Results of Kenya's 6th National Census i.e The 2019 Kenya Population and Housing Census Volume I, II, III, and IV reports.

The total population of South Africa amounted to approximately 63.20 million people in 2024. Following a continuous upward trend, the total population has risen by around 34.12 million people since 1980. Between 2024 and 2030, the total population will rise by around 5.88 million people, continuing its consistent upward trajectory.This indicator describes the total population in the country at hand. This total population of the country consists of all persons falling within the scope of the census.

In the first quarter of 2020, the number of Black South Africans of working age reached approximately 31.4 million, marking a year-on-year change of 1.9 percent compared to the first quarter of 2019. The number of coloreds of working age reached roughly 3.5 million in the first quarter of 2020.

Context

Africa - Population and Internet users statistics

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

Source: https://data.humdata.org/dataset/africa-population-and-internet-users-statistics Last updated at https://data.humdata.org/organization/openafrica : 2019-09-11

The total labor force in sub-Saharan Africa was estimated at *** million people in 2022. By 2023, the number is expected to reach *** million. According to the source estimates, within the period observed, the total of both the employed and unemployed individuals in the region increased annually.

Population density for Kenya recorded by the two census of 2009 and 2019

The zip files contain the following files:BFA_population_v1_1_gridded.tifThis geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of total population size per grid cell across Burkina Faso. The projection is the geographic coordinate system WGS84 (World Geodetic System 1984). NA values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies [2]. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated populations for larger areas.BFA_population_v1_1_agesex.zipThis zip folder contains 39 population rasters in geotiff format. Each raster provides gridded population estimates for an age-sex group. Files are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age within the age group represented by the data. The age groups are in five-year bins labelled with a “5”, “10”, etc. For instance, “f0” and “m0” are population counts of under 5 year olds for females and males, respectively. Eighty-five year olds and over are represented in the groups “f85” and “m85”. We provide three additional rasters that represent demographic groups often targeted by programmes and interventions. These are “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced using age-sex proportions from the 2019 census as provided in the age-sex proportion table (BFA_population_v1_1_agesex.csv) with corresponding region raster (BFA_population_v1_1_agesex_regions.tif). The age-sex proportions were applied to the gridded population estimates (BFA_population_v1_1_gridded.tif) to allocate the population to the different age-sex classes. While this data represents population counts, values contain decimals, i.e. fractions of people. This is because we do not estimate which grid cell each individual in a given age group occupies. For this reason, it is advised to aggregate the rasters at a coarser scale. For example, if four grid cells next to each other have values of 0.25 this indicates that there is 1 person of that age group somewhere in those four grid cells. Note: f0 and m0 in this application represent under 5 population, contrary to other datasets on the WorldPop Open Population Repository where f0 and m0 represent population under the age of 1.Data Citation:WorldPop and Institut National de la Statistique et de la Démographie du Burkina Faso. 2022. Censusbased gridded population estimates for Burkina Faso (2019), version 1.1. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00736.These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) project funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom Foreign, Commonwealth & Development Office (OPP1182425). Project partners include WorldPop at the University of Southampton, the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network in the Columbia Climate School at Columbia University, and the Flowminder Foundation. The Burkina Faso Institut National de la Statistique et de la Démographie supported, facilitated this work, reviewed the results and provided the census database. The modelling work, geospatial data processing, and stakeholder engagement was led by Edith Darin. Support for the engagement work and review of the methods was offered by Mathias Kuépié (UNFPA) and Antoinette Wannebo (CIESIN). Oversight was done by Andrew J.Tatem and Attila N. Lazar.The downloadableMetadataprovides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or gohere.

As of 2019, most rural inhabitants in Africa resided close to small and mid-sized towns. The nearest city to almost ** percent of the rural population had between 10,000 and ****** inhabitants. Smaller shares of rural households, on the other hand, lived closer to larger urban areas. As of the same year, roughly half of the rural residents lived within ** kilometers from a city.

United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. License : CC BY-4.0

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2015, 2016, and 2019. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census...

School age population, pre-primary education, male (number) in South Africa was reported at 2370530 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for pre-primary education, male - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

Abstract

The DTS is a large-scale household survey aimed at collecting accurate statistics on the travel behavior and expenditure of South African residents travelling within the borders of the country. Such information is crucial when determining the contribution of tourism to the South African economy, as well as helping with planning, marketing, policy formulation, and the regulation of tourism-related activities.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers’ hostels. The survey does not cover other collective living quarters such as students’ hostels, old age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalized and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the DTS 2019 was based on a Master Sample (MS) that has been designed for all household surveys conducted by Statistics South Africa.

The Master Sample used a two-staged, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used. Stratification was done in two stages: Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2011 data were summarized at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were administered to collect the survey data:

1. Cover page: includes household information, response details, result codes, field staff information and demographic information (name, sex, age, population group and marital status).
2. Questionnaire: includes education, tourism employment, domestic day trips, domestic night trips and household living conditions.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countires and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, and Round 4 (2008) 20 countries.The survey covered 34 countries in Round 5 (2011-2013), 36 countries in Round 6 (2014-2015), and 34 countries in Round 7 (2016-2018). Round 8 covered 34 African countries. The 34 countries covered in Round 8 (2019-2021) are:

Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Geographic coverage

The survey has national coverage in the following 34 African countries: Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Analysis unit

Households and individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

Kind of data

Sample survey data

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalised settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewers alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found in Section 5 of the Afrobarometer Round 5 Survey Manual

Mode of data collection

Face-to-face

Research instrument

The questionnaire for Round 3 addressed country-specific issues, but many of the same questions were asked across surveys. The survey instruments were not standardized across all countries and the following features should be noted:

• In the seven countries that originally formed the Southern Africa Barometer (SAB) - Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe - a standardized questionnaire was used, so question wording and response categories are the generally the same for all of these countries. The questionnaires in Mali and Tanzania were also essentially identical (in the original English version). Ghana, Uganda and Nigeria each had distinct questionnaires.

