The AIAN Summary File contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The American Indian and Alaska Native Summary File (AIANSF) contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The file also includes housing characteristics, such as tenure (whether a housing unit is owner-occupied or renter- occupied) and age of householder for occupied housing units. Selected aggregates and medians also are provided. A complete listing of subjects in the AIANSF is found in Chapter 3, Subject Locator. The layout of the tables in the AIANSF is similar to that in Summary File 2 (SF 2). These data are presented in 47 population tables (identified with a "PCT") and 14 housing tables (identified with an "HCT") shown down to the census tract level; and 10 population tables (identified with a "PCO") shown down to the county level, for a total of 71 tables. Each table is iterated for the total population, the total American Indian and Alaska Native population alone, the total American Indian and Alaska Native population alone or in combination, and 1,567 detailed tribes and tribal groupings. Tribes or tribal groupings are included on the iterations list if they met a threshold of at least 100 people in the 2010 Census. In addition, the presentation of AIANSF tables for any of the tribes and tribal groupings is subject to a population threshold of 100 or more people in a given geography. That is, if there are fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area in the AIANSF. See Appendix H, Characteristic Iterations, for more information.
The 1995 Kazakstan Demographic and Health Survey (KDHS) is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning and maternal and child health. The 1995 KDHS was the first national level population and health survey in Kazakstan. The purpose of the survey was to provide the Ministry of Health of Kazakstan with information on fertility, reproductive practices of women, maternal care, child health and mortality, child nutrition practices, breastfeeding, nutritional status and anemia. This information is important for understanding the factors that influence the reproductive health of women and the health and survival of infants and young children. It can be used in planning effective policies and programs regarding the health and nutrition of women and their children. This is especially important now during this the time of economic transition which involves virtually all aspects of life for the people of Kazakstan. The survey provides data important to the assessment of the overall demographic situation in the country. It is expected that the findings of the KDHS will become a useful source of information necessary for the ongoing health care reform in Kazakstan.
National
Sample survey data
The 1995 KDHS employed a nationally representative probability sample of women age 15-49. The country was divided into five survey regions. Four survey regions consisted of groups of contiguous oblasts (except the East Kazakstanskaya oblast which is not contiguous). Almaty City constituted a survey region by itself although it is part of the Almatinskaya oblast. The five survey regions were defined as follows:
I) Almaty City 2) South Region: Taldy-Korganskaya, Almatinskaya (except Almaty city), Dzhambylskaya, South Kazakstanskaya, and Kzyl-Ordinskaya 3) West Region: Aktiubinskaya, Mangistauskaya, Atyrauskaya, and West Kazakstanskaya 4) Central Region: Semipalatinskaya, Zhezkazganskaya, and Tourgaiskaya 5) North and East Region: East Kazakstanskaya, Pavlodarskaya, Karagandinskaya, Akmolinskaya, Kokchetauskaya, North Kazakstanskaya, and Koustanaiskaya
It is important to note that the oblast composition of regions outside of Almaty City was determined on the basis of geographic proximity, and in order to achieve similarity with respect to reproductive practices within regions. The South and West Regions are comprised of oblasts which traditionally have a high proportion of Kazak population and high fertility levels. The Central Region contains three oblasts in which the fertility level is similar to the national average. The North and East Region contains seven oblasts situated in northern Kazakstan in which a relatively high proportion of the population is of Russian origin, and the fertility level is lower than the national average.
In Almaty City, the sample for the 1995 KDHS was selected in two stages. In the first stage, 40 census counting blocks were selected with equal probability from the 1989 list of census counting blocks. A complete listing of the households in the selected counting blocks was carried out. The lists of households served as the frame for second-stage sampling; i.e., the selection of the households to be visited by the KDHS interviewing teams. In each selected household, women age 15-49 were eligible to be interviewed.
