This dataset consists of a summary measure that identifies where countries or other geographic areas sit on the spectrum of development. Expressed on a scale of 0 to 1, SDI (Socio-Demographic Index) is a composite average of the rankings of the incomes per capita, average educational attainment, and fertility rates of all areas in the GBD (Global Burden of Disease) study.

As of January 2025, ** percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were ** percent of ** to ** year olds in the country. Overall, ** percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach *** million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive ***** percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for ** percent of all social media visits in 2023, followed by Pinterest with **** percent of visits. The global social media landscape As of April 2024, **** billion people were social media users, accounting for **** percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of **** percent, followed by Western Europe with **** percent and Eastern Asia **** percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. YouTube, Instagram, and WhatsApp followed, all with *** billion or more MAU.

Emerging new purchasing behaviors have been reflected in the sales trends of dairy products, mainly in cow milk consumption. This study aimed to investigate the preferences of milk purchasers toward different product attributes, by considering both individuals’ socio-demographic characteristics (SD) and milk purchasing habits (PH) as independent variables in the milk consumption model definition. To achieve this objective, a questionnaire was administered to a sample of 1,216 residents in Northwest Italy. The application of the Best-Worst scaling (BWS) methodology to define the purchasers’ declared preferences toward a set of 12 milk attributes, showed that milk origin and expiry date are the most important attributes for milk choice in the decision-making process. The correlation analysis showed that the SD and milk purchasing habits variables affect the definition of stated preferences heterogeneously between the intrinsic, extrinsic, and credence attributes.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls 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 1998 GDHS to minimize 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 1998 GDHS 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 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Data appraisal

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

Note: See detailed tables in APPENDIX C of the survey report.

Abstract

The Thai Demographic and Health Survey (TDHS) was a nationally representative sample survey conducted from March through June 1988 to collect data on fertility, family planning, and child and maternal health. A total of 9,045 households and 6,775 ever-married women aged 15 to 49 were interviewed. Thai Demographic and Health Survey (TDHS) is carried out by the Institute of Population Studies (IPS) of Chulalongkorn University with the financial support from USAID through the Institute for Resource Development (IRD) at Westinghouse. The Institute of Population Studies was responsible for the overall implementation of the survey including sample design, preparation of field work, data collection and processing, and analysis of data. IPS has made available its personnel and office facilities to the project throughout the project duration. It serves as the headquarters for the survey.

The Thai Demographic and Health Survey (TDHS) was undertaken for the main purpose of providing data concerning fertility, family planning and maternal and child health to program managers and policy makers to facilitate their evaluation and planning of programs, and to population and health researchers to assist in their efforts to document and analyze the demographic and health situation. It is intended to provide information both on topics for which comparable data is not available from previous nationally representative surveys as well as to update trends with respect to a number of indicators available from previous surveys, in particular the Longitudinal Study of Social Economic and Demographic Change in 1969-73, the Survey of Fertility in Thailand in 1975, the National Survey of Family Planning Practices, Fertility and Mortality in 1979, and the three Contraceptive Prevalence Surveys in 1978/79, 1981 and 1984.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1987 THADHS is defined as the universe of all women Ever-married women in the reproductive ages (i.e., women 15-49). This covered women in private households on the basis of a de facto coverage definition. Visitors and usual residents who were in the household the night before the first visit or before any subsequent visit during the few days the interviewing team was in the area were eligible. Excluded were the small number of married women aged under 15 and women not present in private households.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE AND ALLOCATION

The objective of the survey was to provide reliable estimates for major domains of the country. This consisted of two overlapping sets of reporting domains: (a) Five regions of the country namely Bangkok, north, northeast, central region (excluding Bangkok), and south; (b) Bangkok versus all provincial urban and all rural areas of the country. These requirements could be met by defining six non-overlapping sampling domains (Bangkok, provincial urban, and rural areas of each of the remaining 4 regions), and allocating approximately equal sample sizes to them. On the basis of past experience, available budget and overall reporting requirement, the target sample size was fixed at 7,000 interviews of ever-married women aged 15-49, expected to be found in around 9,000 households. Table A.I shows the actual number of households as well as eligible women selected and interviewed, by sampling domain (see Table i.I for reporting domains).

