds and population per jurisdiction (incorporated place and unincorporated county)Jobs and employment per countyJobs and employment per jurisdiction (incorporated place and unincorporated county)Jobs per Census TractFemale population, by age range, per countyFemale population, by age range, per jurisdiction (incorporated place and unincorporated county)Male population, by age range, per countyMale population, by age range, per jurisdiction (incorporated place and unincorporated county)Total population, by age range, per countyTotal population, by age range, per jurisdiction (incorporated place and unincorporated county)

This feature set contains household and population projections from Projections 2040 for the San Francisco Bay Region. This forecast represents household and population projections resulting from Plan Bay Area 2040. Numbers are provided by county. Household and population numbers are included for 2010 (two versions), 2015, 2020, 2025, 2030, 2035, and 2040. For 2010, two data points are provided:A tabulation (base year A) from the 2010 model simulation (base year A); and(Preferred) A tabulation (base year B) from the 2010 pre-run microdata, designed to approximate (but may still differ from) Census 2010 counts.Projection data is included for total households, group quarter population, household population, persons per household, and total population.This feature set was assembled using unclipped county features. For those who prefer Projections 2040 data using county features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region Counties (clipped).Other Projections 2040 feature sets:Households and population per jurisdiction (incorporated place and unincorporated county)Households and population per Census TractJobs and employment per countyJobs and employment per jurisdiction (incorporated place and unincorporated county)Jobs per Census TractFemale population, by age range, per countyFemale population, by age range, per jurisdiction (incorporated place and unincorporated county)Male population, by age range, per countyMale population, by age range, per jurisdiction (incorporated place and unincorporated county)Total population, by age range, per countyTotal population, by age range, per jurisdiction (incorporated place and unincorporated county)

This layer shows the average household size in Germany in 2023, in a multiscale map (Country, State, Province, District, and Municipality). Nationally, the average household size is 2.0 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of households by typeCount of population by 15-year age incrementsThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in February, 2024 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Abstract

Philippines Population Census 2015 was designed to take an inventory of the total population in the country and collect information about its characteristics. The census of population is the source of information on the size, distribution, and composition of the population in each barangay, city/municipality, province, and region in the country, as well as information about its demographic, social, and economic characteristics. These indicators are vital in the formulation of rational plans and programs towards national and local development.

Specifically, POPCEN 2015 gathered data on: - size and geographic distribution of the population; - population composition in terms of age, sex, and marital status; - religious affiliation; - school attendance, literacy, highest grade/year completed, and technical/vocational course obtained; - usual activity/occupation, and whether overseas worker for members 15 years old and over; - registration of birth and death; - household-level characteristics such as fuel used for lighting and source of water supply for drinking and cooking; - housing characteristics such as the type of building, construction materials of the roof of the building, construction materials of the outer walls of the building/housing unit, and tenure status of the housing unit/lot; and - barangay characteristics such as the presence of selected facilities and establishments; and presence of informal settlers, relocation areas, and in-movers in the barangay due to natural and man-made disasters.

August 1, 2015 was designated as Census Day for the POPCEN 2015, on which date the enumeration of the population in the Philippines was referred. For the purpose of this census, all information collected about the population were as of 12:01 a.m., Saturday, August 1, 2015.

Enumeration lasted for about 25 days, from 10 August to 6 September 2015. In some areas, enumeration was extended until 15 September 2015 for large provinces.

Geographic coverage

The population count is available at the barangay, city/municipal, provincial, regional, and national levels. Demographic, social, and economic characteristics are tabulated at the city/municipal, provincial, regional, and national levels.

Analysis unit

The following are the units of analysis in POPCEN 2015: 1. Individual person 2. Household 3. Housing unit 4. Institutional Population 5. Barangay

Universe

The POPCEN 2015 covered all persons who were alive as of 12:01 a.m. August 1, 2015, and who were members of the household and institution as follows:

Persons Enumerated as Members of the Household:

1. Those who were present at the time of visit and whose usual place of residence was the housing unit where the household lived;

2. Family members who were overseas workers and who were away at the time of the census and were expected to be back within five years from the date of last departure. These included household members who may or may not have had a specific work contract or had been presently at home on vacation but had an existing overseas employment to return to. Undocumented overseas workers were still considered as members of the household for as long as they had been away for not more than five years. Immigrants, however, were excluded from the census.