In the rural areas, the primary sampling units (PSUs) were the raions which were selected with probability proportional to size, the size being the 1993 population published by Goskomstat (1993). At the second stage, one village was selected in each selected raion, from the 1989 Registry of Villages. This resulted in 50 rural clusters being selected. At the third stage, households were selected in each cluster following the household listing operation as in Almaty City.
In the urban areas other than Almaty City, the PSUs were the cities and towns themselves. In the second stage, one health block was selected from each town except in self-representing cities (large cities that were selected with certainty) where more than one health block was selected. The selected health blocks were segmented prior to the household listing operation which provided the household lists for the third stage selection of households. In total, 86 health blocks were selected.
On average, 22 households were selected in each urban cluster, and 33 households were selected in each rural cluster. It was expected that the sample would yield interviews with approximately 4,000 women between the ages of 15 and 49.
Note: See detailed description of sample design in APPENDIX B of the survey report.
Face-to-face
Two questionnaires were used for the 1995 KDHS: the Household Questionnaire and the Individual Questionnaire. The questionnaires were based on the model survey instruments developed in the DHS program. They were adapted to the data needs of Kazakhstan during consultations with specialists in the areas of reproductive health, child health and nutrition in Kazakhstan.
The Household Questionnaire was used to enumerate all usual members and visitors in tile sample households and to collect information relating to the socioeconomic position of a household. In the: first part of the Household Questionnaire, information was collected on age, sex, educational attainment, marital status, and relationship to the head of household of each person listed as a household member or visitor. A primary objective of the first part of the Household Questionnaire was to identify women who were eligible for the individual interview. In the second part of the Household Questionnaire, questions were included on the dwelling unit, such as the number of rooms, the flooring material, the source of water, the type of toilet facilities, and on the availability of a variety of consumer goods.
The Individual Questionnaire was used to collect information from women age 15-49. These women were asked questions on the following major topics: - Background characteristics - Pregnancy history - Outcome of pregnancies and antenatal care - Child health and nutrition practices - Child immunization and episodes of diarrhea and respiratory illness - Knowledge and use of contraception - Marriage and fertility preferences - Husband's background and woman's work - Anthropometry of children and mothers - Hemoglobin measurement of women and children
One of the major efforts of the 1995 KDHS was testing women and children for iron-deficiency anemia. Testing was done by measuring hemoglobin levels in the blood using the Hemocue technique. Before collecting the blood sample, each woman was asked to sign a consent form giving permission for the collection of a finger-stick blood droplet from herself and her children. Results of anemia testing were kept confidential (as are all KDHS data); however, strictly with the consent of respondents, local health care facilities were informed of women and children who had severely low levels of hemoglobin (less than 7 g/dl).
Questionnaires were returned to the Institute of Nutrition in Almaty for data processing. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. The few questions which had not been pre-coded (e.g., occupation, type of chronic disease) were coded at this time. Data were then entered and edited on microcomputers using the ISSA (Integrated System for Survey Analysis) package, with the data entry software translated into Russian. Office editing and data entry activities began in May 1995 (i.e., the same time that fieldwork started) and were completed in September 1995.
A total of 4,480 households were selected in the sample, of which 4,241 were occupied at the time of fieldwork. The main reason for the difference was that some dwelling units which were occupied at the time of the household listing operation were either vacant or the household members were away for an extended period at the time of interviewing. Of the 4,241 occupied households, 4,178 were interviewed, yielding a household response rate of 99 percent.
In the interviewed households, 3,899 women were eligible for the individual interview (i.e., all women 15-49 years of age who were either usual residents or visitors who had spent the previous night in the household). Interviews were successfully completed with 3,771 of these women, yielding a response rate of 97 percent. The principal reason for non-response was the failure to find an eligible woman at home after repeated visits to the household. The overall response rate for the survey--the product of the household and the individual response rates--was 95 percent.
Note: See summarized response rates by place of residence in Table 1.1 of the survey report .
The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the KDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate
This layer shows age and sex demographics. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to the percent of the population ages 18 to 24 years old. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the filter settings. Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges)Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Current Vintage: 2018-2022ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyDate of Census update: Dec 15, 2023Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov
U.S. Government Workshttps://www.usa.gov/government-works
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These are the data for displayed in the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2024, but display data from 2022 and 2023.
