The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

South Africa ZA: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data was reported at 6.065 % in 2015. South Africa ZA: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data is updated yearly, averaging 6.065 % from Dec 2015 (Median) to 2015, with 1 observations. South Africa ZA: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.; ; UNESCO Institute for Statistics; ;

South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 94.368 % in 2015. This records an increase from the previous number of 94.140 % for 2014. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 92.895 % from Dec 1980 (Median) to 2015, with 9 observations. The data reached an all-time high of 94.368 % in 2015 and a record low of 76.200 % in 1980. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This is a collection of data on International and National Distance Education students studying at UNISA, South Africa. The data includes quantitative survey undertaken with 1,295 students; and interviews with 159 students. The survey is adapted from the School and College Questionnaire to include questions about migration intentions and social media use. The interviews explored themes around migration experiences, adaptation and adjustment to study and choice, challenges and constraints of social media use.Presently, the gross enrolment rate for higher education (HE) across Africa runs at only 8 per cent - the lowest in the world. Yet for policy makers throughout the continent, HE is regarded as a vital tool to bring about sustainable economic development. This is echoed by the United Nations' Sustainable Development Goals, adopted in September 2015, which call for equitable access to high quality tertiary education in their toolkit for ending poverty by 2030. This push for an educated population abuts a reality where, in many African countries, HE demand far outstrips supply and is only addressed by the wealthy through migration. Distance education across national borders is filling that gap. Indeed, one third of student registrations in South Africa, a country where higher education is well established, are made up from this international distance education (IDE) cohort. Despite its importance to the African HE landscape, and its potential contribution to continent-wide development challenges, the workings of IDE remain under-researched. Thus, this project fills a significant and timely gap in knowledge which will generate learning of substantial relevance to social and economic development throughout Africa in the decade to come. This project focuses on two areas vital to the future success of IDE in Africa: equality of access to education, and the quality of that education. Research on IDE in other settings demonstrates that this learning style can improve access for students facing demographic and social disadvantages (including gender, race, and disability, as well as learners studying later in life or learners with caring responsibilities). This project will investigate these issues in the African setting, asking "can IDE can generate equitable access to students from across the continent?" Educational quality is important too. Of the South African student cohort in the year 2000, only 30 per cent graduated within five years with attainment levels. Other research also shows that student retention is markedly lower in students from non-traditional backgrounds. The project will investigate the role of education quality plays here, asking "how can the quality of IDE be assessed, and what improvements can be made to create better student outcomes?" The project will examine IDE delivered by the University of South Africa (the sole provider of DE in South Africa until 2014) to students elsewhere in the continent. Research will collect demographic and socio-economic data, reasons for study, labour market intentions, migration plans and educational experience of student cohorts in three countries, Zimbabwe, Lesotho and Nigeria using both qualitative and quantitative methods. This will be compared with South African students and with students studying face-to-face where this data exists. The project will also build on the OU's Learning Design Initiative (OULDI). Using techniques from this innovative programme, existing student performance and reasons for it will be analysed, changes will be made to learning design, and the effects of the design changes will be tested on the following year's cohort. This knowledge exchange will enable the existing and successful OULDI strategy to be employed in another context, and enhance the future development of the OULDI. Central to the success of this project is a team of researchers from top DE institutions in the UK and South Africa. The two universities are continental leaders in learning pedagogies and have established links with key players in the field of IDE and the project's findings will inform teaching approaches at both institutions. The project will be led by Dr Gunter and Prof Raghuram who have a successful track record of collaborative research on South African international HE, with a strong team of co-investigators from each country. The project is informed by postcolonial theory and politics and aims to experiment with two way learning on IDE globally. The research employed a mixed methodology: an extensive online questionnaire survey with undergraduate UNISA students followed by in-depth individual online interviews. This mixed methodology allows the development of a deep, yet broad-based understanding, potentially producing balanced, rich and meaningful research data. The online questionnaire survey was collected from undergraduate students studying across faculties and related to their overall university experiences. It was based upon prior research on international students and academic adjustment, in particular Rienties et al. 2012). A total of 1295 students responded, representing a 16% response rate, which is considered healthy for online surveys and for UNISA specifically. As part of this survey, questions relating to social media use, access to technology, migration experiences, and demographics were asked. The questionnaire was followed up by 122 one-to-one online follow-up interviews which delved deeper into the experiences and perceptions of different UNISA students. IDE students from Zimbabwe and Namibia, two of the most significant locations of UNISA IDE students, were interviewed. The interviews lasted between 30 and 90 minutes and were conducted via Skype to Skype (audio only) or Skype to phone. These Skype interviews facilitated ‘access to global research participants’ , which along with rise in use of mobile phones, increases the accessibility to research participants, especially in Africa.