• This merged dataset combines, into a single variable, responses from across these different countries where either identical or very similar questions were used, or where conceptually equivalent questions can be found in at least nine of the different countries. For each variable, the exact question text from each of the countries or groups of countries ("SAB" refers to the Southern Africa Barometer countries) is listed.

• Response options also varied on some questions, and where applicable, these differences are also noted.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2016 and 2019. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of ur...

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, and Round 6 (2014-2015) 36 countries. The survey covered 34 countries in Round 7 (2016-2018). Round 8 surveys are planned in at least 35 countries in 2019-2020.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens of Ghana who are 18 years and older.

Kind of data

Sample survey data [ssd]

Sampling procedure

• Sample size: 2,400
• Sampling frame: 2019 adult population projections based on the 2010 Ghana Population and Housing Census frame.
• Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample
• Stratification: Region and urban-rural location
• Stages: PSUs (from strata), start points, households, respondents
• PSU selection: Probability proportionate to population size (PPPS)
• Cluster size: 8 households per PSU
• Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval
• Respondent selection: Gender quota filled by alternating interviews between men and women; respondents of appropriate gender listed, after which computer randomly selects individual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 8 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data. As in previous Rounds, about two-thirds of the items from the Round 6 questionnaire remain the same, and about one-third are new items. In identifying new survey topics, the Questionnaire Committee sought to align the instrument with the global development agenda by incorporating topics that speak to the Sustainable Development Goals (SDGs) that were adopted by the United Nations General Assembly in 2015. Some of the new survey topics in the R8 questionnaire include: Safety and Security; State capacity; Migration; Closing spaces; Inclusion; Climate change and, the Middle class.

The questionnaire consists of three parts: 1. Part 1 captures the steps for selecting households and respondents, and includes the introduction to the respondent. This section should be filled in by the Fieldworker. 2. Part 2 covers the core attitudinal and demographic questions that are asked by the Fieldworker and answered by the Respondent. 3. Part 3 includes contextual questions about the setting and atmosphere of the interview, and collects information on the Fieldworker. This section is completed by the Fieldworker.

Response rate

Contact rate: 97% Cooperation rate: 92% Refusal rate: 5% Response rate: 89%

School age population, post-secondary non-tertiary education, male (number) in South Africa was reported at 976471 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for post-secondary non-tertiary education, male - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

The master sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are:urban, tribal and farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4) and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

There are a number of aspects in which the 2013 version of the master sample differs from the 2007 version. In particular, the number of primary sample units increased. Mining strata were also introduced which serves to improve the efficiency of estimates relating to employment in mining. The number of geo-types was reduced from 4 to 3 while the new master sample allows for the publication of estimates of the labour market at metro level. The master sample was also adjusted Given the change in the provincial distribution of the South African population between 2001 and 2011. There was also an 8% increase in the sample size of the master sample of PSUs to improve the precision of the QLFS estimates. The sample size increased most notable in Gauteng, the Eastern Cape and KwaZulu-Natal. For more details on the differences between the two master samples please consult the section 8 (technical notes) of the QLFS 2015 Q3 release document (P0211).

From the master sample frame, the QLFS takes draws exmploying a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population.

For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings are expected to remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of five section: Section 1: Biographical information (marital status, language, migration, education, training, literacy, etc.) Section 2: Economic activities for persons aged 15 years and older Section 3: Unemployment and economic inactivity for persons aged 15 years and older Section 4: Main work activities in the last week for persons aged 15 years and older Section 5: Earnings in the main job for employees, employers and own-account workers aged 15 years and older

This data set is needed to reproduce the figures in the git repository.

The exposure is calculated following the workflow described in the paper:

Weber, T., Bowyer, P., Rechid, D., Pfeifer, S., Raffaele, F., Remedio, A. R., et al. (2020). Analysis of compound climate extremes and exposed population in Africa under two different emission scenarios. Earth's Future, 8, e2019EF001473. https://doi.org/10.1029/2019EF001473

The climate heat indicator for Africa is based on CORDEX-CORE AFR-22 data. For the calculation of the number of extrem heat days (maximum temperature > 40°C) we use the index_calculator. This is using xclim. In addition to the climate change signal, the robustness is also indicated.

We remapped and calculated timemean of the original population data from:

Franziska Piontek, Tobias Geiger (2017): ISIMIP2b secondary population input data (v1.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.432399

The excel file represents the population statisics of Liberia (Projection 2020).

REFERENCE YEAR: 2020

These tables are suitable for database or GIS linkage to the Liberia - Subnational Administrative Boundaries.https://data.humdata.org/dataset/liberia-admin-level-2-boundaries

VERSION HISTORY 18 March 2020 2020 projections update

7 November 2019 ITOS gazetteer added

18 July 2019 2019 projection updates CSV file uploads

5 April 2018 Initial upload