Most data are from the 2022 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for:
Total Population - City of Austin Planning Department (2023) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (2022) Occupied Housing Units - City of Austin Planning Department (2023) Median Home Closing Price - Austin Board of Realtors (2023) Average Monthly Rent - Austin Investor Interests (Q4 2023) Income Restricted Units - City of Austin Affordable Housing Inventory Housing Units-City of Austin Planning Department (2023) Population Density - Esri Updated Demographics Daytime Population Density - Esri Updated Demographics Selected Land Use Percentages - City of Austin Land Use Inventory Transit Stops - Capital Metro (2023)
City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates.
More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics.
These profiles are updated annually.
City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq
The Bangladesh Demographic and Health Survey (BDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health.
The main objective of this survey is to provide policy-makers and program managers in health and family planning with detailed information on fertility and family planning, childhood mortality, maternal and child health, nutritional status of children and mothers, and awareness of HIV/AIDS. The survey consisted of two parts: a household-level survey of women and men and a community survey around the sample points from which the households were selected. Preparations for the survey started in mid-2003 and the fieldwork was carried out between January and May 2004.The urvey was conducted under the authority of the National Institute for Population Research and Training (NIPORT) of the Ministry of Health and Family Welfare. The survey was implemented by Mitra and Associates, a Bangladeshi research firm located in Dhaka. ORC Macro of Calverton, Maryland, provided technical assistance to the project as part of its international Demographic and Health Surveys program, and financial assistance was provided by the U.S. Agency for International Development (USAID)/Bangladesh.
In general, the objectives of the BDHS are to: - Assess the overall demographic situation in Bangladesh - Assist in the evaluation of the population and health programs in Bangladesh - Advance survey methodology.
More specifically, the objective of the BDHS survey is to provide up-to-date information on fertility and childhood mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; nutrition levels; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in the country.
National
Sample survey data
The sample for the 2004 BDHS covered the entire population residing in private dwellings units in the country. Administratively, Bangladesh is divided into six divisions. In turn, each division is divided into zilas, and in turn each zila into upazilas. Each urban area in the upazila is divided into wards, and into mahallas within the ward; each rural area in the upazila is divided into union parishads (UP) and into mouzas within the UPs. The urban areas were stratified into three groups, i) Standard metropolitan areas, ii) Municipality areas, and iii) Other urban areas. These divisions allow the country as a whole to be easily separated into rural and urban areas.
For the 2001 census, subdivisions called enumeration areas (EAs) were created based on a convenient number of dwellings units. Because sketch maps of EAs were accessible, EAs were considered suitable to use as primary sampling units (PSUs) for the 2004 BDHS. In each division, the list of EAs constituted the sample frame for the 2004 BDHS survey.
A target number of completed interviews with eligible women for the 2004 BDHS was set at 10,000, based on information from the 1999-2000 BDHS. The 2004 BDHS sample is a stratified, a multistage cluster sample consisting of 361 PSUs, 122 in the urban area and 239 in the rural area. After the target sample was allocated to each group area according to urban and rural areas, the number of PSUs was calculated in terms of an average of 28 completed interviews of eligible women per PSU (or an average of 30 selected households per PSU).
Mitra and Associates conducted a household listing operation in all the sample points from 3 October 2003 to 15 December 2003. A systematic sample of 10,811 households was then selected from these lists. All ever-married women age 10-49 in the selected households were eligible respondents for the women's questionnaire. For the men's survey, 50 percent of the selected households were chosen through systematic sampling. Interviewers interviewed one randomly selected man, regardless of marital status, in the age group 15-54, from each of the selected households. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 4,400 men age 15-54.
Note: See detailed in APPENDIX A of the survey report.
Data collected for women 10-49, indicators calculated for women 15-49.
