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.

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.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated Although South Africa’s yearly population growth has been dropping since 2013, the growth rate still stood above the world average in 2021. That year, the global population increase reached 0.94 percent, while for South Africa, the rise was 1.23 percent. The majority of the people lived in the borders of Gauteng, the smallest of the nine provinces in land area. The number of people residing there amounted to 15.9 million in 2021. Although Western Cape was the third-largest province, one of it cities, Cape Town, had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the third quarter of 2022, the unemployment rate reached close to 60 percent and 42.9 percent among people aged 15-24 and 25-34 years, respectively. In the same period, some 25 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 71.2 percent.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Total Scheduled Flight: Guangdong: Guangzhou-South Africa: Johannesburg data was reported at 1.000 Unit in 28 Oct 2019. This stayed constant from the previous number of 1.000 Unit for 07 Oct 2019. Total Scheduled Flight: Guangdong: Guangzhou-South Africa: Johannesburg data is updated weekly, averaging 1.000 Unit from Sep 2019 (Median) to 28 Oct 2019, with 5 observations. The data reached an all-time high of 2.000 Unit in 30 Sep 2019 and a record low of 1.000 Unit in 28 Oct 2019. Total Scheduled Flight: Guangdong: Guangzhou-South Africa: Johannesburg data remains active status in CEIC and is reported by VariFlight. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Scheduled Flight: Departure: Guangdong.

This dataset is about cities in Johannesburg, featuring 7 columns including city, continent, country, latitude, and longitude. The preview is ordered by population (descending).

As of 2022, the number of households in South Africa increased and amounted to approximately 18.48 million, roughly 530,000 more than in the previous year. Between 2002 and 2022, the number of families in South Africa grew by around 65 percent. Looking at the number of households from a regional perspective , the Gauteng province (includes the city of Johannesburg) has the bulk of households, with almost 5.6 million residences. Although Gauteng is the smallest region in the country, it is highly urbanized and houses most of the population.

Households headed by women

The number of households headed by women averaged around 42 percent. Rural areas such as the Eastern Cape and Limpopo had a higher proportion of women in charge of their family unit. Urbanized regions, namely Gauteng and the Western Cape, were more likely to be headed by men.

Languages spoken in households

The most spoken language within and outside of South African households was isiZulu, with around 25 percent of the population utilizing it. The English language was the second most common language spoken outside of households, with a share of roughly 17 percent. However, within households, individuals preferred to speak other official languages such as isiXhosa and Afrikaans. South Africa has a diverse range of cultures, and language plays a crucial role in preserving these cultures.

Total Operated Flight: South Africa: Johannesburg-Hong Kong SAR (China) data was reported at 12.000 Unit in 10 Mar 2025. This stayed constant from the previous number of 12.000 Unit for 03 Mar 2025. Total Operated Flight: South Africa: Johannesburg-Hong Kong SAR (China) data is updated weekly, averaging 9.000 Unit from Dec 2018 (Median) to 10 Mar 2025, with 138 observations. The data reached an all-time high of 56.000 Unit in 01 Jul 2019 and a record low of 6.000 Unit in 01 Apr 2024. Total Operated Flight: South Africa: Johannesburg-Hong Kong SAR (China) data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Operated Flight: Arrival: Hong Kong SAR (China).

Abstract

This report has two main objectives: firstly, to present the key findings of the GHS 2011 in the context of the trends that were measured since the first GHS was conducted in 2002; and secondly, to provide a more in-depth analysis of the detailed questions related to selected service delivery issues.

Geographic coverage

All private households all nine provinces of South Africa.

Analysis unit

• Households
• Individuals

Universe

The survey covers all household members (usual residents) of households in the 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-institutionalised and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Periodicity of Data collection: Annual.

Sampling procedure

A multi-stage design was used, which is based on a stratified design with probability proportional to size selection of primary sampling units (PSUs) at the first stage and sampling of dwelling units (DUs) with systematic sampling at the second stage. After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2001 data (secondary stratification). Survey officers employed and trained by Stats SA visited all the sampled dwelling units in each of the nine provinces. During the first phase of the survey, sampled dwelling units were visited and informed about the coming survey as part of the publicity campaign. The actual interviews took place four weeks later. A total of 25 653 households (including multiple households) were successfully interviewed during face-to-face interviews.

