In 2018, tertiary education attainment was highest among the White population in South Africa, with around 43.2, 41.3 and 5.6 percent of the individuals associated with Generation X, Millennials, and Born-free Millennials, respectively. Moreover, compared to the generation of Millennials, tertiary school completion was higher in all population groups in Generation X, except among Indians/Asians. Furthermore, the total share of Millennials who received a tertiary education (12.9 percent) was slightly lower than that of Generation X. However, the source indicates that a possible explanation to that is that some of the Millennials were still obtaining their higher education degrees.

In 2018, Western Cape had the highest attendance rate of Millennials between the ages of 23 and 38 who were in tertiary education. KwaZulu-Natal and Gauteng followed with 54.5 percent and 53.5 percent, respectively. By comparison, Limpopo had the lowest share of Millennials attending tertiary education institutions, at 29.3 percent.

South Africa ZA: School Enrollment: Secondary: Male: % Gross data was reported at 103.519 % in 2015. This records an increase from the previous number of 97.231 % for 2014. South Africa ZA: School Enrollment: Secondary: Male: % Gross data is updated yearly, averaging 85.812 % from Dec 1989 (Median) to 2015, with 21 observations. The data reached an all-time high of 103.519 % in 2015 and a record low of 58.028 % in 1989. South Africa ZA: School Enrollment: Secondary: Male: % Gross 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. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.; ; 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).

Between the years 2015 to 2022, the percentage rate of individuals aged 20 years and older in South Africa who have attained 12th grade has generally been increasing from 28.3 percent to 34.6 percent. Individuals without any school education at all have decreased from five percent to 3.3 percent within the given time period.

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).

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2023 series only includes data for quarter 2 and quarter 3. The GIS coordinates for schools in the Eastern Cape are incorrectly entered in the original data from the DBE. The data entered in the GIS_long variable is incorrectly entered into the GIS_lat variable. This issue only occurs for schools in the Eastern Cape (EC), all other GIS coordinates for all the other provinces is correct. Therefore, for geospatial analysis, users can swap the GIS coordiate data only for the Eastern Cape.

This statistic shows the total number of students that graduated from postsecondary institutions in South Africa in 2015, by field of study and race. In 2015, a total of 27,337 African students earned a degree in education.

EDUBASE is a database of educational information relating to the field of black education in South Africa, covering the period from the nineteenth century to 1992. The resources curated by the EDUBASE collection including books and book chapters, journal articles, conference papers and presentations, white papers and other forms of governmental and civil society documents. In total, EDUBASE includes nearly 9000 publications on the topic of education for black South Africans. A separate database inspired by EDUBASE focusing specifically on education in the Transkei was produced by David Hiscock, and has been added to this collection. The database was originally located on UCT's servers. Over time, it migrated formats several times, from floppy disks to stiffy disks, then to CD-ROMs, a flashdrive, and finally to cloud-based storage provided here on ZivaHub. The School of Education at UCT kindly allocated space for the project up to 1992, and the physical collection now resides at UCT Libraries Special Collections, as the 'BC1584 Edubase Collection'. This collection consists of the database files in MS Access format (EDUBASE_Kallaway.mdb and EDUBASE_Hiscock_Transkei.mbd), along with a list of keywords in .csv format, and a set of instructions on how to navigate the databases as a PDF. The database itself is searchable by keyword, author or date of publication. Open versions of both databases have also been added in .csv format. The funding for the project was made possible by the authors of Apartheid and Education who agreed to donate their share of the royalties to a fund that was established for this purpose. Various people have worked on the EDUBASE over the years, including Jackie Kallaway, Deirdre Birch, Vera Hulley, David Hiscock and Debbie Sheward. Many thanks to them for making possible the development this invaluable tool for the advancement of research in this important area. Thanks also go to Gary November of the UCT Digitisation unit, who digitised the cover of the original EDUBASE printout; Adrianna Pinska and Corne Oosthuizen for their support in migrating the content from the .mdb files to open .csv spreadsheets. The references fields below contain links to Dr Peter Kallaway’s other publications on education for Black South Africans.