Face-to-face
The BDHS used a Household Questionnaire, a Women’s Questionnaire, a Men’s Questionnaire, and a Community Questionnaire. The contents of these questionnaires was based on MEASURE DHS+ model questionnaire. These model questionnaires were adapted for use in Bangladesh during a series of meetings with the Technical Task Force, which consisted of representatives from NIPORT, Mitra and Associates, USAID/Dhaka, ICDDR,B’s Center for Health and Population Research, Bangladesh, Pathfinder/Dhaka, and ORC Macro. Draft questionnaires were then circulated to other interested groups and were reviewed by the BDHS Technical Review Committee. The questionnaires were developed in English and then translated into and printed in Bangla. In addition, two versions of a Verbal Autopsy Questionnaire were used. One version was for neonatal deaths (0-28 days old at death) and the other was for deaths among older children (age 29 days to 5 years at death). The verbal autopsy instruments were developed using the previous two BDHS verbal autopsy surveys, the WHO verbal autopsy questionnaire, and the instrument used since 2003 in the Matlab Health and Demographic Surveillance System.
The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods. The arsenic level of the water used by households for drinking was also tested. The Household Questionnaire was also used to record the heights and weights of all children under six years of age.
The Women’s Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.) - Reproductive history - Knowledge and use of family planning methods - Antenatal and delivery care - Breastfeeding and weaning practices - Vaccinations and health of children under age five - Marriage - Fertility preferences - Husband’s background and respondent’s work - Awareness of AIDS and other sexually transmitted diseases - Causes of deaths of children under five years of age
The Men’s Questionnaire was used to collect information from men age 15-54 whether ever married or not. The men were asked questions on the following topics: - Background characteristics (including respondent’s work) - Health and life style (illness, use of tobacco) - Marriage and sexual activity - Participation in reproductive health care - Awareness of AIDS and other sexually transmitted diseases - Attitudes on women’s decision making roles - Domestic violence
The Community Questionnaire was completed for each sample cluster and included questions about the existence of development organizations in the community and the availability and accessibility of health and family planning services.
The Verbal Autopsy Questionnaire was used for collection of open-ended information including narrative stories on the following topics: - Identification including detailed address of respondent - Informed consent - Detailed age description of deceased child - Information about caretaker or respondent of deceased child - Detailed birth and delivery information - Open-ended section allowing the respondent to provide a narrative history - Maternal history including questions on prenatal care, labor and delivery, and obstetrical complications - Information about accidental deaths - Detailed signs and symptoms preceding death - Treatment module and information on direct, underlying - Contributing causes of death from the death certificate, if available.
All questionnaires for the BDHS were periodically returned to Dhaka for data processing at Mitra and Associates. The processing of the data collected began shortly after the fieldwork commenced. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing inconsistencies found by the computer programs. The data were processed on six microcomputers working in double shifts and carried out by 10 data entry operators and two data entry supervisors. The concurrent processing of the data was an advantage since the quality control teams were able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. Data processing commenced on 12 January 2004 and was completed by 24 June 2004.
A total of 10,811 households were selected for the sample; 10,523 were occupied, of which 10,500 were
The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm. These microdata files present detailed expenditure and income data for the Diary component of the CE for 2002. They include weekly expenditure (EXPD) and annual income (DTBD) files. The data in EXPD and DTBD files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2002 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2002. A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2002".
Consumer Units
Sample survey data [ssd]
Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2002 sample is composed of 105 areas. The design classifies the PSUs into four categories: • 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.
The sampling frame (that is, the list from which housing units were chosen) for the 2002 survey is generated from the 1990 Population Census 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (ED's) from the Census that fail to meet the criterion for good addresses for new construction, and all ED's in nonpermit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year. During the last 6 weeks of the year, however, the Diary Survey sample is supplemented to twice its normal size to increase the reporting of types of expenditures unique to the holidays.
STATE IDENTIFIER Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data. To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.
INTERPRETING THE DATA Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially. Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs. CUs with members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.
Computer Assisted Personal Interview [capi]
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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United States US: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 1.670 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 1.670 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.