3. Those whose usual place of residence was the place where the household lived but were temporarily away at the time of the census for any of the following reasons: a. on vacation, business/pleasure trip, or training somewhere in the Philippines and was expected to be back within six months from the date of departure. An example was a person on training with the Armed Forces of the Philippines for not more than six months; b. on vacation, business/pleasure trip, on study/training abroad and was expected to be back within a year from the date of departure; c. working or attending school outside their usual place of residence but usually came home at least once a week; d. confined in hospitals for a period of not more than six months as of the time of enumeration, except when they were confined as patients in mental hospitals, leprosaria/leper colonies or drug rehabilitation centers, regardless of the duration of their confinement; e. detained in national/provincial/city/municipal jails or in military camps for a period of not more than six months as of the time of enumeration, except when their sentence or detentionwas expected to exceed six months; f. on board coastal, interisland, or fishing vessels within Philippine territories; and g. on board oceangoing vessels but expected to be back within five years from the date of departure.

4. Boarders/lodgers of the household or employees of household-operated businesses who did not return/go home to their respective households weekly;

5. Citizens of foreign countries who resided or were expected to reside in the Philippines for at least a year from their arrival, except members of diplomatic missions and non-Filipino members of international organizations;

6. Filipino balikbayans with usual place of residence in a foreign country but resided or were expected to reside in the Philippines for at least a year from their arrival; and

7. Persons temporarily staying with the household who had no usual place of residence or who were not certain to be enumerated elsewhere.

Persons Enumerated as Members of the Institutional Population:

1. Permanent lodgers in boarding houses;

2. Dormitory residents who did not usually go home to their respective households at least once a week;

3. Hotel residents who stayed in the hotel for more than six months at the time of the census;

4. Boarders in residential houses, provided that their number was 10 or more. However, if the number of boarders in a house was less than 10, they were considered as members of regular households, not of institutions;

5. Patients in hospitals who were confined for more than six months;

6. Patients confined in mental hospitals, leprosaria or leper colonies, and drug rehabilitation centers, regardless of the length of their confinement;

7. Wards in orphanages, homes for the aged, and other welfare institutions;

8. Prisoners of corrective and penal institutions;

9. Seminarians, nuns in convents, monks, and postulants;

10. Soldiers residing in military camps; and

11. Workers in mining and similar camps.

All Filipinos in Philippine embassies, missions, and consulates abroad were also included in the enumeration.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The POPCEN 2015 is a complete enumeration of all persons, households and institutional population in the country. No sampling was done.

Mode of data collection

Face-to-face interview [f2f] and self-administered; Paper and Pencil

Research instrument

Listed below are the basic census forms that were used during the field enumeration:

• CP Form 1 - Listing Booklet This booklet was used to list the buildings, housing units, households, and ILQs within an EA. It was also used to record other information such as the address of the household head or ILQ, total population, and number of males and females corresponding to each household and ILQ listed.

• CP Form 2 - Household Questionnaire This four-page questionnaire was used to record information about the households. Specifically, this form was used to gather information on selected demographic and socio-economic characteristics of the population and some information on housing characteristics.

• CP Form 4 - Institutional Population Questionnaire This four-page questionnaire was used to record information on selected demographic and socio-economic characteristics of the population residing in ILQs.

• CP Form 5 - Barangay Schedule This four-page questionnaire was used to record the physical characteristics (e.g. street pattern) and the presence of service facilities and establishments by kind and emplyment size in the barangay. It was also used to record the presence of informal settlers, relocation areas, and in-movers in the barangay due to natural and man-made disasters.