Abstract

The SACMEQ III Project commenced in 2006 and was completed during 2011. The SACMEQ III data collection was implemented in fifteen SACMEQ Ministries of Education (Botswana, Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania (Mainland), Tanzania (Zanzibar), Uganda, Zambia, and Zimbabwe). The SACMEQ III Project followed the general research direction of the first two SACMEQ Projects by focusing on an examination of the conditions of schooling in relation to achievement levels of learners and their teachers in reading, and mathematics. The focus was expanded to cover the learners’ levels of basic knowledge about HIV and AIDS. The SACMEQ III Project involved data collections from around 61,000 learners, 8,000 teachers, and 2,800 school principals.

Geographic coverage

Tanzania Mainland

Analysis unit

• Pupils
• Schools
• Teachers

Universe

The desired target population for the SACMEQ III study was defined as "All pupils at Grade 6 level in 2007 (at the first week of the eighth month of the school year) who were attending registered mainstream primary schools". This definition used a grade-based description (and not an age-based description) of pupils because an age-based description would have required the collection of data across many grade levels due to the high incidences of "late starters" and grade repetition in SACMEQ school systems.The excluded population consists of those schools and pupils that have been excluded from the desired population to give the defined target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The desired target population definition for the SACMEQ III Project was exactly the same (except for the year) as was employed for the SACMEQ I and II Projects. This consistency was maintained in order to make valid cross-national and cross-time estimates of "change" in the conditions of schooling and the quality of education.

The SACMEQ III data were selected using a stratified two-stage cluster sample design based on the technique of a lottery method of sampling proportional to size, with the assistance of SAMDEM software (Sylla et al., 2003). At the first stage, schools were selected in each region (province) in proportion to the number of pupils in that region in the defined target population. The main reason for choosing Region as the explicit stratification variable was that the SACMEQ Ministries of Education wanted to have education administration regions as "domains" for the study. That is, the Ministries wanted to have a reasonably accurate sample estimates of population characteristics for each region. At the second stage, a simple random sample of 25 pupils was taken within each selected school (in the Seychelles, all Grade 6 pupils in all 25 schools in the island country were tested).

In educational survey research the primary sampling units that are most often employed (schools) are rarely equal in size. This variation causes difficulties with respect to the control of the total sample size when schools are selected with equal probability at the first stage of a multi-stage sample design. One method of obtaining greater control over the total sample size is to stratify the schools accorging to size and then select samples of schools within each stratum. A more widely applied alternative is to employ probability proportional to size (PPS) sampling of schools within strata followed by the selection of a simple random sample size and results in epsem sampling of pupils within strata. The lottery method of PPS selection was implemented for the SACMEQ Projects with the assistance of the SAMDEM software (Sylla et al, 2003).

In order to avoid selection bias, precautions were taken to ensure that school heads and teachers did not have any influence over the sampling procedures within schools. This is because school heads and teachers might have felt they had a vested interest in selecting particular kinds of pupils, and this could have resulted in major distortions of sample estimates (Brickell, 1974).

Mode of data collection

Face-to-face [f2f]

Research instrument

• Apart from pupil achievement scores, SACMEQ studies are renowned for collecting a wide range of information about pupils, teachers, classrooms, school heads, schools, and school communities. For the SACMEQ III study, four main questionnaires (pupil, teacher, school head, and school information) were used.
• It is important to note that SACMEQ questionnaires were subjected to careful thought, thorough examination, and stringent refinement before they were administered. For example, for the SACMEQ III study, the questionnaires were developed by a committee of experts consisting of members drawn from all SACMEQ countries, SCC staff, IIEP staff, and private consultants, following: (a) Field experiences gained from the SACMEQ II study, (b) Recommendations arising from analyses of SACMEQ II data, and (c) policy questions raised by SACMEQ country ministries of education. These questionnaires were refined by the SACMEQ scientific committee, then piloted in each SACMEQ country and refined further before they were administered.
• One important innovation in the development of questionnaires for the SACMEQ III study was introduction of a “Homework form” for pupils to take home. This consisted of questions to which the pupil might not know the answers (for example, parental education, estimates of travel distance to school, home possessions, whether or not their biological parents were alive) that parents, family members, or guardians could help in filling in. This considerably reduced the number of missing values in the SACMEQ III study compared with previous SACMEQ studies.
• Materials were translated into Kiswahili (Tanzania Mainland and Zanzibar) and Portuguese (Mozambique).

