As of the first of January 2024, roughly *** million residents in Spain came from Africa, and more than half of them were male. There were approximately *** percent more female residents original from South America than males from the same region.

In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

Population, female (% of total population) in World was reported at 49.72 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The estimated population of the U.S. was approximately 334.9 million in 2023, and the largest age group was adults aged 30 to 34. There were 11.88 million males in this age category and around 11.64 million females. Which U.S. state has the largest population? The population of the United States continues to increase, and the country is the third most populous in the world behind China and India. The gender distribution has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the highest population in 2023. The U.S. population by race and ethnicity The United States is well known the world over for having a diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

In 2023, over 313,000 foreigners called the Grand Duchy of Luxembourg home. The largest group of foreign inhabitants came from Portugal, at just over 92,000. There were almost twice as many Portuguese living in Luxembourg as any of other nationality. By comparison: the second largest group of foreigners, the French, numbered around 49,100 individuals. In total, Luxembourg had 660,800 thousand inhabitants that year.

Nearly half of Luxembourg’s population is of foreign origin

In 2021, the share of foreign nationals living in the country reached 47.2 percent. This was a slight decrease in comparison to the previous year, when the share of foreigners was at 47.4 percent. In the last decade, Luxembourg’s foreign population share never dropped below 43 percent.

Foreign population by age and gender

Luxembourg’s foreign population counted approximately as many men as women, with men outnumbering women by just 8.9 thousand. The average foreigner was roughly between 25 and 55 years old, with those aged 35-39 making up the largest age group, at just under 30 thousand.

Abstract

From 2014 to 2015, with the aim of collecting data to monitor progress across Rwanda’s health programs and policies, the Government of Rwanda (GOR) conducted the Rwanda Demographic and Health Survey (RDHS) through the Ministry of Health (MOH) and the National Institute of Statistics of Rwanda (NISR) with the members of the national steering committee to the DHS and the technical assistance of ICF International.

The main objectives of the 2014-15 RDHS were to: • Collect data at the national level to calculate essential demographic indicators, especially fertility and infant and child mortality, and analyze the direct and indirect factors that relate to levels and trends in fertility and child mortality • Measure levels of knowledge and use of contraceptive methods among women and men • Collect data on family health, including immunization practices; prevalence and treatment of diarrhea, acute upper respiratory infections, and fever among children under age 5; antenatal care visits; assistance at delivery; and postnatal care • Collect data on knowledge, prevention, and treatment of malaria, in particular the possession and use of treated mosquito nets among household members, especially children under age 5 and pregnant women • Collect data on feeding practices for children, including breastfeeding • Collect data on the knowledge and attitudes of women and men regarding sexually transmitted infections (STIs) and HIV and evaluate recent behavioral changes with respect to condom use • Collect data for estimation of adult mortality and maternal mortality at the national level • Take anthropometric measurements to evaluate the nutritional status of children, men, and women • Assess the prevalence of malaria infection among children under age 5 and pregnant women using rapid diagnostic tests and blood smears • Estimate the prevalence of HIV among children age 0-14 and adults of reproductive age • Estimate the prevalence of anemia among children age 6-59 months and adult women of reproductive age • Collect information on early childhood development • Collect information on domestic violence

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 age 15-49 years and all men age 15-59 who were usual residents in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sampling frame used for the 2014-15 RDHS was the 2012 Rwanda Population and Housing Census (RPHC). The sampling frame consisted of a list of enumeration areas (EAs) covering the entire country, provided by the National Institute of Statistics of Rwanda, the implementing agency for the RDHS. An EA is a natural village or part of a village created for the 2012 RPHC; these areas served as counting units for the census.

The 2014-15 RDHS followed a two-stage sample design and was intended to allow estimates of key indicators at the national level as well as for urban and rural areas, five provinces, and each of Rwanda's 30 districts (for some limited indicators). The first stage involved selecting sample points (clusters) consisting of EAs delineated for the 2012 RPHC. A total of 492 clusters were selected, 113 in urban areas and 379 in rural areas.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected EAs from July 7 to September 6, 2014, and households to be included in the survey were randomly selected from these lists. Twenty-six households were selected from each sample point, for a total sample size of 12,792 households. However, during data collection, one of the households was found to actually be two households, which increased the total sample to 12,793. Because of the approximately equal sample sizes in each district, the sample is not self-weighting at the national level, and weighting factors have been added to the data file so that the results will be proportional at the national level.

All women age 15-49 who were either permanent residents of the household or visitors who stayed in the household the night before the survey were eligible to be interviewed. In half of the households, all men age 15-59 who either were permanent household residents or were visiting the night before the survey were eligible to be interviewed.

In the subsample of households not selected for the male survey, anemia and malaria testing were performed among eligible women who consented to being tested. With the parent's or guardian's consent, children aged 6-59 months were tested for anemia and malaria in this subsample. Height and weight information was collected from eligible women, and children (age 0-5) in the same subsample. In the subsample of households selected for male survey, blood spot samples were collected for laboratory testing of HIV from eligible women and men who consented. Height and weight information was collected from eligible men. In one-third of the same subsample (or 15 percent of the entire sample), blood spot samples were collected for laboratory testing of children age 0-14 for HIV.

The domestic violence module was implemented in the households selected for the male survey: The domestic violence module for men was implemented in 50 percent of the household selected for male survey and domestic violence for women was conducted in the remaining 50 percent of household selected for male survey (or 25 percent of the entire sample, each).

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three types of questionnaires were used in the 2014-15 RDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. They are based on questionnaires developed by the worldwide DHS Program and on questionnaires used during the 2010 RDHS. To reflect relevant issues in population and health in Rwanda, the questionnaires were adapted during a series of technical meetings with various stakeholders from government ministries and agencies, nongovernmental organizations, and international donors. The questionnaires were translated from English into Kinyarwanda.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households as well as to identify women and men eligible for individual interviews. Basic information was collected on the characteristics of each person listed, including relationship to the head of the household, sex, residence status, age, and marital status along with survival status of children’s parents, education, birth registration, health insurance coverage, and tobacco use.

The Woman’s Questionnaire was administered to all women age 15-49 living in the sampled households.

The Man’s Questionnaire was administered to all men age 15-59 living in every second household in the sample. It was similar to the Woman’s Questionnaire but did not include questions on use of contraceptive methods or birth history; pregnancy and postnatal care; child immunization, health, and nutrition; or adult and maternal mortality.

Cleaning operations

The processing of the 2014-15 RDHS data began as soon as questionnaires were received from the field. Completed questionnaires were returned to NISR headquarters. The numbers of questionnaires and blood samples (DBS and malaria slides) were verified by two receptionists. Questionnaires were then checked, and open-ended questions were coded by four editors who had been trained for this task and who had also attended the questionnaire training sessions for the field staff. Blood samples (DBS and malaria slides) with transmittal sheets were sent respectively to the RBC/NRL and Parasitological and Entomology Laboratory to be screened for HIV and tested for malaria.