THE FRAME AND SAMPLE SELECTION

The frame for selecting the sample for urban areas, was provided by the National Statistical Office of Thailand and by the Ministry of the Interior for rural areas. It consisted of information on population size of various levels of administrative and census units, down to blocks in urban areas and villages in rural areas. The frame also included adequate maps and descriptions to identify these units. The extent to which the data were up-to-date as well as the quality of the data varied somewhat in different parts of the frame. Basically, the multi-stage stratified sampling design involved the following procedure. A specified number of sample areas were selected systematically from geographically/administratively ordered lists with probabilities proportional to the best available measure of size (PPS). Within selected areas (blocks or villages) new lists of households were prepared and systematic samples of households were selected. In principle, the sampling interval for the selection of households from lists was determined so as to yield a self weighting sample of households within each domain. However, in the absence of good measures of population size for all areas, these sampling intervals often required adjustments in the interest of controlling the size of the resulting sample. Variations in selection probabilities introduced due to such adjustment, where required, were compensated for by appropriate weighting of sample cases at the tabulation stage.

SAMPLE OUTCOME

The final sample of households was selected from lists prepared in the sample areas. The time interval between household listing and enumeration was generally very short, except to some extent in Bangkok where the listing itself took more time. In principle, the units of listing were the same as the ultimate units of sampling, namely households. However in a small proportion of cases, the former differed from the latter in several respects, identified at the stage of final enumeration: a) Some units listed actually contained more than one household each b) Some units were "blanks", that is, were demolished or not found to contain any eligible households at the time of enumeration. c) Some units were doubtful cases in as much as the household was reported as "not found" by the interviewer, but may in fact have existed.

Mode of data collection

Face-to-face

Research instrument

The DHS core questionnaires (Household, Eligible Women Respondent, and Community) were translated into Thai. A number of modifications were made largely to adapt them for use with an ever- married woman sample and to add a number of questions in areas that are of special interest to the Thai investigators but which were not covered in the standard core. Examples of such modifications included adding marital status and educational attainment to the household schedule, elaboration on questions in the individual questionnaire on educational attainment to take account of changes in the educational system during recent years, elaboration on questions on postnuptial residence, and adaptation of the questionnaire to take into account that only ever-married women are being interviewed rather than all women. More generally, attention was given to the wording of questions in Thai to ensure that the intent of the original English-language version was preserved.

a) Household questionnaire

The household questionnaire was used to list every member of the household who usually lives in the household and as well as visitors who slept in the household the night before the interviewer's visit. Information contained in the household questionnaire are age, sex, marital status, and education for each member (the last two items were asked only to members aged 13 and over). The head of the household or the spouse of the head of the household was the preferred respondent for the household questionnaire. However, if neither was available for interview, any adult member of the household was accepted as the respondent. Information from the household questionnaire was used to identify eligible women for the individual interview. To be eligible, a respondent had to be an ever-married woman aged 15-49 years old who had slept in the household 'the previous night'.

Prior evidence has indicated that when asked about current age, Thais are as likely to report age at next birthday as age at last birthday (the usual demographic definition of age). Since the birth date of each household number was not asked in the household questionnaire, it was not possible to calculate age at last birthday from the birthdate. Therefore a special procedure was followed to ensure that eligible women just under the higher boundary for eligible ages (i.e. 49 years old) were not mistakenly excluded from the eligible woman sample because of an overstated age. Ever-married women whose reported age was between 50-52 years old and who slept in the household the night before birthdate of the woman, it was discovered that these women (or any others being interviewed) were not actually within the eligible age range of 15-49, the interview was terminated and the case disqualified. This attempt recovered 69 eligible women who otherwise would have been missed because their reported age was over 50 years old or over.

b) Individual questionnaire

The questionnaire administered to eligible women was based on the DHS Model A Questionnaire for high contraceptive prevalence countries. The individual questionnaire has 8 sections: - Respondent's background - Reproduction - Contraception - Health and breastfeeding - Marriage - Fertility preference - Husband's background and woman's work - Heights and weights of children and mothers