• CP Form 7 - Household Self-Administered Questionnaire Instructions This form contains specific and detailed instructions on how to fill out/accomplish each item in CP Form 2. It was used as guide/reference by respondents who were not, for some reasons, personally interviewed by the EN.

• CP Form 8 - Institutional Population Self-Administered Questionnaire Instructions This form contains specific and detailed instructions for the managers/administrators to guide them in accomplishing each item in CP Form 4. It was used as guide/reference by managers or administrators of an ILQ.

Listed below are the major administrative and accomplishment forms that were also used to facilitate data collection and supervision, and monitoring of enumeration and personnel:

• Mapping Form This form was used to plot buildings, either occupied by households or vacant, ILQs and important physical landmarks in the area. It was also used to enlarge a map or a block of an EA/barangay if the area being enumerated is too large or congested. CP Form 1 - Listing Booklet

• CP Form 6 - Notice of Listing/Enumeration This form is a sticker. After listing and interviewing a household or ILQ, this sticker was posted in a very conspicuous place, preferably in front of the house or at the gate of the building. This form was used for control and monitoring purposes as its presence indicates that a particular housing unit or ILQ had already been listed/interviewed.

• CP Form 9 - Appointment Slip to the Household/Institution/Barangay Official This form was used to set an appointment with the

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...

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.

Abstract

The household registration system known as ho khau has been a part of the fabric of life in Vietnam for over 50 years. The system was used as an instrument of public security, economic planning, and control of migration, at a time when the state played a stronger role in direct management of the economy and the life of its citizens. Although the system has become less rigid over time, concerns persist that ho khau limits the rights and access to public services of those who lack permanent registration in their place of residence. Due largely to data constraints, however, previous discussions about the system have relied largely on anecdotal or partial information.

Drawing from historical roots as well as the similar model of China’s hukou, the ho khau system was established in Vietnam in 1964. The 1964 law established the basic parameters of the system: every citizen was to be registered as a resident in one and only household at the place of permanent residence, and movements could take place only with the permission of authorities. Controlling migration to cities was part of the system’s early motivation, and the system’s ties to rationing, public services, and employment made it an effective check on unsanctioned migration. Transfer of one’s ho khau from one place to another was possible in principle but challenging in practice.

The force of the system has diminished since the launch of Doi Moi as well as a series of reforms starting in 2006. Most critically, it is no longer necessary to obtain permission from the local authorities in the place of departure to register in a new location. Additionally, obtaining temporary registration status in a new location is no longer difficult. However, in recent years the direction of policy changes regarding ho khau has been varied. A 2013 law explicitly recognized the authority of local authorities to set their own policies regarding registration, and some cities have tightened the requirements for obtaining permanent status.

Understanding of the system has been hampered by the fact that those without permanent registration have not appeared in most conventional sources of socioeconomic data. To gather data for this project, a survey of 5000 respondents in five provinces was done in June-July 2015. The samples are representative of the population in 5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong. Those five provinces/cities are among the provinces with the highest rate of migration as estimated using data from Population Census 2009.

Geographic coverage

5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling for the Household Registration Survey was conducted in two stages. The two stages were selection of 250 enumeration areas (50 EAs in each of 5 provinces) and then selection of 20 households in each selected EA, resulting in a total sample size of 5000 households. The EAs were selected using Probability Proportional to Size (PPS) method based on the square number of migrants in each EA, with the aim to increase the probability of being selected for EAs with higher number of migrants. “Migrants” were defined using the census data as those who lived in a different province five years previous to the census. The 2009 Population Census data was used as the sample frame for the selection of EAs. To make sure the sampling frame was accurate and up to date, EA leaders of the sampled EAs were asked to collection information of all households regardless of registration status at their ward a month before the actual fieldwork. Information collected include name of head of household, address, gender, age of household’s head, household phone number, residence registration status of household, and place of their registration 5 years ago. All households on the resulting lists were found to have either temporary or permanent registration in their current place of residence.