Questionnaire data were entered via the CSPro computer program by 17 data processing personnel who were specially trained to execute this activity. Data processing was coordinated by the NISR data processing officer. ICF International provided technical assistance during the entire data processing period.

Processing the data concurrently with data collection allowed for regular monitoring of team performance and data quality. Field check tables were generated regularly during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue in areas of high quality and to correct areas of needed improvement. Feedback was individually tailored to each team. Data entry, which included 100 percent double entry to minimize keying errors, and data editing were completed on April 26, 2015. Data cleaning and finalization were completed on May 15, 2015.

Response rate

A total of 6,249 men age 15-59 were identified in this subsample of households. Of these men, 6,217 completed individual interviews, yielding a response rate of 99.5 percent.

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 2014-15 Rwanda

According to the 2021 Census, there were 30.4 million (51.0%) women and girls and 29.2 million (49.0%) men and boys in England and Wales.

The total population of Finland amounted to approximately 5.60 million inhabitants in 2023. Slightly more than half of the population in Finland, roughly 2.8 million, were women, compared to around 2.77 million men. The population of Finland has been steadily increasing, but showed a slowing trend in recent years. Fertility rate dropped to an all-time low in Finland The slowing increase in the Finnish population can be seen in the country’s decreasing fertility rate, which dropped to an all-time low in 2019. The estimated number of children born to a woman amounted to 1.35 in 2019, whereas in 2010 this was 1.87. The number of elders in Finland exceeded the number of 0-to-19-year-olds Another factor slowing down the population growth is the aging population of the country. The number of people aged 60 years and older has been increasing in recent years, amounting to over 1.6 million in 2023. In 2018, the elderly population in Finland exceeded that of children and teenagers.

Abstract

The 2015-16 Armenia Demographic and Health Survey (2015-16 ADHS) is the fourth in a series of nationally representative sample surveys designed to provide information on population and health issues. It is conducted in Armenia under the worldwide Demographic and Health Surveys program. Specifically, the objective of the 2015-16 ADHS is to provide current and reliable information on fertility and abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of young children, childhood mortality, maternal and child health, domestic violence against women, child discipline, awareness and behavior regarding AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking, tuberculosis, and anemia. The survey obtained detailed information on these issues from women of reproductive age and, for certain topics, from men as well.

The 2015-16 ADHS results are intended to provide information needed to evaluate existing social programs and to design new strategies to improve the health of and health services for the people of Armenia. Data are presented by region (marz) wherever sample size permits. The information collected in the 2015-16 ADHS will provide updated estimates of basic demographic and health indicators covered in the 2000, 2005, and 2010 surveys.

The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the NSS. The 2015-16 ADHS also provides comparable data for longterm trend analysis because the 2000, 2005, 2010, and 2015-16 surveys were implemented by the same organization and used similar data collection procedures. It also adds to the international database of demographic and health–related information for research purposes.

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to produce representative estimates of key indicators at the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

The sampling frame used for the 2015-16 ADHS is the Armenia Population and Housing Census, which was conducted in Armenia in 2011 (APHC 2011). The sampling frame is a complete list of enumeration areas (EAs) covering the whole country, a total number of 11,571 EAs, provided by the National Statistical Service (NSS) of Armenia, the implementing agency for the 2015-16 ADHS. This EA frame was created from the census data base by summarizing the households down to EA level. A representative probability sample of 8,749 households was selected for the 2015-16 ADHS sample. The sample was selected in two stages. In the first stage, 313 clusters (192 in urban areas and 121 in rural areas) were selected from a list of EAs in the sampling frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey. Appendix A provides additional information on the sample design of the 2015-16 Armenia DHS. Because of the approximately equal sample size in each marz, the sample is not self-weighting at the national level, and weighting factors have been calculated, added to the data file, and applied so that results are representative at the national level.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used for the 2015-16 ADHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker 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 Armenia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Armenian. They were pretested in September-October 2015.

Cleaning operations

The processing of the 2015-16 ADHS data began shortly after fieldwork commenced. All completed questionnaires were edited immediately by field editors while still in the field and checked by the supervisors before being dispatched to the data processing center at the NSS central office in Yerevan. These completed questionnaires were edited and entered by 15 data processing personnel specially trained for this task. All data were entered twice for 100 percent verification. Data were entered using the CSPro computer package. The concurrent processing of the data was an advantage because the senior ADHS technical staff were able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. Moreover, the double entry of data enabled easy comparison and identification of errors and inconsistencies. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in June 2016.

Response rate

A total of 8,749 households were selected in the sample, of which 8,205 were occupied at the time of the fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. The number of occupied households successfully interviewed was 7,893, yielding a household response rate of 96 percent. The household response rate in urban areas (96 percent) was nearly the same as in rural areas (97 percent).

In these households, a total of 6,251 eligible women were identified; interviews were completed with 6,116 of these women, yielding a response rate of 98 percent. In one-half of the households, a total of 2,856 eligible men were identified, and interviews were completed with 2,755 of these men, yielding a response rate of 97 percent. Among men, response rates are slightly lower in urban areas (96 percent) than in rural areas (97 percent), whereas rates for women are the same in urban and in rural areas (98 percent).

The 2015-16 ADHS achieved a slightly higher response rate for households than the 2010 ADHS (NSS 2012). The increase is only notable for urban households (96 percent in 2015-16 compared with 94 percent in 2010). Response rates in all other categories are very close to what they were in 2010.

Sampling error estimates

SAS computer software were used to calculate sampling errors for the 2015-16 ADHS. The programs used the Taylor linearization method of variance estimation for means or proportions and the Jackknife repeated replication method 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 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 - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Vaccinations by background characteristics for children age 18-29 months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

In addition to the general objectives of evaluating family plan-ning and maternal/child health care program performance, examining the family planning needs of the male and female population, and describing fertility levels, the 1989 Haiti National Contra-ceptive Prevalence Survey had several more specific objectives, as follows:

a. To explain the large discrepancy between the number of contraceptives, particularly condoms, reportedly distributed, and the estimated number used according to past surveys and service statistics. Previous surveys (of females only) had indicated that about 1 percent or 14,000 Haitian couples used condoms as a method of preventing pregnancy. This finding persisted in spite of the seemingly incompatible data showing that as many as 15 million or more condoms have been issued from warehouses in Haiti in some years, which would be sufficient to supply 10 percent or 150,000 couples. The 1989 Haiti National Contraceptive Prevalence Survey sought to resolve this discrepancy by including an independent sample of males and using a newly designed condom module questioning both males and females about condom use, the number of condoms distributed to individuals, the number used, and the number remaining unused. Equally important were questions to ascertain the extent to which Haitians are using condoms outside of marital unions and/or as a means of preventing transmission of HIV and other STD infections rather than pregnancy prevention. Condoms are currently the most effective means of preventing sexual transmission of HIV short of abstinence, but as in most developing countries, the amount of condom use for this purpose in Haiti is unknown.