Sampling deviation

The sample design for the GHS 2011 was based on a master sample (MS) that was originally designed for the QLFS and was used for the first time for the GHS in 2008. This master sample is shared by the Quarterly Labour Force Surveys (QLFS), General Household Survey (GHS), Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Surveys (IES). The master sample used a two-stage, 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 and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Research instrument

Most questions in the GHS questionnaire are pre-coded, i.e. there are a set number of choices from which one or more must be selected. For open-ended 'write-in' questions, the description will state that post-coding occurred and explain how this was done. Most variables have been pre-coded from the questionnaire and are not repeated in the variable description. Where the coding is not apparent, the description either provides the codes or indicates where code lists are to be found. One limitation of th study mentions that, it is important to note that the questionnaires for the GHS series were revised extensively in 2009 and that some questions might not be exactly comparable to the data series before then. The details of the questions included in the GHS questionnaire are covered in four sections, each focusing on a particular aspect. Depending on the need for additionalinformation, the questionnaire is adapted on an annual basis. New sections may be introduced on a specific topic for which information is needed or additional questions may be added to existing sections. Likewise, questions that are no longer necessary may be removed. The GHS questionnaire has undergone some revisions over time. These changes were primarily the result of shifts in focus of government programmes over time. The 2002–2004 questionnaires were very similar. Changes made to the GHS 2005 questionnaire included additional questions in the education section with a total of 179 questions. Between 2006 and 2008, the questionnaire remained virtually unchanged. In preparation for GHS 2009. Extensive stakeholder consultation took place during which the questionnaire was reviewed to be more in line with the monitoring and evaluation frameworks of the various government departments. Particular sections that were modified substantially during the review were the sections on education, social development, housing, agriculture, and food security. Even though the number of sections and pages in the questionnaire remained the same, questions in the GHS 2009 were increased from 166 to 185 between 2006 and 2008. Following the introduction of a dedicated survey on Domestic Tourism, the section on tourism was dropped for GHS 2010. Due to a further rotation of questions, the GHS 2011 questionnaire contained 166 questions as follows:

Contents of the GHS 2011 questionnaire

Section Number of Details of each section questions Cover page Household information, response details, field staff information, result codes, etc. Flap 6 Demographic information (name, sex, age, population group, etc.) Section 1 55 Biographical information (education, health, disability, welfare) Section 2 20 Economic activities Section 3 65 Household information (type of dwelling, ownership of dwelling, electricity, water and sanitation, environmental issues, services, transport, etc.) Section 4 20 Food security, income and expenditure (food supply, agriculture, expenditure, etc.) All sections 166 Comprehensive coverage of living conditions and service delivery

Cleaning operations

Historically the GHS used a conservative and hands-off approach to editing. Manual editing, and little if any imputation was done. The focus of the editing process was on clearing skip violations and ensuring that each variable only contains valid values. Very few limits to valid values were set and data were largely released as it was received from the field. With GHS 2009, Stats SA introduced an automated editing and imputation system that was continued for GHS 2010 and GHS 2011. The challenge was to remain as much as possible true to the conservative approach used prior to GHS 2009 and yet, at the same time, to develop a standard set of rules to be used during editing which could be applied consistently across time. When testing for skip violations and doing automated editing, the following general rules are applied in cases where one question follows the filter question and the skip is violated:

• If the filter question had a missing value, the filter is allocated the value that corresponds with the subsequent question which had a valid value. • If the values of the filter question and subsequent question are inconsistent, the filter question’s value is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the question subsequent to the filter question is dealt with by either setting it to missing and imputing or if that fails printing a message of edit failure for further investigation, decision-making and manual editing.

In cases where skip violations take place for questions where multiple questions follow the filter question, the rules used are as follows: • If the filter question has a missing value, the filter is allocated the value that corresponds with the value expected given the completion of the remainder of the question set. • If the filter question and the values of subsequent questions values were inconsistent, a counter is set to see what proportion of the subsequent questions have been completed. If more than 50% of the subsequent questions have been completed the filter question’s value is modified to correspond with the fact that the rest of the questions in the set were completed. If less than 50% of the subsequent questions in the set were completed, the value of the filter question is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the questions in the set that follows the filter question are set to missing.

Response rate

Response rates per province, 2011

Province Per cent Western Cape 91,3 Eastern Cape 98,9 Northern Cape 94,1 Free State 97,3 KwaZulu-Natal 99,2 North West 97,0 Gauteng

Total Scheduled Flight: Hong Kong SAR (China)-South Africa: Johannesburg data was reported at 12.000 Unit in 17 Feb 2025. This stayed constant from the previous number of 12.000 Unit for 10 Feb 2025. Total Scheduled Flight: Hong Kong SAR (China)-South Africa: Johannesburg data is updated weekly, averaging 9.000 Unit from Dec 2018 (Median) to 17 Feb 2025, with 135 observations. The data reached an all-time high of 56.000 Unit in 01 Jul 2019 and a record low of 9.000 Unit in 23 Dec 2024. Total Scheduled Flight: Hong Kong SAR (China)-South Africa: Johannesburg data remains active status in CEIC and is reported by VariFlight. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Scheduled Flight: Departure: Hong Kong SAR (China).