The questionnaire was modified to suit the Thai context. As noted above, several questions were added to the standard DHS core questionnaire not only to meet the interest of IPS researchers hut also because of their relevance to the current demographic situation in Thailand. The supplemental questions are marked with an asterisk in the individual questionnaire. Questions concerning the following items were added in the individual questionnaire: - Did the respondent ever

The changing nature of work in adaptation to socio-economic and cultural shifts has been widely addressed. The present research is aimed at identifying the Social Representations of Work (SRW) and the Meaning of Work (MOW) in education professionals settled in Mozambique. Two studies were conducted with 194 participants, including teachers, superior technicians, technical and operational assistants. In the first study a free association task and a professions classification task were employed to explore the SRW according to different socio-demographic profiles. In the second study, the influence of social justice and values on MOW dimensions was accessed through multiple regression analyses. The main findings suggest that conscientiousness and remuneration-related aspects are central to the SRW; that intellectual activities are perceived as more representative of work than manual ones by participants; and that MOW is positively associated with self-transcendence values and perception of procedural justice, but not with perception of distributive justice.

The sample was drawn by means of the central population register (CPR) of Statistics Sweden. CPR contains basic demographic and social data on every individual born on the 15th of any month, any year, and irrespective of place of birth or place of residence. Thus CPR forms, in effect, a 3.3 probability sample of the entire Swedish population. From CPR were drawn all men born in any of the years 1899, 1902, 1905, and so on, down to and including 1923. Thus there are nine birth cohorts, spaced with three-year intervals. Information about occupation in the present (son's) generation was taken from CPR. The method for gathering information on occupation in the previous (father's) generation was a different one. In CPR parish of birth (if in Sweden) and date of birth is always stated. Consequently every person can be located in the copies of the parish birth registers filed in Stockholm, and in these registers the father's occupation is stated (if the father is known). Other data collected from the CPR: place of birth and current place of residence, marital status, age of the parents, and information on income based on the tax assessments.

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

Percentage of persons aged 15 years and over by level of sense of meaning and purpose, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: 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 Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) 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 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize 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 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical 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% 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 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization 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.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of

Economically active and non-active residents of households and those aged 16-64 who are economically active by National Statistics Socio-Economic classification as defined by own occupation. To provide 2001 Census based information about the National Statistics Socio-Economic (NS-SEC) Group of the population within each area as defined by own occupation. Legacy unique identifier: P00032

Abstract Work underwent transformations that changed the values and determinants of their meanings, putting its centrality in check. This research investigates the meanings of work among Brazilians, as well as the influence of demographic and structural elements on this attribution. The meanings of work refer to individual interpretation, influenced by the social context, about work and what it represents. World Values Survey Brazilian’s sample was used. The influence of socioeconomic and structural characteristics was analyzed via structural equation modeling. The model was well adjusted, having a coefficient of determination of .951. Descriptive results indicated high valuation of work and strong perception of it as a social obligation. The SEM results indicated that men attribute higher meaning to work compared to women and that increasing age influences the attribution of meaning to work. Activities with creativity, intellectuality and independence have indirect (via NSE) and negative influence on the perception of work meanings. Analyzes prioritized the articulation between social and economic aspects with the process of meaning of work, a perspective little explored in the Brazilian’s scientific production, but fundamental for a broader understanding of the phenomenon, especially in stratified societies such as Brazil.

88% of European citizens consider a social Europe important to them personally. In addition, 60% of respondents are aware of at least one recent key EU initiative to improve working and living conditions. This includes the Directive to ensure adequate minimum wages, the work-life balance Directive supporting working parents and carers, or the €142.7 billion of EU and national contributions invested under the European Social Fund Plus to improve skills and tackle social exclusion.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Context

[INSEE][1] is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density...), Economic (Salary, Firms by activity / size...) and more.
It can be a great help to observe and measure inequality in the french population.