b. To obtain current data on contraceptive prevalence, method mix, sources of contraception, fertility, and levels of unplanned pregnancy. The 1989 Haiti National Contraceptive Prevalence Survey also ascertained whether there have been any changes in these areas and defined target groups for family planning activities in both the public and private sectors.

c. To investigate barriers to increased family planning use and identify programmatic factors that are important in improving acceptance and continuation of contraception. To achieve this objective, the survey collected information on perceived and actual problems with obtaining supplies, access to services, and the methods themselves.

d. To examine male roles in family planning decision making and male attitudes about family planning. It is often argued that male attitudes constitute a major impediment to both the adoption of family planning methods and to a reduction of family size in much of the developing world. In Haiti, contraceptive use remains low while fertility remains high, and male attitudes are sometimes cited as a reason for this lack of change. The 1989 Haiti National Contraceptive Prevalence Survey, the first to include males, collected data on the male's role in the couple's decision on whether to use a method and what kind of method to use, as well as how male and female attitudes compare regarding desired family size and the use of family planning. These data will show whether program activities might be modified to take male roles and attitudes into account.

e. To examine the proximate determinants of fertility including both modern and traditional contraception, breastfeeding, amenorrhea and patterns of union and cohabitation. Analysis of the 1989 Haiti National Contraceptive Prevalence Survey should cast further light on the difference between the 1983 estimates of total fertility of 5.5 births per women and the 1987 survey which found total fertility to be substantially higher at 6.4 births per woman.

f. To examine certain sexuality issues, particularly as these issues relate to family planning, condom use, and HIV transmis- sion. The 1989 Haiti National Contraceptive Prevalence Survey included questions on such topics as coital frequency and numbers of sexual partners for currently sexually active persons and, for 15-24 year-olds, a module on the age at which sexual activity began and early use of contraception.

Geographic coverage

The 1989 Haiti National Contraceptive Prevalence Survey was a nationwide population-based household survey. The population to be surveyed was divided into 3 domains: Metropolitan Port-au-Prince, other urban areas and rural areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1989 Haiti National Contraceptive Prevalence Survey was a nationwide population-based household survey. The population to be surveyed was divided into 3 domains: Metropolitan Port-au-Prince, other urban areas and rural areas. Urban areas contain only about one-fourth of Haiti's population. Port-au-Prince and other urban areas were over-sampled so that they include approximately one-half of the sample households so estimates in urban areas would have greater precision. Rural areas were correspondingly undersampled. Thus, all total estimates for Haiti require weights to reflect the true population distribution in each domain (stratum) while unweighted numbers of cases are shown in tables.

The sampling strategy consisted of a two-stage cluster design to select respondents for the survey. The first stage consisted of the selection of independent samples of census enumeration districts (SDEs in French) within each domain. These SDEs were sub-sampled from SDEs selected for a larger 1987 survey of household expenditures (selection of SDEs for that survey was done with probability proportional to size). There were 28 SDEs selected in Port-au-Prince, 22 in other urban areas and 44 in rural areas--a total of 94 SDEs.

The second stage consisted of the random selection of households in each cluster: 46 households in clusters in Port-au-Prince and other urban areas and 54 households in clusters in rural areas. Half the households in each cluster were designated as "male" households and half as "female" households. Male interviewers interviewed all males between the ages of 15 and 59 who resided within selected male households, while female interviewers interviewed all females between the ages of 15 and 49 years in female households.

A total of 4,650 households were included in the sample. (One SDE on the island of Gonave was not visited because of inaccessibility.) It was estimated that this sample size would yield about 4,000 completed individual interviews--1,800 male and 2,200 females. The sample size was based on the minimum number of interviews needed per stratum to obtain adequately precise estimates for most of the survey topics, based on census estimates of potential respondents per household and projected response rates. Complete interviews were conducted with 1,842 males and 1,996 females.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument consisted of two parts--a short household questionnaire and a much longer respondent questionnaire. The household questionnaire was filled out for every residence visited. It included information on the household's location and type of construction, water and latrine facilities, a listing of all residents and a small amount of information on each person listed.

The respondent questionnaire was to be administered in "male" households to all males 15-59 years of age and in "female" households to all females 15-49 years of age listed on the household questionnaire.

The male questionnaire covered the following topics: a. Socioeconomic and demographic characteristics, including age, religion, and socioeconomic status indicators; b. A complete marriage and cohabitation history; c. Contraceptive knowledge and use, including knowledge and past and current use of all family planning methods; d. Condom utilization, including information on numbers of condoms obtained, the number used and on hand, and attitudes toward condoms and their use; e. Male roles in the couple's decision regarding use of family planning methods and male attitudes concerning contraception and fertility; f. Numbers of current sexual partners, coital frequency and, for 15-24 year old young adults, information on the initiation of sexual activity.

The female questionnaire covered all of the above topics except male roles and attitudes. In addition, it included: a. Pregnancy and childbearing information and information on breastfeeding, postpartum amenorrhea, desired fertility, and the planning status of the last pregnancy; b. Barriers to family planning use, including information on reasons for not using or for having stopped using contraception, accessibility of family planning services, satisfaction with services used or available, and other factors which may be hindering acceptance or continuation of methods; c. Pregnancy termination, including information on reported induced abortions.

Cleaning operations

Data processing activities were carried out at the CHI using micro-computer operators hired for the survey. A CHI programmer supervised data entry and editing. Data entry and editing were done concurrently using software developed at the Centers for Disease Control (CDC) and modified for this survey. This software performs checks on the ranges of all variables, the consistency between variables and the "skip patterns" of the questionnaires. The data entry staff, thus, had the added responsibility of passing questionnaire problems on to the data manager. These staff members were trained with the interviewers to insure familiarity with the data collection instruments. A CDC programmer traveled to Haiti shortly after field work and data collection began for the purpose of installing the software and training the CHI staff in the use of the software, as well

Abstract

The 2015 Afghanistan Demographic and Health Survey (2015 AfDHS) is the first DHS survey conducted in Afghanistan. The main objective of the 2015 AfDHS is to provide up-to-date information on fertility and childhood mortality levels; fertility preferences; awareness, approval, and use of family planning methods; maternal and child health; and knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STIs). The 2015 AfDHS calls for a nationally representative sample of 25,650 residential households; in all the sample households, all ever-married women age 15-49 who are usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In half of the sample households, all ever-married men age 15-49 who are usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In each household, one woman age 15-49 was randomly selected to be eligible for the Domestic Violence module.