Cleaning operations

Data Entry was done using WinDEM (Windows Data Entry Manager) Software. Preliminary data cleaning involved checks on data to ensure it was clean before it was sent to the SACMEQ Coordinating Centre (SCC) for further checks an analysis and calculation of sampling weights. (See p12 of the NRC Manual - provided as external resources - for more detail on the process.)

Data appraisal

The quality of the data provided by the school heads, teachers, and pupils was examined in the following ways. First, at the time of data collection, the data collectors who visited the schools verified, for example (a) The actual existence and conditions of the school resources such as library, school head office, and staff room, and (b) The official school records about the information provided by pupils such as their gender, age, days absent, and whether or not their parents were alive. Second, similar questions were included in the school head, pupil, and teacher questionnaires, and these helped to verify the responses given by the respondents during data cleaning. For example, a question on the existence of a class library was included in both the teacher and pupil questionnaires. Any inconsistencies between the responses of the school heads, teachers, and pupils were followed up by the national research coordinators (NRCs) and corrected during data cleaning. The processes of generating pupil scores, competency levels, measure of school location, socioeconomic status and tabulations are outlined in the SACMEQ-III Project Results Working Document Number 1 available as external resources.

In 2018, the population group in South Africa with the highest share in primary education was Black African. This represented **** percent of the share of children between the ages of *** and ** attending primary educational institutions in the country. Moreover, some **** percent of the Colored children were enrolled in primary education. The population group with the lowest level of enrollment in primary education was the Asian/Indian population, at **** percent.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2021 series only includes data for quarter 1 and quarter 2.

Abstract

Despite the many useful studies on the use of Open Educational Resources (OER) in higher education, most are focused on the activity of students and instructors in the Global North who enjoy comparatively higher levels of economic development, educational provision, policy elaboration, and technological access than those in the Global South – the region where OER is touted as having its potentially greatest impact. This dataset arises from a survey focusing on higher education instructors and students in South America, Sub-Saharan Africa, and South and Southeast Asia. This was a cross-regional survey of 295 instructors at 28 universities in nine countries, Brazil, Chile, Colombia, Ghana, Kenya, South Africa, India, Indonesia, Malaysia. This research seeks to establish a baseline of empirical data for assessing OER awareness and use in the Global South.

The overarching research questions that this study set out to answer are: 1. What proportion of instructors in the Global South have ever used OER? 2. Which variables may account for different OER usage rates between respondents in the Global South?

In order to address these questions, survey responses were correlated against the question (26) of the survey which directly addresses OER usage: "Have you ever used OER that are available in the public domain or has an open license (e.g. Creative Commons) that allows it to be used and/or adapted by others?" A core purpose of the overarching ROER4D project is the development of an empirical baseline of OER and Open Educational Practice (OEP) activity in the Global South. OER itself is a novel concept, and is tied to a broader spectrum of OEP that overlap with, but do not always exactly coincide with, formal OER practice. As such, an investigation into the use, reuse, adaptation, and sharing practices performed by higher education instructors, and the digital infrastructure and foundational literacies that underpin these practices (regardless of their knowledge of formal OER activity) is integral in ascertaining baseline practice. This dataset includes responses by instructors who engage in reuse and sharing activities, irrespective of whether they have consciously used OER in their practice. As such, it offers insights into the practices that exist outside of formally-labelled OER production. Dimension 2 of the survey instrument "Educational Resources" is framed around general practice relating to sharing, use, reuse, creation, and licensing of educational materials, rather than OER per se. Data arising from these responses are to be treated with caution in terms of making inferences around OER, but remain useful in terms of gaining a more informed sense of instructors’ everyday practice. The survey was conducted in four languages (English, Spanish, Portuguese, and Bahasa Melayu); as such, four research instruments were originally produced and four sets of microdata collected. The microdata have been translated into English, and only the English instrument and the aggregated, translated instructor- response microdata is included here. The student-response microdata is not part of this dataset. The dataset is considered to be of interest to OER scholars, practitioners, and policy-makers, as it seeks to provide a useful cross-regional comparison of various aspects of OER adoption.