The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women
National coverage
The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.
Sample survey data [ssd]
The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.
The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.
The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.
The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.
For further details on sample selection, see Appendix A of the final report.
Face-to-face [f2f]
Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.
All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.
Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.
In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).
Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Zambia Demographic and Health Survey (ZDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018
Note: Data quality tables are presented in APPENDIX C of the report.
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Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -0.610 % in 2015. Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -0.610 % from Dec 2015 (Median) to 2015, with 1 observations. Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.
The 2016 Timor-Leste Demographic and Health Survey (TLDHS) was implemented by the General Directorate of Statistics (GDS) of the Ministry of Finance in collaboration with the Ministry of Health (MOH). Data collection took place from 16 September to 22 December, 2016.
The primary objective of the 2016 TLDHS project is to provide up-to-date estimates of basic demographic and health indicators. The TLDHS provides a comprehensive overview of population, maternal, and child health issues in Timor-Leste. More specifically, the 2016 TLDHS: • Collected data at the national level, which allows the calculation of key demographic indicators, particularly fertility, and child, adult, and maternal mortality rates • Provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality • Measured the levels of contraceptive knowledge and practice • Obtained data on key aspects of maternal and child health, including immunization coverage, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care, including antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and collected anthropometric measures to assess nutritional status in children, women, and men • Tested for anemia in children, women, and men • Collected data on the knowledge and attitudes of women and men about sexually-transmitted diseases and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviors and condom use), and coverage of HIV testing and counseling • Measured key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • Collected information on the extent of disability • Collected information on non-communicable diseases • Collected information on early childhood development • Collected information on domestic violence • The information collected through the 2016 TLDHS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.
National
The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.
Sample survey data [ssd]
The sampling frame used for the TLDHS 2016 survey is the 2015 Timor-Leste Population and Housing Census (TLPHC 2015), provided by the General Directorate of Statistics. The sampling frame is a complete list of 2320 non-empty Enumeration Areas (EAs) created for the 2015 population census. An EA is a geographic area made up of a convenient number of dwelling units which served as counting units for the census, with an average size of 89 households per EA. The sampling frame contains information about the administrative unit, the type of residence, the number of residential households and the number of male and female population for each of the EAs. Among the 2320 EAs, 413 are urban residence and 1907 are rural residence.
There are five geographic regions in Timor-Leste, and these are subdivided into 12 municipalities and special administrative region (SAR) of Oecussi. The 2016 TLDHS sample was designed to produce reliable estimates of indicators for the country as a whole, for urban and rural areas, and for each of the 13 municipalities. A representative probability sample of approximately 12,000 households was drawn; the sample was stratified and selected in two stages. In the first stage, 455 EAs were selected with probability proportional to EA size from the 2015 TLPHC: 129 EAs in urban areas and 326 EAs in rural areas. In the second stage, 26 households were randomly selected within each of the 455 EAs; the sampling frame for this household selection was the 2015 TLPHC household listing available from the census database.
For further details on sample design, see Appendix A of the final report.
Face-to-face [f2f]
Four questionnaires were used for the 2016 TLDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Timor-Leste.
The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two staff who took part in the main fieldwork training. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2016 and completed in February 2017.
A total of 11,829 households were selected for the sample, of which 11,660 were occupied. Of the occupied households, 11,502 were successfully interviewed, which yielded a response rate of 99 percent.
In the interviewed households, 12,998 eligible women were identified for individual interviews. Interviews were completed with 12,607 women, yielding a response rate of 97 percent. In the subsample of households selected for the men’s interviews, 4,878 eligible men were identified and 4,622 were successfully interviewed, yielding a response rate of 95 percent. Response rates were higher in rural than in urban areas, with the difference being more pronounced among men (97 percent versus 90 percent, respectively) than among women (98 percent versus 94 percent, respectively). The lower response rates for men were likely due to their more frequent and longer absences from the household.