The 2015 AfDHS was designed to provide most of the key indicators for the country as a whole, for urban and rural areas separately, and for each of the 34 provinces in Afghanistan. These provinces are located in eight regions as follows: - The Northern region: Balkh, Faryab, Jawzjan, Samangan, and Sar-E-Pul - The North Eastern region: Badakhshan, Baghlan, Kunduz, and Takhar - The Western region: Badghis, Farah, Ghor, and Herat - The Central Highland region: Bamyan and Daykundi - The Capital region: Kabul, Kapisa, Logar, Panjsher, Parwan, and Wardak - The Southern region: Ghazni, Helmand, Kandahar, Nimroz, Urozgan, and Zabul - The South Eastern region: Khost, Paktika, and Paktya - The Eastern region: Kunarha, Laghman, Nangarhar, and Nooristan

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), and all ever-women aged 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2015 AfDHS is an updated version of the Household Listing Frame, prepared in 2003-04 and updated in 2009, provided by the Central Statistics Organization (CSO). The sampling frame had information on 25,974 enumeration areas (EAs). An EA is a geographic area consisting of a convenient number of dwelling units that serve as counting units for the census. The sampling frame contained information about the location (province, district, and control area), the type of residence (urban or rural), and the estimated number of residential households for each of the 25,974 EAs. Satellite maps were also available for each EA, which delimited the geographic boundaries of the area. The sampling frame excluded institutional populations such as persons in hotels, barracks, and prisons.

The 2015 AfDHS followed a stratified two-stage sample design and was intended to allow estimates of key indicators at the national level, in urban and rural areas, and for each of the 34 provinces of Afghanistan. The first stage involved selecting sample points (clusters) consisting of EAs. A total of 950 clusters were selected, 260 in urban areas and 690 in rural areas. It was recognized that some areas of the country might be difficult to reach because of ongoing security issues. Therefore, to mitigate the situation, reserve clusters were selected in all of the provinces to replace the inaccessible clusters. The 101 reserve clusters that were preselected did not exceed 10% of the selected clusters in the province.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters, and a fixed number of 27 households per cluster were selected through an equal probability systematic selection process, for a total sample size of 25,650 households. Because of the approximately equal sample size in each province, the sample is not self-weighting at the national level, and weighting factors have been calculated, added to the data file, and applied so that results are representative at the national level.

All ever-married women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. In half of the households, all ever-married men age 15-49 who were either residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed.

During the household listing operation, more than 70 selected clusters were identified as insecure. Therefore, a decision was made to carry out the household listing operation in all of the 101 preselected reserve clusters, which also accounted for the possibility of identifying more insecure clusters during data collection. Household listing was successfully completed in 976 of 1,051 clusters. Overall, the survey was successfully carried out in 956 clusters.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2015 AfDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s 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 Afghanistan. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, the questionnaires were translated into Dari and Pashto. The survey protocol and the questionnaires were approved by the ICF Institutional Review Board (IRB) and the Ministry of Public Health of Afghanistan.

Cleaning operations

All completed questionnaires were edited in the field and dispatched to the data processing center at the CSO central office in Kabul. CSPro data processing software was used to enter the data. All the data were entered twice for 100% verification.

Response rate

A total of 25,741 households were selected for the sample, of which 24,941 were occupied during the survey fieldwork. Of the occupied households, 24,395 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 30,434 ever-married women age 15-49 were identified for individual interviews; interviews were completed with 29,461 of these women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 11,778 ever-married men age 15-49 were identified and 10,760 were successfully interviewed, yielding a response rate of 91%. The lower response rate for men was likely due to their more frequent and longer absences from the household.

The response rates are lower in urban areas than in rural areas. The difference is more prominent for men than women, as men in the urban areas are often away from their households for work. Moreover, given the security situation in the country, the field teams could not carry out interviews in the late evenings when more men are at home.

Sampling error estimates

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 by either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Afghanistan Demographic and Health Survey (2015 AfDHS) 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 2015 AfDHS 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 2015 AfDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed by SAS programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means,

Abstract

The Ghana Living Standards Survey (GLSS), with its focus on the household as a key social and economic unit, provides valuable insights into living conditions in Ghana. This present report gives a summary of the main findings of the fourth round survey, which was carried out by the Ghana Statistical Service (GSS) over a 12-month period (April 1998 to March 1999).

A representative nationwide sample of more than 5,998 households, containing over 25,000 persons, was covered in GLSS 4. Detailed information was collected on all aspects of living conditions, including health, education, employment, housing, agricultural activities, the operation of non-farm establishments, remittances, savings, and credit and assets. The special focus of GLSS 4 was on collecting detailed labour force, income and expenditure data in respect of all household members.

The key findings of the survey are as follows:

Education

Information are given on levels of educational attainment of the adult population, current school enrolment, educational expenditure by households, adult literacy rates, and apprenticeship training. About 32 percent of all adults (representing nearly three and a half million people) have never been to school, a quarter went to school but did not obtain any qualifications; about 33 percent have the MSLC/JSS certificate as their highest qualification, while the remaining 10 percent (a million adults) have secondary or higher-level qualifications (Section 2.1).

About 8 in every ten children aged 6-15, and about half of those aged 16-18, are currently attending school or college. Attendance rates for females are lower than those for males, especially in the northern half of the country (Section 2.2). The average annual cost to a household of maintaining a person at school or college was ¢163,500 per year in March 1999 cedis (Section 2.3). The survey results indicate that 50 percent of adults in Ghana are literate in English or a local language. There are substantial differences between the sexes, and between localities, with regard to literacy. A little over 6 out of every 10 men, but fewer than 4 out of every 10 women, are literate. More than two-thirds (66%) of adults in urban areas are literate, but in rural areas only 41 percent are literate (Section 2.4).

Health

The survey collected data on each person's health condition over the previous two weeks; on the fertility, pre-natal care and contraceptive use of women aged 15-49; on the post-natal care of children aged 5 years and under; and on the preventive health care and vaccination of children aged 7 years and under. About 26 percent of the sample reported having suffered from an illness or injury in the previous two weeks, 61 percent of whom had to stop their usual activities due to the indisposition (Section 3.2).

The survey found that 7.0 percent of women were currently pregnant, and a further 13.2 percent had been pregnant in the last 12 months. Only about 15 percent of all women aged 15-49 or their partners reported using contraceptives; about 11 percent use modern methods, and 4 percent use traditional methods, to prevent or delay pregnancy (Section 3.3). The level of breastfeeding in Ghana is very high; about 98 percent of all children under 5 have been breastfed at one time or another. About 7 percent of children below the age of 8 have never been vaccinated against any of the childhood killer diseases.

Employment

As a major focus of the survey, a wide range of estimates of economic activity, employment, unemployment, underemployment and working conditions are given in the report. The survey also has detailed information about time spent on housekeeping activities. About 77 percent of the adult population (aged 15+) is currently economically active. The activity rates for males and females differ, with the rate for women in the age group (15-64) lower than those for men, but in the younger age group (7-14) and the older age group (65+) the rates for females exceed those for males. For each age group the activity rates for males and females are higher in rural areas (apart from rural savannah) than in urban areas (Section 4.2).