Geographic coverage

The survey was conducted in nine countries in South America, Sub-Saharan Africa, and South and Southeast Asia.Countries covered were Brazil, Chile, Colombia, Ghana, Kenya, South Africa, India, Indonesia, Malaysia.

Analysis unit

Individuals

Universe

The study engaged instructors in higher education institutions in the nine countries involved in the study.

Kind of data

Qualitative data

Mode of data collection

Face-to-face [f2f]

Response rate

The survey gathered 295 usable responses from instructors.

South Africa is experiencing a rapidly growing diabetes epidemic that threatens its healthcare system. Research on the determinants of diabetes in South Africa receives considerable attention due to the lifestyle changes accompanying South Africa’s rapid urbanization since the fall of Apartheid. However, few studies have investigated how segments of the Black South African population, who continue to endure Apartheid’s institutional discriminatory legacy, experience this transition. This paper explores the association between individual and area-level socioeconomic status and diabetes prevalence, awareness, treatment, and control within a sample of Black South Africans aged 45 years or older in three municipalities in KwaZulu-Natal. Cross-sectional data were collected on 3,685 participants from February 2017 to February 2018. Individual-level socioeconomic status was assessed with employment status and educational attainment. Area-level deprivation was measured using the most recent South African Multidimensional Poverty Index scores. Covariates included age, sex, BMI, and hypertension diagnosis. The prevalence of diabetes was 23% (n = 830). Of those, 769 were aware of their diagnosis, 629 were receiving treatment, and 404 had their diabetes controlled. Compared to those with no formal education, Black South Africans with some high school education had increased diabetes prevalence, and those who had completed high school had lower prevalence of treatment receipt. Employment status was negatively associated with diabetes prevalence. Black South Africans living in more deprived wards had lower diabetes prevalence, and those residing in wards that became more deprived from 2001 to 2011 had a higher prevalence diabetes, as well as diabetic control. Results from this study can assist policymakers and practitioners in identifying modifiable risk factors for diabetes among Black South Africans to intervene on. Potential community-based interventions include those focused on patient empowerment and linkages to care. Such interventions should act in concert with policy changes, such as expanding the existing sugar-sweetened beverage tax.

School enrollment, tertiary (% gross) in South Africa was reported at 27.17 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - School enrollment, tertiary (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2019 series only includes data for quarter 3 and quarter 4.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2017 series only includes data for quarter 1.

Abstract

The Survey of Activities of Young People was conducted by Statistics South Africa and commissioned by the Department of Labour, primarily to gather information necessary for formulating an effective programme of action to address the issue of harmful work done by children in South Africa. Technical assistance for the survey was provided by the International Labour Organisation (ILO) and a consultant appointed by the Department of Labour. Stats SA also worked with an advisory committee, consisting of representatives from national government departments most directly concerned with child labour (the Departments of Labour,Welfare,Education and Health), non-governmental organisations, and the United Nations Children's Fund (Unicef).

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The sample frame was based on the 1996 Population Census Enumerator Areas (EA) and the number of households counted in 1996 Population Census. The sampled population excluded all prisoners in prison, patients in hospitals, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households. Coverage rules for the survey were that all children of usual residents were to be included even if they were not present. This means that most boarding school pupils were included in their parents’ household. The 16 EA types from the 1996 Population Census were condensed into four area types. The four area types were Formal Urban, Informal Urban, Tribal, and Commercial Farms. A decision was made to drop the Institution type EAs.