The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TLDHS 2016 to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TLDHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the TLDHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the TLDHS 2016 is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends
See details of the data quality tables in Appendix C of the survey final report.
The 2011 Ethiopia Demographic and Health Survey (EDHS) was conducted by the Central Statistical Agency (CSA) under the auspices of the Ministry of Health.
The principal objective of the 2011 Ethiopia Demographic and Health Survey (EDHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, use of maternal and child health services, knowledge of HIV/AIDS, and prevalence of HIV/AIDS and anaemia. The specific objectives are these: - Collect data at the national level that will allow the calculation of key demographic rates; - Analyse the direct and indirect factors that determine fertility levels and trends; - Measure the levels of contraceptive knowledge and practice of women and men by family planning method, urban-rural residence, and region of the country; - Collect high-quality data on family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under ge five, and maternity care indicators, including antenatal visits and assistance at delivery; - Collect data on infant and child mortality and maternal mortality; - Obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to assess the nutritional status of women and children; - Collect data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluate patterns of recent behaviour regarding condom use; - Conduct haemoglobin testing on women age 15-49 and children 6-59 months to provide information on the prevalence of anaemia among these groups; - Carry out anonymous HIV testing on women and men of reproductive age to provide information on the prevalence of HIV.
This information is essential for informed policy decisions, planning, monitoring, and evaluation of programmes on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Agency to plan, conduct, process, and analyse data from complex national population and health surveys.
Moreover, the 2011 EDHS provides national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries and to Ethiopia’s two previous DHS surveys, conducted in 2000 and 2005. Data collected in the 2011 EDHS add to the large and growing international database of demographic and health indicators.
The survey was intentionally planned to be fielded at the beginning of the last term of the MDG reporting period to provide data for the assessment of the Millennium Development Goals (MDGs).
The survey interviewed a nationally representative population in about 18,500 households, and all women age 15-49 and all men age 15-59 in these households. In this report key indicators relating to family planning, fertility levels and determinants, fertility preferences, infant, child, adult and maternal mortality, maternal and child health, nutrition, women’s empowerment, and knowledge of HIV/AIDS are provided for the nine regional states and two city administrations. In addition, this report also provides data by urban and rural residence at the country level.
Major stakeholders from various government, non-government, and UN organizations have been involved and have contributed in the technical, managerial, and operational aspects of the survey.
A nationally representative sample of 17,817 households was selected.
All women 15-49 who were usual residents or who slept in the selected households the night before the survey were eligible for the survey. A male survey was also conducted. All men 15-49 who were usual residents or who slept in the selected households the night before the survey were eligible for the male survey.
Sample survey data
The sample for the 2011 EDHS was designed to provide population and health indicators at the national (urban and rural) and regional levels. The sample design allowed for specific indicators, such as contraceptive use, to be calculated for each of Ethiopia's 11 geographic/administrative regions (the nine regional states and two city administrations). The 2007 Population and Housing Census, conducted by the CSA, provided the sampling frame from which the 2011 EDHS sample was drawn.
Administratively, regions in Ethiopia are divided into zones, and zones, into administrative units called weredas. Each wereda is further subdivided into the lowest administrative unit, called kebele. During the 2007 census each kebele was subdivided into census enumeration areas (EAs), which were convenient for the implementation of the census. The 2011 EDHS sample was selected using a stratified, two-stage cluster design, and EAs were the sampling units for the first stage. The sample included 624 EAs, 187 in urban areas and 437 in rural areas.
Households comprised the second stage of sampling. A complete listing of households was carried out in each of the 624 selected EAs from September 2010 through January 2011. Sketch maps were drawn for each of the clusters, and all conventional households were listed. The listing excluded institutional living arrangements and collective quarters (e.g., army barracks, hospitals, police camps, and boarding schools). A representative sample of 17,817 households was selected for the 2011 EDHS. Because the sample is not self-weighting at the national level, all data in this report are weighted unless otherwise specified.