Total Cancelled Flight: South Africa: Johannesburg-Guangdong: Guangzhou data was reported at 1.000 Unit in 14 Oct 2019. This stayed constant from the previous number of 1.000 Unit for 07 Oct 2019. Total Cancelled Flight: South Africa: Johannesburg-Guangdong: Guangzhou data is updated weekly, averaging 1.000 Unit from Sep 2019 (Median) to 14 Oct 2019, with 4 observations. The data reached an all-time high of 2.000 Unit in 23 Sep 2019 and a record low of 1.000 Unit in 14 Oct 2019. Total Cancelled Flight: South Africa: Johannesburg-Guangdong: Guangzhou data remains active status in CEIC and is reported by VariFlight. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Cancelled Flight: Arrival: Guangdong.

Total Cancellation Rate: South Africa: Johannesburg-Hong Kong SAR (China) data was reported at 0.000 % in 17 Feb 2025. This stayed constant from the previous number of 0.000 % for 10 Feb 2025. Total Cancellation Rate: South Africa: Johannesburg-Hong Kong SAR (China) data is updated weekly, averaging 0.000 % from Dec 2018 (Median) to 17 Feb 2025, with 135 observations. The data reached an all-time high of 45.450 % in 01 Apr 2024 and a record low of 0.000 % in 17 Feb 2025. Total Cancellation Rate: South Africa: Johannesburg-Hong Kong SAR (China) data remains active status in CEIC and is reported by VariFlight. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Cancellation Rate: Arrival: Hong Kong SAR (China).

China Total Cancellation Rate: Beijing-South Africa: Johannesburg data was reported at 0.000 % in 17 Feb 2025. This stayed constant from the previous number of 0.000 % for 10 Feb 2025. China Total Cancellation Rate: Beijing-South Africa: Johannesburg data is updated weekly, averaging 0.000 % from Dec 2018 (Median) to 17 Feb 2025, with 166 observations. The data reached an all-time high of 66.670 % in 03 Apr 2023 and a record low of 0.000 % in 17 Feb 2025. China Total Cancellation Rate: Beijing-South Africa: Johannesburg data remains active status in CEIC and is reported by VariFlight. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Cancellation Rate: Departure: Beijing.

Total Operated Flight: Guangdong: Shenzhen-South Africa: Johannesburg data was reported at 6.000 Unit in 27 Jan 2025. This stayed constant from the previous number of 6.000 Unit for 20 Jan 2025. Total Operated Flight: Guangdong: Shenzhen-South Africa: Johannesburg data is updated weekly, averaging 6.000 Unit from Dec 2018 (Median) to 27 Jan 2025, with 163 observations. The data reached an all-time high of 8.000 Unit in 23 Dec 2019 and a record low of 1.000 Unit in 20 Mar 2023. Total Operated Flight: Guangdong: Shenzhen-South Africa: Johannesburg data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TM: VariFlight Flight Statistics: Total Operated Flight: Departure: Guangdong.

his dataset captures observations of consumer visits to three major shopping malls in Johannesburg, South Africa, from 2022 to 2023. The data, sourced from Fetch Analytics, utilizes ​​​​smartphone​​​​ signal tracking to provide insights into consumer behaviour. Key variables include mall name, visit frequency, distance travelled, and demographic indicators such as income and Living Standard Measure (LSM). The dataset allows for a granular analysis of how spatial and socio-economic factors influence shopping patterns in a fragmented retail landscape. This dataset is valuable for researchers investigating consumer behaviour, spatial economics, and urban retail planning.

Abstract

The South Africa Enterprise Survey was conducted between January and December 2007. Data from 1057 establishments in private manufacturing and services sectors were analyzed. The sample included enterprises with more than four employees (937 companies) as well as micro firms, establishments with less than 5 workers, (120 observations). The survey targeted establishments in Johannesburg, Cape Town, Port Elizabeth and Durban.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The South Africa Enterprise Survey 2007 included enterprises with more than four employees as well as micro establishments, firms with less than five workers. There are 120 micro establishments in the sample.

The sample for enterprises with more than four employees was designed using stratified random sampling with strata defined by region, sector and firm size.

Establishments located in Johannesburg, Cape Town, Port Elizabeth and Durban were interviewed.