The majority of the working population is employed in agricultural activities (55.0%), followed by trading (18.3%) and then manufacturing (11.7%). Whereas 27.4 percent of working females are engaged in trading, only 7.4 percent of males are traders. The highest hourly wage rates are obtained in mining and quarrying, followed by financial services and then trading. For all areas of employment, females earn lower wages than males (Section 4.3). About 8 percent of the currently active population can be classified as unemployed, but there is also a high degree of underemployment, with some people having a job but wanting to do more work (Section 4.4).

In many households, particularly in rural areas, family members (especially women) spend a great deal of their time fetching water and firewood, in addition to the time spent on other household activities such as cooking and cleaning (Section 4.5).

Migration

The report provides data on migration to create some awareness that would generate further discussions and research into the complex field of population relocation. Some 52 percent of all Ghanaians are migrants, having previously lived in a locality different from where they are living at present; a further 16 percent have moved away from their birthplace, but subsequently returned (Section 5.1).

Housing

Detailed information is presented on a variety of housing characteristics: the occupancy status of the household; household size and room density; access to drinking water, toilet facilities, source of lighting and fuel, rubbish disposal, and materials used in house construction. A little over 40 percent (24 percent in urban areas and 60 percent in rural areas) of the households own the houses they live in. About 80 percent of the households in urban areas have access to pipe-borne water, compared with only 19 percent in rural areas. More than three-quarters of urban households have electricity for lighting, compared with only 17 percent of rural households. Most urban households use charcoal for cooking, whereas most households in rural areas use firewood. Only 14 percent of urban households, and 2 percent of rural households, have access to a flush toilet (Section 6.3).

Household agriculture

About 2.7 million households in Ghana own or operate a farm or keep livestock (Section 7.1). More than half of households, which cultivate crops hire labour for their operations. The major crops, in terms of sales, are cocoa, maize, groundnuts/peanuts, and rice (Section 7.2). About 2 and a half million households process crops or fish for sale, with the major responsibility for this activity falling on women.

Non-farm enterprises

Approximately 1.9 million households or 49 percent of all households in Ghana operate a non-farm business with women operating two-thirds of these businesses. About 56 percent of all businesses involve retail trade, and most of the rest cover some kind of manufacturing (for instance food, beverages, textiles or clothing) (Section 8.1).

Total expenditure

Average annual household expenditure (both cash and imputed) relative to March 1999 prices was about ¢4,244,000. Given an average household size of 4.3, this implies annual per capita expenditure of about ¢987,000 (Section 9.1). With an exchange rate of ¢2,394 to the US dollar prevailing at March 1999, the average annual household expenditure is US$1,773 and the pre-capita expenditure is US$412. Overall, cash expenditure on food represents 45.4 percent of total household expenditure, while the imputed value of own-produced food consumed by households represents a further 10.3 percent (Section 9.2).

Cash expenditure

Relative to March 1999 prices, Ghanaian households spend on average almost ¢3,500,000 a year (at March 1999 prices), or ¢804,000 on per capita basis (Section 9.3). On national terms, just below half of total cash expenditure (46%) went to food and beverages; and alcohol and tobacco, and clothing and footwear, each accounted for about 10 percent of it. The next most important expenditure groups, in terms of amount spent, are recreation and education (7.5%), transport and communications (5.6%), housing and utility (6.4%) and household goods, operations and services (6.0%).

Food consumption

At the time of the survey Ghanaian households (which number about 4.2 million) were spending on average an amount of almost ¢2.4 billion (at March 1999 prices) on food (Section 9.5), with own-grown food consumed amounting to the value of almost ¢435,000 (Section 8.7). The most important food consumption subgroups, in terms of cash expenditure are roots and tubers (22%), fish (16%), cereals and cereal products (15%), vegetables (9%), and meat (5%). Prepared meals account for 11 percent by value of total food consumption.

While the pattern of consumption, in terms of food subgroups, is broadly similar in urban and rural areas, residents in rural areas consume more roots and tubers, and pulses and nuts than their counterparts in urban areas. Expenditure on alcohol and tobacco is also higher in rural areas. In contrast, the consumption of meat and prepared meal are much higher in urban areas than in rural areas, and urban residents spend much more on cereals and cereal products and poultry and poultry products than their rural counterparts (Section 9.5).

Remittances

About 76 percent of all households reported having remitted money or goods in the previous 12 months to persons who were not their household members. The bulk of these remittances to non-household members went to relatives (93%), and in particular to parents or children (50%), brothers or sisters (18%), and other relatives (23%). Such income flows from the household benefited females (64%) more than their male counterparts (36%).

Whilst

Abstract

The 2013 Liberia Demographic and Health Survey (LDHS) is designed to provide data for monitoring the population and health situation in Liberia. The 2013 LDHS is the fourth Demographic and Health Survey conducted in Liberia since 1986. The primary objective of the 2013 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2013 LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, and HIV/AIDS and other sexually transmitted infections (STIs). In addition, the 2013 LDHS provides estimates on HIV prevalence among adult Liberians.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual/ person
• Children age 0-5 years
• Woman age 15 to 49 years
• Man age 15 to 49 years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sampling frame for the 2013 LDHS was developed by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) after the 2008 National Population and Housing Census (NPHC). The sampling frame is similar to that used for the 2009 and 2011 Liberia Malaria Indicator Surveys (LMIS), except that the classification of localities as urban or rural was updated through the application of standardized definitions. The sampling frame excluded nomadic and institutional populations such as residents of hotels, barracks, and prisons. Notably, the sampling frame for the 2013 LDHS differs markedly from that used for the 2007 LDHS, which was based on the 1984 NPHC. Taken together, these differences may complicate data comparisons between surveys.

The 2013 LDHS followed a two-stage sample design that allowed estimates of key indicators for the country as a whole, for urban and rural areas separately, for Greater Monrovia and other urban areas separately, and for each of 15 counties. To facilitate estimates of geographical differentials for certain demographic indicators, the 15 counties were collapsed into five regions as follows: North Western: Bomi, Grand Cape Mount, and Gbarpolu South Central: Montserrado, Margibi, and Grand Bassa South Eastern A: River Cess, Sinoe, and Grand Gedeh South Eastern B: River Gee, Grand Kru, and Maryland North Central: Bong, Nimba, and Lofa

Regional data were presented in the 2007 LDHS, the 2009 LMIS, and the 2011 LMIS. However, in contrast with these past surveys, the South Central region now includes Monrovia. Thus, data presented for the South Central region in this report is not directly comparable to that presented in the 2007 LDHS, the 2009 LMIS, or the 2011 LMIS.

The first stage of sample selection involved selecting sample points (clusters) consisting of enumeration areas (EAs) delineated for the 2008 NPHC. Overall, the sample included 322 sample points, 119 in urban areas and 203 in rural areas. To allow for separate estimates of Greater Monrovia and Montserrado as a whole, 44 sample points were selected in Montserrado; 16 to 26 sample points were selected in each of the other 14 counties.

