In 2023, the population of Africa was projected to grow by 2.34 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.3 percent from 2000 onwards, and it peaked at 2.59 percent between 2012 and 2013. Despite a slowdown in the growth rate, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2022, the total population of Africa amounted to around 1.4 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 810 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.9 billion people, compared to 4.7 billion in Asia. The world's youngest continent The median age in Africa corresponded to 18.8 years in 2023. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of 10 percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

In 2023, the share of the population with access to electricity in Sub-Saharan Africa increased by *** percentage points (+*** percent) compared to 2022. With ***** percent, the share thereby reached its highest value in the observed period. Access to electricity refers to the share of the population having the possibility to access electricity

South Africa Population: Mid Year: African: Male: 10 to 14 Years data was reported at 2,229,354.000 Person in 2018. This records an increase from the previous number of 2,161,893.482 Person for 2017. South Africa Population: Mid Year: African: Male: 10 to 14 Years data is updated yearly, averaging 2,046,838.645 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 2,229,354.000 Person in 2018 and a record low of 1,916,836.523 Person in 2013. South Africa Population: Mid Year: African: Male: 10 to 14 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G003: Population: Mid Year: by Group, Age and Sex.

South Africa Population: Mid Year: Eastern Cape: 10 to 14 Years data was reported at 727,015.000 Person in 2018. This records an increase from the previous number of 700,056.903 Person for 2017. South Africa Population: Mid Year: Eastern Cape: 10 to 14 Years data is updated yearly, averaging 728,290.609 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 919,159.039 Person in 2002 and a record low of 644,540.729 Person in 2013. South Africa Population: Mid Year: Eastern Cape: 10 to 14 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G004: Population: Mid Year: by Province, Age and Sex.

(UNCLASSIFIED) This dataset depicts LandScan data (population estimate) for the African countries of Liberia, Sierra Leone, Guinea, Nigeria, Senegal, Gambia, Guinea Bissau, Mali, Benin, Ghana, Togo, Burkina Faso, and Cote D'Ivoire for 2013. The LandScan Global Population Database was developed by Oak Ridge National Laboratory (ORNL) for the United States Department of Defense (DoD). The LandScan (TM) Dataset comprises a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other information. LandScan has been developed as part of the ORNL's Global Population Project for estimating ambient populations at risk. This release represents the 14th version of LandScan and succeeds all previous versions. It is recommended that users of previous versions of LandScan replace any earlier version with LandScan 2013.(UNCLASSIFIED) Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. The unique aspect of population distribution measurement that Landscan methodology provides, and that differs from other population data, is the representation of average population distribution across a variety of socio cultural and economic human activities, not solely where people reside and sleep.

This statistic shows the population change in Sub-Saharan Africa from 2014 to 2024. Sub-Saharan Africa includes almost all countries south of the Saharan desert. In 2024, Sub-Saharan Africa's population increased by approximately 2.44 percent compared to the previous year.

This statistic shows the total population of Sub-Saharan Africa from 2014 to 2024. Sub-Saharan Africa includes all countries south of the Sahara desert. In 2024, the total population of Sub-Saharan Africa amounted to approximately 1.29 billion inhabitants.

This statistic shows the age structure in the Central African Republic from 2013 to 2023. In 2023, about 49.17 percent of the Central African Republic's total population were aged 0 to 14 years.