Using these lists, selection of survey households was stratified at the EA level to ensure a substantial surveyed population of households without permanent registration. In each EA random selection was conducted of 12 households with temporary registration status and 8 households with permanent registration status. For EAs where the number of temporary registration households was less than 12, all of the temporary registration households were selected and additional permanent registration households were selected to ensure that each EA had 20 survey households. Sampling weights were calculated taking into the account the selection rules for the first and second stages of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was mostly adapted from the Vietnam Household Living Standard Survey (VHLSS), and the Urban Poverty Survey (UPS) with appropriate adjustment and supplement of a number of questions to follow closely the objectives of this survey. The household questionnaire consists of a set of questions on the following contents:

• Demographic characteristics of household members with emphasis on their residence status in terms of both administrative management (permanent/temporary residence book) and real residential situation. • Education of household members. Beside information on education level, the respondents are asked whether a household member attend school as “trai-tuyen” , how much “trai-tuyen” fee/enrolment fee, and difficulty in attending schools without permanent residence status. • Health and health care, collecting information on medical status and health insurance card of household members. • Labour and employment, asking household member’s employment status in the last 30 days; their most and second-most time-consuming employment during the last 30 days; and whether they had been asked about residence status when looking for job. • Assets and housing conditions. This section collects information on household’s living conditions such as assets, housing types and areas, electricity, water and energy. • Income and expenditure of households. • Social inclusion and protection. The respondents are asked whether their household members participate in social organizations, activities, services, contribution; whether they benefit from any social project/policy; do they have any loans within the last 12 months; and to provide information about five of their friends at their residential area. • Knowledge on the Law of Residence, current regulations on conditions for obtaining permanent residence, experience dealing with residence issues, and opinion on current household registration system of the respondents.

Cleaning operations

Managing and Cleaning the Data

Data were managed and cleaned each day immediately upon being received, which occurred at the same time as the fieldwork surveys. At the end of each workday, the survey teams were required to review all of the interviews conducted and transfer collected data to the server. The data received by the main server were downloaded and monitored by MDRI staff.

At this stage, MDRI assigned a technical team to work on the data. First, the team listened to interview records and used an application to detect enumerators’ errors. In this way, MDRI quickly identified and corrected the mistakes of the interviewers. Then the technical team proceeded with data cleaning by questionnaire, based on the following quantity and quality checking criteria.

• Quantity checking criteria: The number of questionnaires must be matched with the completed interviews and the questionnaires assigned to each individual in the field. According to the plan, each survey team conducted 20 household questionnaires in each village. All questionnaires were checked to ensure that they contained all essential information, and duplicated entries were eliminated. • Quality checking criteria: Our staff performed a thorough examination of the practicality and logic of the data. If there was any suspicious or inconsistent information, the data management team re – listened to the records or contacted the respondents and survey teams for clarification via phone call. Necessary revisions would then be made.

Data cleaning was implemented by the following stages: 1. Identification of illogical values; 2. Software – based detection of errors for clarification and revision; 3. Information re-checking with respondents and/or enumerators via phone or through looking at the records; 4. Development and implementation of errors correction algorithms; The list of detected and adjusted errors is attached in Annex 6.

Outlier detection methods The data team applied a popular non - parametric method for outlier detection, which can be done with the following procedure: 1. Identify the first quartile Q1 (the 25th percentile data point) 2. Identify the third quartile Q3 (the 75th percentile data point) 3. Identify the inter-quartile range(IQR): IQR=Q3-Q1 4. Calculate lower limits (L) and upper limits (U) by the following formulas: o L=Q1-1.5*IQR o U=Q3+1.5*IQR 5. Detect outliers by the rule: An observation is an outlier if it lies below the lower bound or beyond the upper bound (i.e. less than L or greater than U)

Data Structure The completed dataset for the “Household registration survey 2015” includes 9 files in STATA format (.dta): • hrs_maindata: Information on the households, including: assets, housing, income, expenditures, social inclusion and social protection issues, household registration procedures • hrs_muc1: Basic information on the

Abstract

The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.