Content

Four files are in the dataset :

• base_etablissement_par_tranche_effectif : give information on the number of firms in every french town, categorized by size , come from [INSEE][2].
  • CODGEO : geographique code for the town (can be joined with code_insee column from "name_geographic_information.csv')
  • LIBGEO : name of the town (in french)
  • REG : region number
  • DEP : depatment number
  • E14TST : total number of firms in the town
  • E14TS0ND : number of unknown or null size firms in the town
  • E14TS1 : number of firms with 1 to 5 employees in the town
  • E14TS6 : number of firms with 6 to 9 employees in the town
  • E14TS10 : number of firms with 10 to 19 employees in the town
  • E14TS20 : number of firms with 20 to 49 employees in the town
  • E14TS50 : number of firms with 50 to 99 employees in the town
  • E14TS100 : number of firms with 100 to 199 employees in the town
  • E14TS200 : number of firms with 200 to 499 employees in the town
  • E14TS500 : number of firms with more than 500 employees in the town

• name_geographic_information : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )

  • EU_circo : name of the European Union Circonscription
  • code_région : code of the region attached to the town
  • nom_région : name of the region attached to the town
  • chef.lieu_région : name the administrative center around the town
  • numéro_département : code of the department attached to the town
  • nom_département : name of the department attached to the town
  • préfecture : name of the local administrative division around the town
  • numéro_circonscription : number of the circumpscription
  • nom_commune : name of the town
  • codes_postaux : post-codes relative to the town
  • code_insee : unique code for the town
  • latitude : GPS latitude
  • longitude : GPS longitude
  • éloignement : i couldn't manage to figure out what was the meaning of this number

• net_salary_per_town_per_category : salaries around french town per job categories, age and sex

  • CODGEO : unique code of the town
  • LIBGEO : name of the town
  • SNHM14 : mean net salary
  • SNHMC14 : mean net salary per hour for executive
  • SNHMP14 : mean net salary per hour for middle manager
  • SNHME14 : mean net salary per hour for employee
  • SNHMO14 : mean net salary per hour for worker
  • SNHMF14 : mean net salary for women
  • SNHMFC14 : mean net salary per hour for feminin executive
  • SNHMFP14 : mean net salary per hour for feminin middle manager
  • SNHMFE14 : mean net salary per hour for feminin employee
  • SNHMFO14 : mean net salary per hour for feminin worker
  • SNHMH14 : mean net salary for man
  • SNHMHC14 : mean net salary per hour for masculin executive
  • SNHMHP14 : mean net salary per hour for masculin middle manager
  • SNHMHE14 : mean net salary per hour for masculin employee
  • SNHMHO14 : mean net salary per hour for masculin worker
  • SNHM1814 : mean net salary per hour for 18-25 years old
  • SNHM2614 : mean net salary per hour for 26-50 years old
  • SNHM5014 : mean net salary per hour for >50 years old
  • SNHMF1814 : mean net salary per hour for women between 18-25 years old
  • SNHMF2614 : mean net salary per hour for women between 26-50 years old
  • SNHMF5014 : mean net salary per hour for women >50 years old
  • SNHMH1814 : mean net salary per hour for men between 18-25 years old
  • SNHMH2614 : mean net salary per hour for men between 26-50 years old
  • SNHMH5014 : mean net salary per hour for men >50 years old

• population : [demographic][3] information in France per town, age, sex and living mode

  • NIVGEO : geographic level (arrondissement, communes...)
  • CODGEO : unique code for the town
  • LIBGEO : name of the town (might contain some utf-8 errors, this information has better quality name_geographic_information)
  • MOCO : cohabitation mode : [list and meaning available in Data description]
  • AGE80_17 : age category (slice of 5 years) | ex : 0 -> people between 0 and 4 years old
  • SEXE : sex, 1 for men | 2 for women
  • NB : Number of people in the category

• departments.geojson : contains the borders of french departments. From [Gregoire David (github)][4]

These datasets can be merged by : CODGEO = code_insee

Acknowledgements

The entire dataset has been created (and actualized) by INSEE, I just uploaded it on Kaggle after doing some jobs and checks ...