The second stage of selection involved the systemic sampling of households. A household listing operation was undertaken in all the selected EAs from mid-September to mid-October 2012. From these lists, households to be included in the survey were selected. Approximately 30 households were selected from each sample point for a total sample size of 9,677 households. During the listing, geographic coordinates (latitude and longitude) were taken in the center of the populated area of each EA using global positioning system (GPS) units.

Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level, and weighting factors have been added to the data file so that the results will be proportional at the national level.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In half of the households, all men age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In the subsample of households selected for the male survey, blood samples were collected for laboratory testing to detect HIV from eligible women and men who consented; in this same subsample of households, height and weight information was collected from eligible women, men, and children 0-59 months.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2013 LDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires are based on MEASURE DHS standard survey questionnaires and were adapted to reflect the population and health issues relevant to Liberia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors.

Given that there are dozens of local languages in Liberia, most of which have no accepted written script and are not taught in the schools, and given that English is widely spoken, it was decided not to attempt to translate the questionnaires into vernaculars. However, many of the questions were broken down into a simpler form of Liberian English that interviewers could use with respondents.

The Household Questionnaire was used to list all the usual members of and visitors to selected households. Some basic demographic information was collected on the characteristics of each person listed, including his or her 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 age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for individual interview and HIV testing. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facility, materials used for the floor of the house, ownership of various durable goods, ownership and use of mosquito nets, and information on household out-of-pocket health-related expenditures. The Household Questionnaire was also used to record height and weight measurements of children 0-59 months and eligible adults. Also recorded was whether or not eligible adults consented to HIV testing.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49.

The Man’s Questionnaire was administered to all men age 15-49 in the subsample of households selected for the male survey in the 2013 LDHS sample. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.

Cleaning operations

All questionnaires were returned to the LISGIS central office in Monrovia for data processing, which consisted of office editing, coding of open-ended questions, data entry, and editing computer-identified errors. The data were processed by a team of 12 data entry clerks, two data editors, one data entry supervisor, and two administrators of questionnaires; the latter checked that the clusters were completed according to the sample selection and that all members of the household eligible for individual interview were identified. Secondary editing was led by an LDHS coordinator. Several LISGIS staff took on the responsibility of receiving the blood samples from the field and checking them before sending them to the Montserrado Regional Blood Bank for storage. Data entry and editing using CSPro software was initiated in April 2013 and completed in late August 2013.

Response rate

A total of 9,677 households were selected for the sample, of which 9,386 were occupied. Of the occupied households, 9,333 were successfully interviewed, yielding a response rate of 99 percent.

In the interviewed households, 9,462 eligible women were identified for individual interview; of these, complete interviews were conducted with 9,239 women, yielding a response rate of 98 percent. In the subsample of households selected for the male survey, 4,318 eligible men were identified and 4,118 were successfully interviewed, yielding a response rate of 95 percent. The lower response rate for men was likely due to their more frequent and longer absences from the household.

Sampling error estimates

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 2013 Liberia Demographic and Health Survey 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 2013 LDHS is only one of many samples that could have been selected from the same population,

In 2023, the largest share of women aged 75 and older were living alone (** percent). In comparison, approximately **** percent of men in this age group lived with their spouse. When it comes to living with relatives, women were ***** times more likely to fall into this group than men.

Abstract

The Labor Force Survey (LFS) 2012-13, was carried out to generate reliable up-to-date information on employment and unemployment situations and other labor force characteristics of the Malawian population between the ages of 15-64 years. The National Statistical Office (NSO), together with the Ministry of Labor and Trade, the Ministry of Economic Planning and Development collaborated in conducting this survey.

The 2012-13 survey indicated that 7 million people within the age group 15-64 were in the labor force. Of this total, 3.3 million were males and 3.7 million were females. By sub-population groups, the results show that out of the total labor force, 87 percent were residents in the rural areas, 64 percent had no education and nearly half (48 percent) were under 30 years old. The labor force participation rates for both males and females were quite high. The rates ranged from 70 percent in the age group 15-19 to 97 percent in age groups 30-34 and 40-44.The specific objectives of the survey were: 1. To estimate the size of the labor force of people between 15-64 years by demographic characteristics. 2. To estimate the number of employed persons by occupation, industry and employment status. 3. To estimate the population which is not working together with their demographic characteristics. 4. To estimate youth unemployment.

The results of the survey provided statistics that served a wide variety of purposes. Some of these purposes are: - To monitor the economic situation. - To formulate and implement policies for decent work, employment creation and poverty reduction, income support as well as other social programs. - To provide indicators for monitoring the country's progress towards achieving both Millennium Development Goals (MGDS II and MDGs) goals.

Geographic coverage

National and regional levels for rural and urban areas.

Analysis unit

• Individuals
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the LFS was to provide employment and unemployment estimates at the national and regional levels and for rural and urban areas. A two stage sampling design was used. During the first stage, 550 clusters were drawn from the 2008 Population and Housing Census sample frame. 213 clusters from urban areas and 337 clusters from rural areas. At regional level, Northern, Central and Southern, 97 clusters, 192 clusters and 261 clusters were drawn respectively.

The NSO staff conducted an exhaustive listing of households in each of the selected clusters between July and September 2013. The household listing provided the frame for second stage of sampling, where a systematic sample of 20 households was drawn from each of LFS selected cluster. A total of 11,000 households were sampled; 4,260 from urban areas and 6,740 from rural areas. All men and women age 10 years and over in selected households were eligible for individual interviews.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires used in the 2012-13 LFS survey for data collection: the Household Questionnaire and the Individual Questionnaire. The contents were based on ILO model questionnaires, which were adapted for use in Malawi in collaboration with a wide range of stakeholders. The questionnaires were translated into two local languages, Chichewa and Tumbuka prior to pretesting.

Cleaning operations

All completed questionnaires were sent to the NSO Headquarters for data processing. The data processing started in February and was completed in June 2013. The data went through several rigorous stages of data cleaning such as structural edits, content edit and imputation. Data entry was done in the Census and Survey Processing System (CSPro), data entry application which was developed in-house. The final dataset was sent to the ILO Office in South Africa for data weighting and estimation.

Response rate

97.5%

Abstract

The primary objective of the 2014 GDHS was to generate recent reliable information on fertility, family planning, infant and child mortality, maternal and child health, and nutrition. In addition, the survey collected specialised data on malaria treatment, prevention, and prevalence among children age 6-59 months; blood pressure among adults; anaemia among women and children; and HIV prevalence among adults. This information is essential for making informed policy decisions and for planning, monitoring, and evaluating programmes related to health in general, and reproductive health in particular, at both the national and regional levels. Analysis of data collected in the 2014 GDHS provides updated estimates of basic demographic and health indicators covered in the earlier rounds of the 1988, 1993, 1998, 2003, and 2008 surveys.