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

South Africa Population: Mid Year: North West: 10 to 14 Years data was reported at 377,237.000 Person in 2018. This records an increase from the previous number of 349,809.189 Person for 2017. South Africa Population: Mid Year: North West: 10 to 14 Years data is updated yearly, averaging 323,891.788 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 377,237.000 Person in 2018 and a record low of 306,196.965 Person in 2013. South Africa Population: Mid Year: North West: 10 to 14 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G004: Population: Mid Year: by Province, Age and Sex.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and 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..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..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions 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 definitions due to differences in the effective dates of the geographic entities..Telephone service data are not available for certain geographic areas due to problems with data collection. See Errata Note #93 for details. ..Occupation codes are 4-digit codes and are based on Standard Occupational Classification 2010..Industry codes are 4-digit codes and are based on the North American Industry Classification System 2012. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Due to methodological changes to data collection for data year 2013, comparisons of current-year language estimates to past years' language estimates should be made with caution. For more information, see: http://www.census.gov/acs/www/data_documentation/user_notes/.In data year 2013, there were a series of changes to data collection operations that could have affected some estimates. These changes include the addition of Internet as a mode of data collection, the end of the content portion of Failed Edit Follow-Up interviewing, and the loss of one monthly panel due to the Federal Government shut down in October 2013. For more information, see: User Notes.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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013 American Community Survey

South Africa Population: Mid Year: Indian and Asian: 25 to 29 Years data was reported at 124,722.000 Person in 2018. This records an increase from the previous number of 124,273.103 Person for 2017. South Africa Population: Mid Year: Indian and Asian: 25 to 29 Years data is updated yearly, averaging 121,309.750 Person from Jun 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 124,797.624 Person in 2013 and a record low of 93,363.000 Person in 2001. South Africa Population: Mid Year: Indian and Asian: 25 to 29 Years data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.G003: Population: Mid Year: by Group, Age and Sex.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and 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..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..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2009-2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions 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 definitions due to differences in the effective dates of the geographic entities..Telephone service data are not available for certain geographic areas due to problems with data collection. See Errata Note #93 for details. ..Industry codes are 4-digit codes and are based on the North American Industry Classification System (NAICS). The Census industry codes for 2013 and later years are based on the 2012 revision of the NAICS. To allow for the creation of 2009-2013 and 2011-2013 tables, industry data in the multiyear files (2009-2013 and 2011-2013) were recoded to 2013 Census industry codes. We recommend using caution when comparing data coded using 2013 Census industry codes with data coded using Census industry codes prior to 2013. For more information on the Census industry code changes, please visit our website at http://www.census.gov/people/io/methodology/..Census occupation codes are 4-digit codes and are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2010 and later years are based on the 2010 revision of the SOC. To allow for the creation of 2009-2013 tables, occupation data in the multiyear files (2009-2013) were recoded to 2013 Census occupation codes. We recommend using caution when comparing data coded using 2013 Census occupation codes with data coded using Census occupation codes prior to 2010. For more information on the Census occupation code changes, please visit our website at http://www.census.gov/people/io/methodology/..Methodological changes to data collection in 2013 may have affected language data for 2013. Users should be aware of these changes when using multi-year data containing data from 2013..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 dis...

As of January 2024, there were 45.3 million internet users in South Africa. This was an increase of roughly 1.8 million individuals compared to the previous year. The digital population increased significantly from close to 25 million in 2013.

The population density maps presented here for the UNDESERT study areas in Burkina Faso, Benin, Niger and Senegal for 1990, 2000 and 2010 were produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Centro Internacional de Agricultura Tropical (CIAT). CIESIN/CIAT population density grids are available for the entire globe at a 2.5 arc-minutes resolution (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/sets/browse). The UNDESERT project (EU FP7 243906), financed by the European Commission, Directorate General for Research and Innovation, Environment Program, aims to improve the Understanding and Combating of Desertification to Mitigate its Impact on Ecosystem Services in West Africa. Humans originate and contribute significantly to desertification processes. Based on the CIESIN/CIAT population density grids we want to illustrate how population density changed in the UNDESERT study areas and countries during the last 20 years. Data for 1990 and 2000 were downloaded from the Gridded Population of the World, Version 3 (GPWv3) consisting of estimates of human population by 2.5 arc-minute grid cells and associated data sets dated circa 2000. Data for 2010 were copied from the Gridded Population of the World, Version 3 (GPWv3) consisting in a future estimate of human population by 2.5 arc-minute grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics.