Analysis unit

Household-level; Individual-level: household primary breadwinners, respondent, student, primary caregivers, and under-5 years old kids

Sampling procedure

The sampling frame of the Indonesia high-frequency phone-based monitoring of socio-economic impacts of COVID-19 on households was the list of households enumerated in three recent World Bank surveys, namely Urban Survey (US), Rural Poverty Survey (RPS), and Digital Economy Household Survey (DEHS). The US was conducted in 2018 with 3,527 sampled households living in the urban areas of 10 cities and 2 districts in 6 provinces. The RPS was conducted in 2019 with the sample size of 2,404 households living in rural areas of 12 districts in 6 provinces. The DEHS was conducted in 2020 with 3,107 sampled households, of which 2,079 households lived in urban areas and 1,028 households lived in rural areas in 26 districts and 31 cities within 27 provinces. Overall, the sampled households drawn from the three surveys across 40 districts and 35 cities in 27 provinces (out of 34 provinces). For the final sampling frame, six survey areas of the DEHS which were overlapped with the survey areas in the UPS were dropped from the sampling frame. This was done in order to avoid potential bias later on when calculating the weights (detailed below). The UPS was chosen to be kept since it had much larger samples (2,016 households) than that of the DEHS (265 households). Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code.

In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.

In the Round 8 survey where we focused on early nutrition knowledge and early child development, we introduced an additional respondent who is the primary caregiver of under 5 years old in the household. We prioritized the mother as the target of caregiver respondents. In households with multiple caregivers, one is randomly selected. Furthermore, only the under 5 children who were taken care of by the selected respondent will be listed in the early child development module.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire in English is provided for download under the Documentation section.

Response rate

The HiFy survey was initially designed as a 5-round panel survey. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers was from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, 6, 7, and 8 were 4,067 (94%), 3,953 (91%), 3,686 (85%), 3,471 (80%), 3,435 (79%), 3,383 (78%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, 6, 7, and 8 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%), 3,116 (72%), and 2,856 (66%) respectively.

Abstract

The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, health and social development, housing, household access to services and facilities, food security, and agriculture. This report has three main objectives: firstly, to present the key findings of GHS 2014. Secondly, it provides trends across a thirteen year period, i.e. since the GHS was introduced in 2002; and thirdly, it provides a more in-depth analysis of selected service delivery issues. As with previous reports, this report will not include tables with specific indicators measured, as these will be included in a more comprehensive publication of development indicators, entitled Selected development indicators

Geographic coverage

The General Household Survey 2014 had national coverage. The lowest level of geographic aggregation for this dataset is province.

Analysis unit

The units of anaylsis for the General Household Survey 2014 are individuals and households.

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage design was used in this survey, which is based on a stratified design with probability proportional to size selection of primary sampling units (PSUs) at the first stage and sampling of dwelling units (DUs) with systematic sampling at the second stage. After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2001 data (secondary stratification).Survey officers employed and trained by Stats SA visited all the sampled dwelling units in each of the nine provinces. During the first phase of the survey, sampled dwelling units were visited and informed about the coming survey as part of the publicity campaign. The actual interviews took place four weeks later. A total of 25 363 households (including multiple households) were successfully interviewed during face-to-face interviews. Two hundred and thirty-three enumerators (233) and 62 provincial and district coordinators participated in the survey across all nine provinces. An additional 27 quality assurors were responsible for monitoring and ensuring questionnaire quality. National training took place over a period of four days. The national trainers then trained provincial trainers for five days at provincial level. They in turn provided district training to the survey officers for a period of six days.

Sampling deviation

The sample design for the GHS 2014 was based on a master sample (MS) that was originally designed for the Quarterly Labour Force Survey (QLFS) and was used for the first time for the GHS in 2008. This master sample is shared by the QLFS, GHS, Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Survey (IES).