The GDHS will assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of Ghana’s population. The 2014 GDHS also provides comparable data for long-term trend analysis in Ghana, since the surveys were implemented by the same organisation, using similar data collection procedures. Furthermore, the survey adds to the international database on demographic and health–related information for research purposes.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Children age 4-15
• Woman age 15-49
• Man age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2014 GDHS is an updated frame from the 2010 Ghana Population and Housing Census provided by the Ghana Statistical Service (GSS 2013b). The sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks, and prisons.

The 2014 GDHS followed a two-stage sample design and was intended to allow estimates of key indicators at the national level as well as for urban and rural areas and each of Ghana's 10 administrative regions. The first stage involved selecting sample points (clusters) consisting of enumeration areas (EAs) delineated for the 2010 PHC. A total of 427 clusters were selected, 216 in urban areas and 211 in rural areas.

The second stage involved the systematic sampling of households. A household listing operation was undertaken in all the selected EAs in January-March 2014, and households to be included in the survey were randomly selected from the list. About 30 households were selected from each cluster to constitute the total sample size of 12,831 households. Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level, and weighting factors have been added to the data file so that the results will be proportional at the national level.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed and have their blood pressure measured.

In half of the households, all men age 15-59 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. In addition, in the subsample of households selected for the male survey: • blood pressure measurements were performed among eligible men who consented to being tested; • children age 6-59 months were tested for anaemia and malaria with the parent's or guardian's consent; • eligible women who consented were tested for anaemia; • blood samples were collected for laboratory testing of HIV from eligible women and men who consented; and • height and weight information was collected from eligible women, men, and children age 0- 59 months.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2014 GDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires, which were based on standard Demographic and Health Survey (DHS) questionnaires, were adapted to reflect the population and health issues relevant to Ghana. Comments on the questionnaires were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. The definitive questionnaires were first prepared in English; they were then translated into the major local languages, namely Akan, Ga, and Ewe.

The Household Questionnaire was used to list all the members of and visitors to the selected households. Basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, marital status, education, and relationship to the head of the household. For children under age 18, parents’ survival status was determined. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for individual interviews. The Household Questionnaire also included questions on child education as well as the characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the dwelling unit, and ownership of various durable goods.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49.

In half of the selected households, the Man’s Questionnaire was administered to all men age 15-59. The Man’s Questionnaire collected much of the same information found in the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.

Cleaning operations

The data processing operation included 100 percent verification (also called second data entry) and secondary editing, which involved resolution of computer-identified inconsistencies. The data processing activities at the central office were led by one key GSS officer who took part in the main fieldwork training. Data processing was accomplished using CSPro software. Data entry and editing were initiated in September 2014 and completed in February 2015.

Response rate

A total of 12,831 households were selected for the sample, of which 12,010 were occupied. Of the occupied households, 11,835 were successfully interviewed, yielding a response rate of 99 percent, the same as the 2008 GDHS household response rate (GSS, GHS, and ICF Macro 2009).

In the interviewed households, 9,656 eligible women were identified for individual interviews; interviews were completed with 9,396 women, yielding a response rate of 97 percent. In the subsample of households selected for the male survey, 4,609 eligible men were identified and 4,388 were successfully interviewed, yielding a response rate of 95 percent. The lower response rate for men was likely due to their more frequent and longer absences from the household.

Sampling error estimates

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 2014 Ghana DHS (GDHS) 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 2014 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.

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 2014 GDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use 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.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

Abstract

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 BDHS is intended to serve as a source of population and health data for policymakers and the research community. 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, and - advance survey methodology.

More specifically, the objective of the BDHS 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.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into six administrative divisions, 64 districts (zillas), and 490 thanas. In rural areas, thanas are divided into unions and then mauzas, a land administrative unit. Urban areas are divided into wards and then mahallas. The 1996-97 BDHS employed a nationally-representative, two-stage sample that was selected from the Integrated Multi-Purpose Master Sample (IMPS) maintained by the Bangladesh Bureau of Statistics. Each division was stratified into three groups: 1 ) statistical metropolitan areas (SMAs), 2) municipalities (other urban areas), and 3) rural areas. 3 In the rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 Census frame, the units for the BDHS were sub-selected from the IMPS with equal probability so as to retain the overall probability proportional to size. A total of 316 primary sampling units were utilized for the BDHS (30 in SMAs, 42 in municipalities, and 244 in rural areas). In order to highlight changes in survey indicators over time, the 1996-97 BDHS utilized the same sample points (though not necessarily the same households) that were selected for the 1993-94 BDHS, except for 12 additional sample points in the new division of Sylhet. Fieldwork in three sample points was not possible (one in Dhaka Cantonment and two in the Chittagong Hill Tracts), so a total of 313 points were covered.

Since one objective of the BDHS is to provide separate estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal and Sylhet Divisions and for municipalities relative to the other divisions, SMAs and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.

Mitra and Associates conducted a household listing operation in all the sample points from 15 September to 15 December 1996. A systematic sample of 9,099 households was then selected from these lists. Every second household was selected for the men's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed all currently married men age 15-59. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 3,000 currently married men age 15-59.

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

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Men' s Questionnaire and a Community Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. These model questionnaires were adapted for use in Bangladesh during a series of meetings with a small Technical Task Force that consisted of representatives from NIPORT, Mitra and Associates, USAID/Bangladesh, the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Population Council/Dhaka, and Macro International Inc (see Appendix D for a list of members). Draft questionnaires were then circulated to other interested groups and were reviewed by the BDHS Technical Review Committee (see Appendix D for list of members). The questionnaires were developed in English and then translated into and printed in Bangla (see Appendix E for final version in English).

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 the 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 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, - Knowledge of AIDS, - Height and weight of children under age five and their mothers.

The Men's Questionnaire was used to interview currently married men age 15-59. It was similar to that for women except that it omitted the sections on reproductive history, antenatal and delivery care, breastfeeding, vaccinations, and height and weight. The Community Questionnaire was completed for each sample point and included questions about the existence in the community of income-generating activities and other development organizations and the availability of health and family planning services.

Response rate

A total of 9,099 households were selected for the sample, of which 8,682 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 8,762 households occupied, 99 percent were successfully interviewed. In these households, 9,335 women were identified as eligible for the individual interview (i.e., ever-married and age 10-49) and interviews were completed for 9,127 or 98 percent of them. In the half of the households that were selected for inclusion in the men's survey, 3,611 eligible ever-married men age 15-59 were identified, of whom 3,346 or 93 percent were interviewed.

The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

Note: See summarized response rates by residence (urban/rural) 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) 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 BDHS 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 BDHS 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 BDHS 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 BDHS is the ISSA Sampling Error Module. This module used the Taylor

Abstract

As family planning and reproductive health programs increasingly emphasize strategies designed to meet the needs of individual women, information on the circumstances under which women make and implement reproductive decisions is crucial. The Negotiating Reproductive Outcomes (NRO) study is an effort to understand the realities of women's everyday life and to identify the obstacles they may face in achieving their reproductive and health goals by investigating the nature of negotiation within sexual unions.