The EAs were stratified by province, and within a province by the four area types defined above. The sample size (6110 households) was disproportionately allocated to strata by using the square root method. Within the strata the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). PSUs consisted of ONE or more EAs of size 100 households to ensure sufficient numbers for screening. Statistics SA was advised by child labour experts that there was a likelihood of high rates of child labour in the Urban Informal and Rural Farm areas. The sample allocation to Rural Commercial Farms was therefore increased to a minimum of 20 PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2016 series only includes data for quarter 1, quarter 2 and quarter 3.

Abstract

The Survey of Activities of Young People (SAYP) is a household-based survey that collects data on the activities of young people aged 7-17 years who live in South Africa. The survey covers involvement of children in market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. Statistics South Africa collects SAYP information as part of the module of the Quarterly Labour Force Survey (QLFS) every four years. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7-17 years.

The aim of the first survey (SAYP 1999) was to collect information on children’s economic activities, including paid and unpaid work. All subsequent survey's (SAYP 2010, 2015 and 2019) are intended to provide updated information on the economic activities of children, including an analysis of child labour in South Africa. The specific objectives of the SAYP are to understand the extent of children’s involvement in economic activities, provide information for the formulation of an informed policy to combat child labour within the country and to monitor the South African Child Programme of Action (CLPA) and Sustainable Development Goal (SDG'S).

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The SAYP covers children aged 7-17 years resident in a household. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey of Activities of Young People (SAYP) comprised two stages. The first stage involved identifying households with children aged 7-17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2019 (Q3:2019). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SAYP collected data in two phases using one questionnaire.

The first phase questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting, and heating, water source for domestic use, land ownership, tenure, and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey.

The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

Despite the many useful studies on the use of Open Educational Resources (OER) in higher education, most are focused on the activity of students and instructors in the Global North who enjoy comparatively higher levels of economic development, educational provision, policy elaboration, and technological access than those in the Global South – the region where OER is touted as having its potentially greatest impact.This dataset arises from a study focusing on higher education instructors and students in South America, Sub-Saharan Africa, and South and Southeast Asia. Based on a cross-regional survey of 295 instructors at 28 universities in nine countries, this research seeks to establish a baseline of empirical data for assessing OER awareness and use in the Global South.The overarching research questions that this study set out to answer are:1. What proportion of instructors in the Global South have ever used OER?2. Which variables may account for different OER usage rates between respondents in the Global South?In order to address these questions, survey responses were correlated against the question (26) of the survey which directly addresses OER usage: “Have you ever used OER that are available in the public domain or has an open license (e.g. Creative Commons) that allows it to be used and/or adapted by others?”A core purpose of the overarching ROER4D project is the development of an empirical baseline of OER and Open Educational Practice (OEP) activity in the Global South. OER itself is a novel concept, and is tied to a broader spectrum of OEP that overlap with, but do not always exactly coincide with, formal OER practice. As such, an investigation into the use, reuse, adaptation, and sharing practices performed by higher education instructors, and the digital infrastructure and foundational literacies that underpin these practices (regardless of their knowledge of formal OER activity) is integral in ascertaining baseline practice.This dataset includes responses by instructors who engage in reuse and sharing activities, irrespective of whether they have consciously used OER in their practice. As such, it offers insights into the practices that exist outside of formally-labelled OER production. Dimension2 of the survey instrument “Educational Resources” is framed around general practice relating to sharing, use, reuse, creation, and licensing of educational materials, rather than OER per se. Data arising from these responses are to be treated with caution in terms of making inferences around OER, but remain useful in terms of gaining a more informed sense of instructors’ everyday practice.The survey was conducted in four languages (English, Spanish, Portuguese, and Bahasa Melayu); as such, four research instruments were originally produced and four sets of microdata collected. The microdata have been translated into English, and only the English instrument and the aggregated, translated instructor-response microdata is included here. The student-response microdata is not part of this dataset.The dataset is considered to be of potential interest to OER scholars, practitioners, and policy-makers, as it seeks to provide a useful cross-regional comparison of various aspects of OER adoption.This dataset was first published by DataFirst.