The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of primary sampling units (PSUs) from within strata, and systematic sampling of dwelling units (DUs) from the sampled PSUs. A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used: • Where possible, PSU sizes were kept between 100 and 500 DUs; • EAs with fewer than 25 DUs were excluded; • EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type; • Virtual splits were applied to large PSUs: 500 to 999 split into two; 1 000 to 1 499 split into three; and 1 500 plus split into four PSUs; and • Informal PSUs were segmented.

A randomised-probability-proportional-to-size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.

Mode of data collection

Face-to-face [f2f]

Research instrument

The details of the questions included in the GHS questionnaire are covered in 10 sections, each focusing on a particular aspect. Depending on the need for additional information, the questionnaire is adapted on an annual basis. New sections may be introduced on a specific topic for which information is needed or additional questions may be added to existing sections. Likewise, questions that are no longer necessary may be removed.

   A summary of the contents of the GHS 2014 questionnaire

Section Number of Details of each section questions Cover page Household information, response details, field staff information, result codes, etc. Flap 6 Demographic information (name, sex, age, population group, etc.) Section 1 41 Biographical information (education, health, disability, welfare) Section 2 13 Health and general functioning section 3 3 Social grants and social relief Section 4 19 Economic activities Section 5 59 Household information (type of dwelling, ownership of dwelling, electricity, water and sanitation, environmental issues, services, transport, etc.) Section 6 11 Communication, postal services and transport Section 7 15 Health, welfare and food security section 8 28 Households Livelihoods (agriculture, household income sources and expenditure)
Section 9 7 Mortality in the last 12 months Section 10 3 Questions to interviewers
All sections 202 Comprehensive coverage of living conditions and service delivery

The GHS questionnaire has undergone some revisions over time. These changes were primarily the result of shifts in focus of government programmes over time. The 2002–2004 questionnaires were very similar. Changes made to the GHS 2005 questionnaire included additional questions in the education section with a total of 179 questions. Between 2006 and 2008, the questionnaire remained virtually unchanged. For GHS 2009, extensive stakeholder consultation took place during which the questionnaire was reviewed to be more in line with the monitoring and evaluation frameworks of the various government departments. Particular sections that were modified substantially during the review were the sections on education, social development, housing, agriculture, and food security. Even though the number of sections and pages in the questionnaire remained the same, questions in the GHS 2009 were increased from 166 to 185 between 2006 and 2008. Following the introduction of a dedicated survey on Domestic Tourism, the section on tourism was dropped for GHS 2010. Due to a further rotation of questions, particularly the addition of a module on mortality, the GHS 2014 questionnaire contained 202 questions.

Cleaning operations

Historically the GHS used a conservative and hands-off approach to editing. Manual editing, and little if any imputation was done. The focus of the editing process was on clearing skip violations and ensuring that each variable only contains valid values. Very few limits to valid values were set and data were largely released as they were received from the field. With GHS 2009, Stats SA introduced an automated editing and imputation system that was continued for GHSs 2010–2014. The challenge was to remain true, as much as possible, to the conservative approach used prior to GHS 2009, and yet, at the same time, to develop a standard set of rules to be used during editing which could be applied consistently across time. When testing for skip violations and doing automated editing, the following general rules are applied in cases where one question follows the filter question and the skip is violated:

• If the filter question had a missing value, the filter is allocated the value that corresponds with the subsequent question which had a valid value. • If the values of the filter question and subsequent question are

This statistic shows the total number of families in Canada in 2022, distinguished by province or territory. In 2022, around 4.17 million families were living in Ontario.

Abstract

The Living Conditions Monitoring Survey conducted in 2002/2003 was a nation-wide survey. The sample design and sample size used in the survey allow for reliable estimates at province, location (Rural/Urban) and national levels.