The NRO study was conducted in two districts in Uganda--Masaka and Lira. It was implemented jointly by the Demographic and Health Surveys (DHS) program of Macro International Inc. and the Institute of Statistics and Applied Economics (ISAE) at Makerere University in Kampala, Uganda. The study has two components, a focus group study and a survey of women and men. The survey population includes 1,750 women age 20-44 and 1,356 of their male partners, whether formally married or living together. The survey data are representative of the two districts and were designed to enable estimates to be made for urban and rural areas separately within each district.

The study has three primary objectives: - To examine how reproductive decisions and their outcomes are negotiated within sexual unions; - To determine which characteristics of the individual, household, and community influence the negotiation process; and - To investigate how the position of women influences their ability to negotiate the outcomes they desire.

Geographic coverage

The NRO study was conducted in two districts in Uganda -- Masaka and Lira.

Analysis unit

• Women age 22-44
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

The NRO sample was designed to provide estimates for women and men in Lira and Masaka separately. It was also designed to allow estimation for urban and rural areas within each district.

Sample Eligibility In order to complete a full interview, a woman had to pass three eligibility criteria. She had to be a regular resident of the household. She had to be between age 20 and age 44 in completed years. Finally, those women meeting the age and residence criteria were asked a series of introductory questions about marital status. Within the accepted age range, women who reported themselves to be "married" were automatically considered eligible to complete the full questionnaire. Unmarried women were asked to complete the full questionnaire only if they reported being in a conjugal relationship lasting six months or more. The rationale for the six-month cutoff was that non-marital, short-term relationships would be less likely to involve negotiations about long-term issues of family formation, family planning, and so forth. Teenagers were excluded on the same grounds; even in a young-marrying population, it was thought that the sample would yield a sizeable proportion of short-term, uncommitted relationships.

A different set of eligibility criteria were set for men. They were required to be partners of eligible women, either formally married or living with a woman. No age criteria were set. Residence criteria depended on marital status. Any married or unmarried partner living in the same household with an eligible woman was considered eligible to answer the male questionnaire. Husbands living in a different residence were still considered eligible, and interviews were attempted if the husband could be located within a reasonable distance of the survey area. If the woman was not married and her partner lived elsewhere, however, he was ruled ineligible (to protect the confidentiality of both partners), and no attempt was made to trace him. Men with multiple wives living in the same household and meeting the other eligibility criteria were administered separate questionnaires for each wife. In general, locating males for interview, whether they were resident or not, proved to be the most difficult and time-consuming part of the fieldwork, requiring multiple visits and visits at irregular times in the early morning or late evening.

Sample Design The sample was selected in two stages. At the first stage, census enumeration areas (EAs) were selected systematically with probability proportional to size in the 1991 census. In order to take advantage of the household listings assembled for the recent Uganda DHS, all of the DHS EAs in each district were included. The selection proceeded as follows: if 5 EAs were selected in a district for the DHS survey with a selection interval I and the NRO sample required the selection of 10 EAs, then the NRO sample was selected by reducing the interval by half (i.e., I/2) and maintaining the first random selection as in the DHS sample. At the second stage, households were selected systematically within each EA.

A random stratified sample of 40 enumeration areas was selected from each district. Due to the tendency of Masaka EAs to be larger than Lira EAs, a higher proportion of the total sample was expected from Masaka compared with Lira. In order to obtain adequate representation of urban areas, urban areas were oversampled. In Masaka district, with a population that was 10 percent urban at the time of the 1991 census, 20 EAs--or half of the sample--were drawn from urban areas.

Urban areas in Lira also were oversampled. With 5 percent of the population categorized as urban at the time of the 1991 census, 16 out of the total 40 EAs in Lira were selected as urban. The selection procedure in Lira was altered to adjust for varying definitions of "urban" in Uganda. The Department of Statistics in Uganda defines urban in one of two ways. The first is based on a set of objective demographic criteria taken during every decennial census; these include a population of over 10,000 people, access to roads, water supplies, schools, and related "urban" amenities. The presence of such amenities is determined prior to each census during the mapping of enumeration areas. The second way to achieve urban status is for an area legally to register itself as a city or town. At the time of the 1991 census, many northern districts, including Lira, were never mapped due to local political instability. In the absence of mapping to establish demographic criteria for urban status, Lira town is the only officially recognized urban area in Lira district; its status is based on legal registration. Because Masaka was mapped prior to the 1991 census, the two districts have asymmetric definitions of urban areas.

To improve the comparability of the definitions of "urban" between the two districts and to avoid oversaturation of the one official urban site in Lira, a secondary set of potential urban sites was chosen. A list of the 12 largest trading centers outside Lira town was compiled using the 1981 census records. Six of these were selected at random and included in a kind of second tier, "small urban" sample. The remaining 10 urban EAs were drawn from Lira town.

Mode of data collection

Face-to-face [f2f]

Research instrument

Based on the results of the focus group study and on an examination of the relevant demographic and anthropological literature, three questionnaires were developed: a household questionnaire, a women's questionnaire, and a men's questionnaire. The men's and women's questionnaires are alike, with minor exceptions.

The questionnaires were originally written in English, then translated into Luganda and Lango by staff from the Department of Languages at Makerere University. A pretest of the survey instruments was conducted in July 1995. Eight interviewers (4 men and 4 women) received approximately one week of training to administer the questionnaires. The training included classroom instruction and practice interviews. A day of field practice was conducted in two areas of Kampala where residents are mainly from the ethnic groups predominating in Lira and Masaka districts.

The pretest was conducted at two sites, Mukono (a Luganda speaking area) and Lira. At each site, one urban and one rural area was selected. Sixty couples were interviewed: 20 in Mukono and 40 in Lira. The results of the pretest were used to modify the skip patterns, translations, and precoded responses in the questionnaires.

Cleaning operations

Data entry began two weeks after the commencement of fieldwork. The survey data were entered on three microcomputers in the project office in Kampala. All data processing for the survey was done with ISSA (Integrated System for Survey Analysis). Initial editing and consistency checking of the questionnaires was performed in the field by the team supervisors. Some further coding and editing was carried out in the project office prior to data entry. The data entry program detects range, skip, and many consistency errors at the data entry stage. In addition, one hundred percent of the questionnaires were reentered for verification. Finally, secondary editing was performed using a program that carries out complex internal consistency checks and prints out a list of errors, which are then checked against the questionnaires and corrected where possible.

Response rate

A total of 3,869 households were selected for interview. Of these, 3,710 were found. The remainder was not valid households either because the dwelling was vacant or destroyed or because the household was absent for an extended period or could not be located. Approximately 97 percent of the contacted households (3,610 households) were successfully interviewed.

The household questionnaires identified 2,384 eligible women. Interviews with 485 of these women were terminated after the initial questions on marital status,

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.