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

This table provides the number of grade ones who entered the system 12 years ago, which will provide a useful sense of the drop out rate when one looks at the 2012 matric pass figures. This will allow users to determine the pass rate as a fraction of those who entered the school system 12 years ago. It should show a much smaller pass rate percentage than the official ones, as drop our rates in South Africa are massive. For example last year only 1 out of 10 of the learners who entered the school system in 2000 managed to pass in 2011 in the Eastern Cape. Downloaded data from http://www.education.gov.za/EMISSTATISTICS/StatisticalPublications/tabid/462/Default.aspx as a PDF, isolated the relevant table and scraped to XLS using PDFTOEXCEL online tool

Geographic coverage

The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

Analysis unit

The units of analysis under observation in the South African census 1985 are households and individuals

Universe

The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

Mode of data collection

Face-to-face [f2f]

Research instrument

The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

Data appraisal

UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
Whites 7.6%
Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

Unemployment Rate in South Africa increased to 33.20 percent in the second quarter of 2025 from 32.90 percent in the first quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

Geographic coverage

The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the "independent states" of Bophuthatswana, Transkei, and Venda. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

Households and individuals

Universe

The 1980 South African census covered all household members (usual residents).

The 1980 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980) were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census enumeration data

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons employed as domestics (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

Abstract

The 1991 South African population census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, family and employment details.

Geographic coverage

The South African Census 1991 covered the whole of South Africa. The "homelands" of Transkei, Bophuthatswana, Venda and Ciskei were enumerated separately and the dataset contains data files for Bophuthatswana, Venda and Ciskei. The dataset does not include a data file for the Transkei.

Analysis unit

The units of analysis under observation in the South African census 1991 are households and individuals

Universe

The 1991 Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 7 and 8 March 1991) were therefore enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were enumerated and included in the figures. The Diplomatic and Consular Corps of foreign countries were not included. Crews and passengers of ships were also not enumerated, except those who were present at the harbours of the RSA on census night. Similarly, residents of the RSA who were absent from the night were not enumerated. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Sampling deviation

As a result of the unplanned and unstructured nature of certain residential areas, as well as the inaccessibility of certain areas during the preparations for the enumeration of census, comprehensive door-to-door surveys were not possible. The Human Sciences Research Council had to enumerate these areas by means of sample surveys. 88 areas country-wide were enumerated on this basis.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1991 Population Census questionnaire covered particulars of households: dwelling type, ownership type, type of area (rural/urban) and particulars of individuals: relationship within household, sex, age, marital status, population group, birthplace, citizenship, duration of residency, religion, education level, language, literacy,employment status, occupation, economic sector and income.

ZA: Adolescents Out of School: % of Lower Secondary School Age data was reported at 14.907 % in 2015. This records an increase from the previous number of 6.863 % for 2005. ZA: Adolescents Out of School: % of Lower Secondary School Age data is updated yearly, averaging 11.280 % from Dec 1994 (Median) to 2015, with 9 observations. The data reached an all-time high of 16.081 % in 1994 and a record low of 6.863 % in 2005. ZA: Adolescents Out of School: % of Lower Secondary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

: In South Africa, the interlacing socio-economic challenges of crime and high unemployment have long been a cause for concern. These challenges have also prompted extensive attention from scholars. Thus, the relationship between crime and unemployment emerges as one of the important niche areas of inquiry. The study used time series data from 1995 to 2020 to address this nexus. An Autoregressive Distributive Lag was deemed fit for this study as the data properties point to its use.

Description: The data set measures the views of experts on South African democracy drawing from the relevant indicators of the Varieties of Democracy (V- Dem) project, which include accountability, civil and political liberties, elections and campaigns, the right to franchise, separation of powers, voter participation, civic education, equality before the law, ethical political elite behaviour and political tolerance. Of the targeted population of 159, 51 responses (32%) were realised. The data set contains 57 variables and 51 cases. Abstract: Given that existing global measures of democratic performance suffer from notable shortcomings, the Chicago Centre of Democracy (CCD) intends to construct a new index that is simpler, more transparent, and uses a more rational aggregation technique to assess democratic health in a country. The aim is for this index to be simple enough that non-technical individuals can understand it. This project addresses a gap in the availability of tools that track improvements in and regressions of democratic performance. In particular, the project focuses on insufficiencies in three interrelated areas of cross-national tracking of democracy: Global measurement of democratic performance - Various societal actors have a need for accurate tracking of national democratic health. These include civil society organizations, multilateral organizations, governmental foreign ministries, journalists, and others. Yet existing democracy indices are extremely complex, use simplistic aggregation methodologies, and have difficulty explaining why scores change year to year. Each of these stakeholder groups has the potential to benefit from a new, robust, transparent democratic performance index. Rhetorical strategies of populist leaders - A growing number of elected leaders come into office through democratic means, but then proceed to threaten democratic institutions without overly violating the law. How are voters and watchdog groups to know that such politicians represent a threat before they come to power? With the right type of analysis, the campaign statements of politicians can provide clues. By using a machine learning approach to analyse the campaign speeches of politicians across a dozen countries, CCD will create a tool that will allow users to see rhetorical patterns within and across speeches from politicians around the world. The role of referendums - Referendums are often considered a tool of "direct democracy", in that they provide the voting population a direct say in matters of policy importance. However, many questions are unanswered about referendums, such as why they are initiated, the role of special interest groups, and why the results are often considered suboptimal. CCD will create a publicly available database of national referendums from 1960 to present, organized by category, country, results, and other variables. This tool will help those involved in designing or campaigning for referendums to understand how they can be structured and implemented most effectively. The CCD aims to complete regional surveys in seven countries: Brazil, Peru, Poland, South Africa, South Korea, Turkey, and the United Kingdom to create a set of composite weightings of the democratic principles, such that CCD will develop scores for nearly every country in the world drawing on expert surveys. Web-based self-completion Individuals who are experts in political science/studies. This includes people who hold Masters' degree in political science and lecturers in the field of study. Building on Christopolous (2007) methodological conceptualisation of implementing expert surveys, this study draws on social network analysis, which entails targeted snowball sampling where one accessed a group of small closely knit populations (such as experts in political studies/sciences), where one constructs the research population through snowball sampling in order to improve reliability and validity of the data (Christopolous, 2007). To determine who holds authoritative opinion on democracy in South Africa, as well as to determine the size of the expert population on democracy in South Africa, a database of political scientists at institutions of higher education was created. For the purposes of this study, an expert in political science/studies is conceptualised as a person who holds at minimum a Master's degree, is employed (either full-time or part-time) as academic staff in a Department of Political Science/Studies at an institution of higher education, and conducts research into fields that constitute political science/studies. Since academic staff refers to individuals that hold the positions of Lecturer, Senior Lecturer, Associate Professor, and Professor. The research population is limited to lecturer level as individuals at this level would require a minimum of a Master's degree, and as such, would have studied political sciences for a minimum period of 6 years, assuming they completed their qualification at Master's level in 2 years. As a result, from an audit of political science/studies departments across South Africa's 26 public universities, of the 26 public universities, 16 have political science departments with a combined total staff complement of 229. We liaised with relevant heads of department as well as the South African Association of Political Studies to circulate the online survey among members. Given the focus on a minimum of a Master's degree in political sciences, the eligible research population was 159.