In the Somali region, in 18 of the 65 selected EAs listed households were not interviewed for various reasons, such as drought and security problems, and 10 of the 65 selected EAs were not listed due to security reasons. Therefore, the data for Somali may not be totally representative of the region as a whole. However, national-level estimates are not affected, as the percentage of the population in the EAs not covered in the Somali region is proportionally very small.
SAMPLING FRAME
The sampling frame used for 2011 EDHS is the Population and Housing Census (PHC) conducted in 2007 provided by the Central Statistical Agency (CSA, 2008). CSA has an electronic file consisting of 81,654 Enumeration Areas (EA) created for the 2007 census in 10 of its 11 geographic regions. An EA is a geographic area consisting of a convenient number of dwelling units which served as counting unit for the census. The frame file contains information about the location, the type of residence, and the number of residential households for each of the 81,654 EAs. Sketch maps are also available for each EA which delimitate the geographic boundaries of the EA. The 2007 PHC conducted in the Somali region used a different methodology due to difficulty of access. Therefore, the sampling frame for the Somali region is in a different file and in different format. Due to security concerns in the Somali region, in the beginning it was decided that 2011 EDHS would be conducted only in three of nine zones in the Somali region: Shinile, Jijiga, and Liben, same as in the 2000 and 2005 EDHS. However, a later decision was made to include three other zones: Afder, Gode and Warder. This was the first time that these three zones were included in a major nationwide survey such as the 2011 EDHS. The sampling frame for the 2011 EDHS consists of a total of 85,057 EAs.
The sampling frame excluded some special EAs with disputed boundaries. These EAs represent only 0.1% of the total population.
Ethiopia is divided into 11 geographical regions. Each region is sub-divided into zones, each zone into Waredas, each Wareda into towns, and each town into Kebeles. Among the 85,057 EAs, 17,548 (21 percent) are in urban areas and 67,509 (79 percent) are in rural areas. The average size of EA in number of households is 169 in an urban EA and 180 in a rural EA, with an overall average of 178 households per EA. Table A.2 shows the distributions of households in the sampling frame, by region and residence. The data show that 81 percent of the Ethiopia’s households are concentrated in three regions: Amhara, Oromiya and SNNP, while 4 percent of all households are in the five smallest regions: Afar, Benishangul-Gumuz, Gambela, Harari and Dire Dawa.
Face-to-face [f2f]
The 2011 EDHS used three questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from model survey instruments developed for the MEASURE DHS project to reflect the population and health issues relevant to Ethiopia. Issues were identified at a series of meetings with the various stakeholders. In addition to English, the questionnaires were translated into three major languages—Amharigna, Oromiffa, and Tigrigna.
The Household Questionnaire was used to list all the usual members and visitors of selected households. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The data on the age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various consumer
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Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data was reported at 13.800 % in Dec 2018. This records a decrease from the previous number of 14.400 % for Sep 2018. Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data is updated quarterly, averaging 15.400 % from Dec 2011 (Median) to Dec 2018, with 29 observations. The data reached an all-time high of 16.300 % in Mar 2014 and a record low of 13.800 % in Dec 2018. Russia Population: Percent of Total: Household Income per Capita: 14000.1 - 19000 RUB per Month data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA013: Population by Average Household Income.