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of American Samoa, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on American Samoa's data products, see the 2020 Island Areas Censuses Technical Documentation..[1] The universe includes anyone born in a foreign country or at sea. It excludes anyone born in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, the U.S. Virgin Islands, Puerto Rico, or the United States (50 states and District of Columbia). It also excludes anyone born in Johnston Atoll, the Midway Islands, Navassa Island, Wake Island, Baker Island, Howland Island, Jarvis Island, Kingman Reef, or Palmyra Atoll..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, American Samoa.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of the U.S. Virgin Islands, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on the U.S. Virgin Islands' data products, see the 2020 Island Areas Censuses Technical Documentation..[1] The universe includes anyone born in a foreign country or at sea. It excludes anyone born in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, the U.S. Virgin Islands, Puerto Rico, or the United States (50 states and District of Columbia). It also excludes anyone born in Johnston Atoll, the Midway Islands, Navassa Island, Wake Island, Baker Island, Howland Island, Jarvis Island, Kingman Reef, or Palmyra Atoll..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, U.S. Virgin Islands.

Abstract copyright UK Data Service and data collection copyright owner.

The variables in the General Household Survey, 2000-2001: Social Capital Teaching Dataset are a subset taken from the full General Household Survey, 2000-2001 (GHS). For that year of the GHS, a social capital 'trailer' was conducted alongside the main survey, which included questions on respondents' local area, fear of crime, participation and trust. The trailer was funded by the Health Development Agency as part of a larger body of work to further understanding of social capital in terms of its meaning, measurement and links to health within the British population. The variables included here are those from the social capital file and others from the main survey, chosen to reflect different dimensions of social capital in relation to a variety of demographic variables, and some outcome variables such as, health, income and employment.

Further information can be found in the Social capital: introductory user guide.

The second edition of the study (released February 2008) replaced the previous edition (released February 2006). The second edition contains a rescaled weight with a mean of 1 (correcting the previous version) and corrects a systematic error in the data which affected the internal consistency of the social capital module variables in relation to those from the main file. Current users of the data are strongly advised to switch to the second edition of the study.

The full General Household Survey series is held at the UK Data Archive under GN 33090.

Main Topics:

Topics covered in this teaching dataset include views about respondents' local area; civic participation, social networks (including contact with friends and relatives) and social participation (involvement with groups and voluntary activities). A range of demographic variables are also included.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to operational changes for military installation enumeration, the 2020 Census of Guam data tables reporting housing, social, and economic characteristics do not include housing units or populations living on Guam's U.S. military installations in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about operational changes and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of Guam, data users should consider the following when using Guam's data products: 1) Data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation. 2) Cells in data tables will display the letter "N" when those data are not statistically reliable. A list of the geographic areas and data tables that will not have data displayed due to data quality concerns can be found in the 2020 Island Areas Censuses Technical Documentation. 3) The Census Bureau advises that data users consider high allocation rates while using the 2020 Census of Guam's available characteristics data. Allocation rates -- a measure of item nonresponse -- are higher than past censuses. Final counts can be adversely impacted when an item's allocation rate is high, and bias can be introduced if the characteristics of the nonrespondents differ from those reported by respondents. Allocation rates for Guam's key population and housing characteristics can be found in the 2020 Island Areas Censuses Technical Documentation. .Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, Guam.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to operational changes for military installation enumeration, the 2020 Census of Guam data tables reporting housing, social, and economic characteristics do not include housing units or populations living on Guam's U.S. military installations in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about operational changes and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of Guam, data users should consider the following when using Guam's data products: 1) Data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation. 2) Cells in data tables will display the letter "N" when those data are not statistically reliable. A list of the geographic areas and data tables that will not have data displayed due to data quality concerns can be found in the 2020 Island Areas Censuses Technical Documentation. 3) The Census Bureau advises that data users consider high allocation rates while using the 2020 Census of Guam's available characteristics data. Allocation rates -- a measure of item nonresponse -- are higher than past censuses. Final counts can be adversely impacted when an item's allocation rate is high, and bias can be introduced if the characteristics of the nonrespondents differ from those reported by respondents. Allocation rates for Guam's key population and housing characteristics can be found in the 2020 Island Areas Censuses Technical Documentation. .[1] Occupation codes are 4-digit codes and are based on the 2018 Standard Occupational Classification (SOC)..[2] Industry codes are 4-digit codes and are based on the 2017 North American Industry Classification System (NAICS)..[3] "Families" consist of a householder and one or more other people related to the householder by birth, marriage, or adoption..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, Guam.