Source: http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density Center for International Earth Science Information Network (CIESIN)/Columbia University, and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density. Accessed 28/10/2013 And http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates Center for International Earth Science Information Network (CIESIN)/Columbia University, and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, Future Estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates. Accessed 28/10/2013

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in South Gorin. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In South Gorin, the median household income for the households where the householder is White decreased by $3,706(9.07%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $40,862 in 2011 and $37,156 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in South Gorin, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/south-gorin-mo-median-household-income-by-race-trends.jpeg" alt="South Gorin, MO median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in South Gorin.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Gorin median household income by race. You can refer the same here

Taux de scolarisation de la population du Burkina Faso de 2003-2013

The West Africa Coastal Vulnerability Mapping: Social Vulnerability Indices data set includes three indices: Social Vulnerability, Population Exposure, and Poverty and Adaptive Capacity. The Social Vulnerability Index (SVI) was developed using six indicators: population density (2010), population growth (2000-2010), subnational poverty and extreme poverty (2005), maternal education levels circa 2008, market accessibility (travel time to markets) circa 2000, and conflict data for political violence (1997-2013). Because areas of high population density and growth (high vulnerability) are generally associated with urban areas that have lower levels of poverty and higher degrees of adaptive capacity (low vulnerability), to some degree, the population factors cancel out the poverty and adaptive capacity indicators. To account for this, the data set includes two sub-indices, a Population Exposure Index (PEI), which only includes population density and population growth; and a Poverty and Adaptive Capacity Index (PACI), composed of subnational poverty, maternal education levels, market accessibility, and conflict. These sub-indices are able to isolate the population indicators from the poverty and conflict metrics. The indices represent Social Vulnerability in the West Africa region within 200 kilometers of the coast.

BackgroundAdult height reflects childhood circumstances and is associated with health, longevity, and maternal–fetal outcomes. Mean height is an important population metric, and declines in height have occurred in several low- and middle-income countries, especially in Africa, over the last several decades. This study examines changes at the population level in the distribution of height over time across a broad range of low- and middle-income countries during the past half century.Methods and findingsThe study population comprised 1,122,845 women aged 25–49 years from 59 countries with women’s height measures available from four 10-year birth cohorts from 1950 to 1989 using data from the Demographic and Health Surveys (DHS) collected between 1993 and 2013. Multilevel regression models were used to examine the association between (1) mean height and standard deviation (SD) of height (a population-level measure of inequality) and (2) median height and the 5th and 95th percentiles of height. Mean-difference plots were used to conduct a graphical analysis of shifts in the distribution within countries over time. Overall, 26 countries experienced a significant increase, 26 experienced no significant change, and 7 experienced a significant decline in mean height between the first and last birth cohorts. Rwanda experienced the greatest loss in height (−1.4 cm, 95% CI: −1.84 cm, −0.96 cm) while Colombia experienced the greatest gain in height (2.6 cm, 95% CI: 2.36 cm, 2.84 cm). Between 1950 and 1989, 24 out of 59 countries experienced a significant change in the SD of women’s height, with increased SD in 7 countries—all of which are located in sub-Saharan Africa. The distribution of women’s height has not stayed constant across successive birth cohorts, and regression models suggest there is no evidence of a significant relationship between mean height and the SD of height (β = 0.015 cm, 95% CI: −0.032 cm, 0.061 cm), while there is evidence for a positive association between median height and the 5th percentile (β = 0.915 cm, 95% CI: 0.820 cm, 1.002 cm) and 95th percentile (β = 0.995 cm, 95% CI: 0.925 cm, 1.066 cm) of height. Benin experienced the largest relative expansion in the distribution of height. In Benin, the ratio of variance between the latest and earliest cohort is estimated as 1.5 (95% CI: 1.4, 1.6), while Lesotho and Uganda experienced the greatest relative contraction of the distribution, with the ratio of variance between the latest and earliest cohort estimated as 0.8 (95% CI: 0.7, 0.9) in both countries. Limitations of the study include the representativeness of DHS surveys over time, age-related height loss, and consistency in the measurement of height between surveys.ConclusionsThe findings of this study indicate that the population-level distribution of women’s height does not stay constant in relation to mean changes. Because using mean height as a summary population measure does not capture broader distributional changes, overreliance on the mean may lead investigators to underestimate disparities in the distribution of environmental and nutritional determinants of health.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). The survey collects data on the labour market activities of individuals aged 15 years and above who live in South Africa. The objective of the QLFS is to collect quarterly information about persons in the labour market, i.e., those who are employed; those who are unemployed and those who are not economically active.