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis. Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: To provide statistics on population, demographic, social, economic and housing characteristics; To provide a base for the selection of a new sampling frame; To provide data at lowest geographical level; and To provide a primary base for the mid-year projections.

Context

Education plays a critical role in the development of a child's future and ultimately country's development.

Content

Unicef has collected household survey data from the past 10 years for the calculation of adjusted net attendance rate. For countries with multiple years of data, the most recent dataset is used.

More details about the 2 columns in the dataset. Region, Sub-region EAP East Asia and the Pacific ECA Europe and Central Asia EECA Eastern Europe and Central Asia ESA Eastern and Southern Africa LAC Latin America and the Caribbean MENA Middle East and North Africa NA North America SA South Asia SSA Sub-Saharan Africa WCA West and Central Africa

Acknowledgements

The dataset is sourced from Unicef.

Inspiration

The dataset can be used to draw critical insights about the out of school rates.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation for the data is Province

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are 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]

Data appraisal

In the report for the 2016 LMDSA Statistics South Africa have included the following "cautionary notes":

Mining: Caution is required when making conclusions based on the industrial profile of employed persons, since the clustered nature of the Mining industry means that it might not have been adequately captured by the QLFS sample. Alternative mining estimates are also included in the Quarterly Employment Statistics (QES).

2013 Master Sample: In 2015, Stats SA introduced a new master sample based on the Census 2011 data (2013 Master Sample). A number of improvements took place, including efforts to improve Mining estimates through the inclusion of Mining strata in provinces where employment in this industry was more than 30% of total employment. In addition, estimates of labour market indicators at a metro level was also published for the first time.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation for the data is Province

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are 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]

South Africa ZA: Educational Attainment: At Least Completed Upper Secondary: Population 25+ Years: Male: % Cumulative data was reported at 66.649 % in 2015. This records an increase from the previous number of 65.796 % for 2014. South Africa ZA: Educational Attainment: At Least Completed Upper Secondary: Population 25+ Years: Male: % Cumulative data is updated yearly, averaging 54.374 % from Dec 1970 (Median) to 2015, with 16 observations. The data reached an all-time high of 66.649 % in 2015 and a record low of 8.755 % in 1985. South Africa ZA: Educational Attainment: At Least Completed Upper Secondary: Population 25+ Years: Male: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Education Statistics. The percentage of population ages 25 and over that attained or completed upper secondary education.; ; UNESCO Institute for Statistics; ;

ABSTRACT: The 1970 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria). Variables include: Particulars of dwellings- type of dwelling, number of rooms, and ownership; Particulars of person- relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, duration of residence at normal dwelling, religion/denomination, languages and literacy, level of education, sport and recreation, occupation, work status, identity of employer, economic sector and income; Particulars of the family including children at boarding school, university or undergoing military training.

Abstract

This dataset includes imputation for missing data in key variables in the ten percent sample of the 2001 South African Census. Researchers at the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford used sequential multiple regression techniques to impute income, education, age, gender, population group, occupation and employment status in the dataset. The main focus of the work was to impute income where it was missing or recorded as zero. The imputed results are similar to previous imputation work on the 2001 South African Census, including the single ‘hot-deck’ imputation carried out by Statistics South Africa.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]