Following the ISIC (revision 3.1) classification, the following industries were targeted: all manufacturing sectors (group D), construction (group F), retail and wholesale services (subgroups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub-group 72 of group K). For establishments with five or more full-time permanent paid employees, this universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15), Machinery and Equipment (Group D, sub-group 29), Electrical Machinery and Equipment (Group D, sub-group 31); 2. Manufacturing: Textiles (Group D, sub-group 17), Garment (Group D, sub-group 18), Leather and Footwear (Group D, sub-group 19), Paper and Paper Products (Group D, sub-group 21), Printing and Publishing (Group D, sub-group 22); 3. Manufacturing: Non-Metallic Mineral Products (Group D, sub-group 26), Basic Metals (Group D, sub-group 27), Fabricated Metal Products (Group D, sub-group 28); 4. Manufacturing: Wood and Wood Products (Group D, sub-group 20), Furniture (Group D, sub-group 36) 5. Manufacturing: Refined Petroleum Products (Group D, sub-group 23), Chemical Products (Group D, sub-group 24), Rubber and Plastics (Group D, sub-group 25) 6. Retail Trade: (Group G, sub-group 52); 7. Rest of the universe, including: • Other Manufacturing (Group D excluding sub-groups in strata 1-5); • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

The implementing agency (EEC Canada) was unable to obtain a satisfactory sample frame from South African statistical agency (STASA) or its Department of Revenue. The best alternative solution was a list obtained from the Department of Trade and Industry Companies and Intellectual Property Registration Office (CIPRO), which contained about 800000 establishments when delineating in-scope cities and industries, but which had incomplete firm characteristics necessary for stratification purposes (e.g. contact information, size). In order to determine the sample frame, EEC Canada randomly drew 9550 units and contacted them.

In South Africa, the survey included panel data collected from establishments surveyed in the 2003 Investment Climate Survey (ICS) of South Africa. That survey included establishments in the manufacturing and the rest of universe strata, distributed across Gauteng (Johannesburg), KwaZulu Natal (Durban), Western Cape (Cape Town) and Eastern Cape (Port Elizabeth) provinces.

In order to collect the largest possible set of panel data, an attempt was made to contact and survey valid establishments (579) in the panel list provided which was part of the Enterprise Survey's scope. Of the 716 establishments provided to EEC Canada from those surveyed in 2003, there were 35 doubles, 8 out-of-scope, 89 excluded from this survey by The World Bank to avoid over representing Construction in a single Residual stratum, and 5 with undefined ISIC codes. This left a total potential of 579 panel establishments. EEC Canada surveyed 231 panel establishments or 40% of the total potential panels without eliminating those establishments which had closed. Once eliminated, this percentage coverage exceeded 55%. Given the non-random nature of panel establishment selection, these establishments are not allocated probability weights in the final dataset.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees located in Johannesburg. The implementing agency selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all states of the survey.

First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to local sources. The EEC team then went in the field to verify the sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At the head office, the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Johannesburg, enumerators were sent to each zone with instructions how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

For complete information about sampling methodology, refusal rate and weighting please review "South Africa Enterprise Survey 2007 Implementation Report" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro

This statistic shows the top ten largest municipalities in South Africa as of 2016. Johannesburg had the largest population of South African municipalities in 2016, with nearly 5 million inhabitants.

Abstract

This dataset is from the Gauteng City-Region Observatory which is a partnership between the University of Johannesburg, the University of the Witwatersrand, the Gauteng Provincial Government and several Gauteng municipalities. The GCRO has conducted previous Quality of Life Surveys in 2009 (Round 1), 2011 (Round 2), 2013-2014 (Round 3) and 2015-2016 (Round 4), and 2017-2018 (Round 5). Round 6 was conducted in 2020-2021 and is the latest round of the survey.

Geographic coverage

The survey covers the Gauteng province in South Africa.

Analysis unit

Households and individuals

Universe

The survey covers all adult residence in Gauteng province, South Africa.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

South Africa Bond Market: Turnover: Face Value data was reported at 3,232,635.000 ZAR mn in Oct 2018. This records an increase from the previous number of 2,428,112.000 ZAR mn for Sep 2018. South Africa Bond Market: Turnover: Face Value data is updated monthly, averaging 1,078,276.000 ZAR mn from Jul 1994 (Median) to Oct 2018, with 292 observations. The data reached an all-time high of 3,232,635.000 ZAR mn in Oct 2018 and a record low of 107,426.000 ZAR mn in Dec 1994. South Africa Bond Market: Turnover: Face Value data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z013: Johannesburg Stock Exchange: Bond Market.