Euromaidan (or the Revolution of Dignity) is the name given to the protests of late November 2013 - February 2014 that began after the Ukrainian government announced that it was suspending its course towards European integration. KIIS, in collaboration with DIF and with funding from the International Renaissance Foundation, conducted three polls among participants of the Maidan protests in Kyiv: • The first poll of Maidan participants was held on weekdays, the 7th and 8th of December 2013. A total of 1037 respondents were interviewed using a method that provides a random sample of Maidan participants. • The second poll of Maidan, conducted when it had become a stationary camp (Maidan-camp), took place on December 20, 2013 (Friday). A total of 515 persons were interviewed, representing all stationary points on the square. • On February 3, 2014 (Monday), the third survey of Maidan participants was conducted. In total, 502 people were interviewed at all stationary points of Maidan (tents, House of Trade Unions, the building of the Kyiv City State Administration, October Palace, Ukrainian House, and others), according to a sample that provided proportional coverage of Maidan participants. The topics addressed in these polls include the motivations and demands of the protesters, their willingness to continue participating in protests, the conditions under which they would leave, as well as the socio-demographic profile of participants. The purpose of the survey was to discover whether and what changes had happened among Maidan participants at different stages, exploring shifts in the social and demographic structure, as well as changes in views and demands. After the end of Euromaidan and a period of time passed, KIIS incorporated a question into its nationwide polls with the objective of understanding the public's perception of those protests. The question was: "Please tell me which of the following two statements is closer to your opinion? Euromaidan was... 1) a people's protest in support of Ukraine's European path of development, against government corruption, and violence from representatives of law enforcement OR 2) a struggle for power by anti-Russian, nationalist forces supported by Western intelligence services". This question was designed and employed as part of an assessment of the effectiveness of Russian propaganda; therefore, the second option deliberately repeats one of the Russian propaganda theses. In the period 2015-2018, this question was included in four public opinion polls. Each of the polls was carried out on a sample representative of Ukraine's adult population (aged 18 and older), with an average sample size of about 2,000 respondents. For ease of analysis, the data from these polls was merged into one dataset, with a total of 8,119 respondents. The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement). These data can be used to analyse the transformations of retrospective perceptions of the Euromaidan events among the population of Ukraine, particularly as an indicator of the influence of various narratives across different territorial and socio-demographic groups.
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The proportion of adults with a General Health Questionnaire GHQ12 score of 4 and over. To reduce levels of sickness and ill health.Improving the life outcomes for those people with mental health problems continues to be a priority for the government. To this end it is important to monitor the percentage of people who suffer from poor mental health, and to explore how this proportion varies across sections of society. Legacy unique identifier: P00863
This layer 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 web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.
In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).
Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.
As of March 2024, NASCAR fans in the United States tended to be younger on average than the general population, with most fans being aged under 44. Only 16 percent of NASCAR fans were aged 65 and over, compared to 21 percent of the wider U.S. population.
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Estimate, Median Age by Sex, Total Population (5-year estimate) in Scioto County, OH was 40.60000 Years of Age in January of 2023, according to the United States Federal Reserve. Historically, Estimate, Median Age by Sex, Total Population (5-year estimate) in Scioto County, OH reached a record high of 40.60000 in January of 2022 and a record low of 37.90000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate, Median Age by Sex, Total Population (5-year estimate) in Scioto County, OH - last updated from the United States Federal Reserve on August of 2025.
In 2023, the life expectancy at birth of Vietnamese men was estimated at 71.1 years. Meanwhile, Vietnamese women tend to live longer, around 76.5 years on average. In that year, the general life expectancy of the Vietnamese population was around 73.3 years.
The AIAN Summary File contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The American Indian and Alaska Native Summary File (AIANSF) contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The file also includes housing characteristics, such as tenure (whether a housing unit is owner-occupied or renter- occupied) and age of householder for occupied housing units. Selected aggregates and medians also are provided. A complete listing of subjects in the AIANSF is found in Chapter 3, Subject Locator. The layout of the tables in the AIANSF is similar to that in Summary File 2 (SF 2). These data are presented in 47 population tables (identified with a "PCT") and 14 housing tables (identified with an "HCT") shown down to the census tract level; and 10 population tables (identified with a "PCO") shown down to the county level, for a total of 71 tables. Each table is iterated for the total population, the total American Indian and Alaska Native population alone, the total American Indian and Alaska Native population alone or in combination, and 1,567 detailed tribes and tribal groupings. Tribes or tribal groupings are included on the iterations list if they met a threshold of at least 100 people in the 2010 Census. In addition, the presentation of AIANSF tables for any of the tribes and tribal groupings is subject to a population threshold of 100 or more people in a given geography. That is, if there are fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area in the AIANSF. See Appendix H, Characteristic Iterations, for more information.