The Quarterly Labour Force Survey (QLFS) frame has been developed as a general-purpose household survey frame that can be used by all other household surveys, irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30,000 dwellings per quarter. The sample is based on information collected during the 2001 Population Census conducted by Stats SA. The sample is designed to be representative at provincial level and within provinces at metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms, and tribal. This implies, for example, that within a metropolitan area the sample is representative of the different geography types that may exist within that metro.

Geographic coverage

National Coverage

Analysis unit

Members of households aged 15 years or older.

Universe

The QLFS sample covers the non-institutional population except for workers' hostels. However, 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 sample size for the QLFS is roughly 30,000 dwellings.The sample is based on information collected during the 2001 Population Census conducted by Statistics South Africa (Stats SA). In preparation for Census 2001, the country was divided into 80,787 enumeration areas (EAs). Some of these EAs are small in terms of the number of households that were enumerated in them at the time of Census 2001. The Stats SA household-based surveys use a Master Sample of primary sampling units (PSUs) which comprises EAs that are drawn from across the country. For the purposes of the Master Sample, the EAs that contained fewer than 25 households were excluded from the sampling frame, and those that contained between 25 and 99 households were combined with other EAs of the same geographic type to form primary sampling units (PSUs). The number of EAs per PSU ranges between one and four. On the other hand, very large EAs represent two or more PSUs.

The sample is designed to be representative at provincial level and within provinces at metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms, and tribal. This implies that, for example, within a metropolitan area the sample is designed to be representative at the different geography types that may exist within that metro.

The current sample size is 3,080 PSUs. It is equally divided into four subgroups 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 to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the Labour Force Survey is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The sampled PSUs have been assigned to 4 rotation groups, and dwellings selected from the PSUs assigned to rotation group '1' are rotated in the first quarter. Similarly, the dwellings selected from the PSUs assigned to rotation group '2' are rotated in the second quarter, and so on. Thus, each sampled dwelling will 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, say 2 quarters, and a new household moves in, then 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 (unoccupied). At the end of each quarter, a quarter of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. A total of 3,080 PSUs were selected for the redesigned LFS, and 770 have been assigned to each of the four rotation groups.

Mode of data collection

Face-to-face [f2f]

Research instrument

Contents of the QLFS questionnaire: -Section 1 of the QLFS questionnaire: Biographical information (marital status, language, migration, education, training, literacy, etc.) -Section 2 of the QLFS questionnaire: Economic activities -Section 3 of the QLFS questionnaire: Unemployment and economic inactivity -Section 4 of the QLFS questionnaire: Main work activities in the last week -Section 5 of the QLFS questionnaire: Earnings in the main job (Earnings are published once a year) -All sections of the QLFS questionnaire: Comprehensive coverage of all aspects of the labour market

Sampling error estimates

Because estimates are based on sample data, they differ from figures that would have been obtained from complete enumeration of the population using the same instrument. Results are subject to both sampling and non-sampling errors. Non-sampling errors include biases from inaccurate reporting, processing, and tabulation etc., as well as errors from non-response and incomplete reporting. These types of errors cannot be measured readily. However, to the extent possible, non-sampling errors can be minimised through the procedures used for data collection, editing, quality control, and non-response adjustment. The variances of the survey estimates are used to measure sampling errors.