1. Context

A variety of research is currently being developed in order to predict the future of the current covid-19 pandemic. Among all models, the SIR and other compartmental models are the main tool for explainable, extrapolation-robust predictions.

Our idea is to provide the community with an extensive set of socio-demographics (and health) indicators, with the aim of explaining/predicting the variability in the pandemic dynamics. There is already a lot of literature covering these kind of indicators, for instance people study contact matrices, which account for the typical structure of contacts that various segments of the population have (in short: who has contacts with who?).

However we haven't found systematic, Machine-Learning based studies of which indicators most impact the R0 (reproduction number) or other coefficients that are usually fitted to the (time-series) data of infected/hospitalized/deaths/etc. There are some nice initiatives similar to ours that have spawned on kaggle, and have been noticed (see the panel /datasets at https://www.kaggle.com/covid-19-contributions , in particular check out https://www.kaggle.com/jieyingwu/covid19-us-countylevel-summaries#counties.csv).

Given the variety of indicators we provide here, we expect one should be able to predict the department-to-department variations of the empirical coefficient R0, but also of other rates, such has the rate of the process (infected->hospitalized), and to some extent, the rates of (hospitalized->resuscitation), or the rates of ([various states]-> dead). This means the prediction deals both with the spread of the pandemic and the severity of its impact on people's lives and on the health system.

Other data sources, to complete this repo (including worldwide data): https://modcov19.math.cnrs.fr/publicdata/#numeric

2. Content

Here we provide a training+test set of 100 'examples': the 100 departments of France (we had to exclude Mayotte for lack of reliable/available data). They can be considered homogeneous in the sense that all indicators have been recorded in the same way (see below). Likewise, the time-series of hospitalized/in resuscitation/returned home/deaths are measured in a consistent way among the different French departments, since procedures and instructions are very similar everywhere. This is the advantage of remaining in a single country (here, France).

There are two kind of data we provide in the files.

The static data is an aggregation of socio-demographics and health indicators taken from the last couple of years (2016-2019). It is mostly curated by INSEE, the French national statistics institution, but INSEE itself is only a statistics-precessing place, and their data comes from other French agencies and from some surveys INSEE performs itself (like the census data). It comes as a single file, but it is actually the result of our concatenation of several databases (7 of them). Each original database has a separate original source that we provide in the metadata. Sometimes this source itself is a link to the INSEE website, which then details which agencies originally produced the data. For several of these files, we had to preprocess data from the city-level into the larger departmental level. All the original files coming from public institutions, and the codes that we used to pre-process them, are available at this gitlab: https://gitlab.inria.fr/flandes/covid-19-fr-socio-demographics.git

The time-series (or dynamic) data comes from Santé Publique France (also called Agence Nationale de Santé, ANS), one of the French Public Health agencies, which gathers data from hospitals and from the Regional Health Agencies (Agence Régionale de Santé, ARS). An additional time-series is the lockdown (confinement) level time series, that we produced ourselves (from reading the news, basically). This one is a bit particular, in the sense that it makes no sense to predict it, instead the level of lockdown (which decreases starting on May 5th) impacts the epidemic spread, and can be known in advance.

Note that the static data (many features) comes with feature names that start in a precise, regular way. We defined tags such that post-processing would be easy.

column names

• The first tag can be: Nbre, Pop, RateIncome, RateMedian, RatePoverty, Rate[whatever]
• The other tags are (in this order):
  • sex=all, sex=F, sex=H (F=Femme=Woman, H=homme=Man)
  • age=all, agemin=0_agemax=150 (or other values) (arbitrarily, the maximumof agemax is 150 years)
• The suffix is a description of the feature

There are many more features than there are examples in this data set, which means one has to be extremely cautious with over-fitting.

3. Acknowledgements

We thank INSEE and its partners for providing this wealth of data freely, and people wor...