The main objectives of the LCMSIII Survey are to: - Monitor the impact of Government policies, programs and donor support on the well being of the Zambian population - Monitor and evaluate the implementation of some of the programs envisaged in the Poverty Reduction Strategy Paper (PRSP) - Monitor poverty and its distribution in Zambia - Provide various users with a set of reliable indicators against which to monitor development - Provide province specific poverty profiles using different poverty lines - Identify vulnerable groups in society and enhance targeting in policy formulation and implementation - Provide data required for developing new national and province specific weights for the Consumer Price Index (CPI) - Provide data required for estimating Gross Domestic Products? (GDP) household final consumption

The Living Conditions Monitoring Survey 2002/2003 collected data on the living conditions of households and persons in the areas of education, health, economic activities and employment, child nutrition, death in the households, income sources, income levels, food production, household consumption expenditure, access to clean and safe water and sanitation, housing and access to various socio-economic facilities and infrastructure such as schools, health facilities, transport, banks, credit facilities, markets, etc.

Geographic coverage

The survey has a nationwide coverage on a sample basis. It covers both rural and urban areas in all the nine provinces. Hence it draws a very big sample size of about 19,600 households.

Analysis unit

• Households
• Individuals

Universe

The eligible household population consisted of all households.Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Monitoring Survey III (LCMSIII) was designed to cover 520 Standard Enumeration Areas (SEAs) or approximately 10,000 non-institutionalized private households residing in both the rural and urban areas of all the nine provinces in Zambia. The survey was carried out for a period of 12 months using a rolling sample. For the purposes of this survey, a survey reference month had 36 days instead of 30 or 31 days, as is the case with calendar months. This implies that the 360 days in a year were divided into 10 cycles of 36 days each. As a result 52 SEAs, which is one-tenth of the 520 SEAs, were covered every cycle countrywide.

Sample Stratification and Allocation The sampling frame used for LCMSIII survey was developed from the 2000 census of population and housing. The frame is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 155 constituencies, which are also divided into wards. Wards consist of Census Supervisory Areas (CSA), which in turn embrace Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the ultimate Primary Sampling Units (PSUs).

In order to have equal precision in the estimates in all the provinces and at the same time take into account variation in the sizes of the provinces, the survey adopted the Square Root sample allocation method, (Lesli Kish, 1987). This approach offers a better compromise between equal and proportional allocation methods in terms of reliability of both combined and separate estimates. The allocation of the sample points (PSUs) to rural and urban strata was almost proportional. The allocated provincial samples were multiples of 10 so as to facilitate the rolling of equal samples during the 10 cycles of data collection.

Sample Selection The LCMSIII survey employed a two-stage stratified cluster sample design whereby during the first stage, 520 SEAs were selected with Probability Proportional to Estimated Size (PPES). The size measure was taken from the frame developed from the 2000 census of population and housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at provincial, residential and national levels.

Selection of Standard Enumeration Areas (SEAs) Please see section 2.5.3 of the Survey Report in External Resources

Selection of Households The LCMSIII survey commenced by listing all the households in the selected SEAs. In the case of rural SEAs, households were stratified and listed according to their agricultural activity status. Therefore, there were four explicit strata created in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMSIII survey, about 7, 5 and 3 households were supposed to be selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were implicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas.

About 15 and 25 households were sampled from rural and urban SEAs, respectively. However, the number of rural households selected in some cases exceeded the desired sample size of 15 households depending on the availability of large scale farming households.

The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies:

Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires will be used in the survey. These are:- 1. The Listing Booklet - to be used for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - to be used for collecting detailed information on all household members.

The Main Household questionnaire was divided into two parts, namely:- 1. Main Questionnaire Part I - used for collecting information on the various aspects of the living conditions of the households. 2. Main Questionnaire Part II - all the information collected using the household expenditure diary was later on transcribed to this questionnaire in aggregates so as to make computer data capturing easy. This part of the questionnaire was also used to collect information on household Income, Non-Farm enterprises and deaths in the households.

Cleaning operations

Data Processing and Analysis: The data from the LCMSIII survey was processed and analyzed using the CSPRO and the Statistical Analysis System (SAS) software respectively. Data entry was done from all the provincial offices with 100 percent verification, whilst data cleaning and analysis was undertaken at CSO's headquarters.