Government Spending in South Africa increased to 914941 ZAR Million in the third quarter of 2025 from 912423 ZAR Million in the second quarter of 2025. This dataset provides the latest reported value for - South Africa Government Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

South Africa ZA: Research and Development Expenditure: % of GDP data was reported at 0.723 % in 2013. This records a decrease from the previous number of 0.735 % for 2012. South Africa ZA: Research and Development Expenditure: % of GDP data is updated yearly, averaging 0.758 % from Dec 1997 (Median) to 2013, with 13 observations. The data reached an all-time high of 0.898 % in 2006 and a record low of 0.584 % in 1997. South Africa ZA: Research and Development Expenditure: % of GDP 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: Technology. Gloss domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development.; ; 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).

South Africa NT Forecast: National Gov Budget Balance: % of Nominal GDP data was reported at -3.300 % in 2020. This stayed constant from the previous number of -3.300 % for 2019. South Africa NT Forecast: National Gov Budget Balance: % of Nominal GDP data is updated yearly, averaging -3.400 % from Mar 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of -3.300 % in 2020 and a record low of -3.900 % in 2017. South Africa NT Forecast: National Gov Budget Balance: % of Nominal GDP data remains active status in CEIC and is reported by National Treasury. The data is categorized under Global Database’s South Africa – Table ZA.F002: National Government Revenue and Expenditure: Forecast: National Treasury.

BackgroundThe South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead.MethodsOur tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary.ResultsGiven the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely.ConclusionThe SACMC’s models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout.

South Africa NT Forecast: National Gov Budget Balance data was reported at -180,677,600,000.000 ZAR in 2020. This records a decrease from the previous number of -171,152,800,000.000 ZAR for 2019. South Africa NT Forecast: National Gov Budget Balance data is updated yearly, averaging -170,842,200,000.000 ZAR from Mar 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of -166,798,100,000.000 ZAR in 2018 and a record low of -180,677,600,000.000 ZAR in 2020. South Africa NT Forecast: National Gov Budget Balance data remains active status in CEIC and is reported by National Treasury. The data is categorized under Global Database’s South Africa – Table ZA.F002: National Government Revenue and Expenditure: Forecast: National Treasury.

Description: This data set contains data on the R&D expenditure by province, type of R&D, research fields, socioeconomic objectives, sources of funds and province. R&D personnel data is also included. The data is recorded for each of the following economic sectors of the South African economy: Business, Government, Higher Education, Not-for-Profit Organisations and Science Councils. Response rate = 100 x [Responses / (Responses + Non-responses - Out-of-scopes)], where non-responses include refusals and out-of-scopes include closed down, non-R&D performer, return to sender, untraceable, etc. Business sector response rate = 100 x [280/(280 + 187 - 32)] = 64.4% Not-for-profit sector response rate = 100 x [34/(34 + 20 - 4)] = 68.0% Government sector response rate = 100 x [42/(42 + 26 - 8)] = 70.0% Science Councils sector response rate = 100 x [11/(11 + 0 - 0)] = 100.0% Higher Education sector (Public) response rate = 100 x [20/(20 + 6 - 0)] = 76.9% Higher Education sector (Private) response rate = 100 x [5/(5+ 1 - 0)] =83.3% Overall survey response rate = 100 x [392/(392 + 240 - 44)] = 66.7% Abstract: The National Survey of Research and Experimental Development (R&D) Survey collects data under strict confidentiality. Aggregated data are used primarily to inform policy and strategic planning at a national level. The National R&D Survey 2021/22 collected primary data from five survey sectors: Business sector which consists of companies, business associations and state owned enterprises. Government sector includes national and provincial departments, research institutes and museums. Higher Education Institutions sector is made up of universities, universities of technology and private higher education institutions. Some limited supplementary data from HEMIS was used in this sector. Not-for-Profit Organisations (NPO) sector. Science Councils sector. Some general organisational information was collected. The survey however focused on human resources and financial data relating to in-house R&D conducted on the national territory of South Africa. In-house R&D personnel categories included: Researchers Technicians directly supporting R&D Other personnel directly supporting R&D Qualifications, gender and race Full-time-equivalents (FTE) on R&D In-house R&D expenditure categories included: Capital expenditure, labour costs and other current expenditure. Type of R&D (basic research, strategic basic research, applied research and experimental development) Provincial location Sources of funds Research fields (fields of science) Socio-economic objective Industry of R&D (Business sector only) The key users of the data and findings include government departments, especially DSI and the OECD which use survey indicators in their annual time-series publication on Main Science and Technology Indicators (MSTI). Ad hoc requests for data are also accommodated and inform academic papers, reports and other outputs. Email survey Face-to-face interview Telephone interview Web-based self-completion The universe of R&D performers was divided into the following five sectors as per the Survey Report 2001/2 dated September 2004: Business enterprises (BUS): The business sector of large, medium and small enterprises, including state-owned companies. Government (GOV): All government departments with an R&D component, government research institutes and museums. Higher education institutions (HEI): Higher education institutions, namely the 21 universities (and academic hospitals) and 15 Universities of Technology. Not-for-profit (NPO): Non-governmental and other organisations formally registered as not-for-profit institutions. Science councils (SCI): The 8 science councils, including the Africa Institute of South Africa, as established through their individual Acts of Parliament. Note: The SIC codes used in the 2018-19 survey have not changed and were applied to the 2019-20 survey. The sampling method of each sector is briefly outlined below: Business Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Government Sector: was surveyed using a census approach. All national and provincial government departments, research institutions and museums performing R&D were included. Higher Education Sector: institutions, namely universities and Universities of Technology were included through a census survey. Not-for-Profit Organisations (NPO) Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Science Councils Sector: was surveyed using a census approach.

South Africa NT Forecast: National Gov Expenditure: Debt Service Costs data was reported at 197,319,800,000.000 ZAR in 2020. This records an increase from the previous number of 180,651,500,000.000 ZAR for 2019. South Africa NT Forecast: National Gov Expenditure: Debt Service Costs data is updated yearly, averaging 171,502,300,000.000 ZAR from Mar 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of 197,319,800,000.000 ZAR in 2020 and a record low of 146,281,400,000.000 ZAR in 2017. South Africa NT Forecast: National Gov Expenditure: Debt Service Costs data remains active status in CEIC and is reported by National Treasury. The data is categorized under Global Database’s South Africa – Table ZA.F002: National Government Revenue and Expenditure: Forecast: National Treasury.

Description: This data set contains data on the R&D expenditure by province, type of R&D, research fields, socioeconomic objectives, sources of funds and province. R&D personnel data is also included. The data is recorded for each of the following economic sectors of the South African economy: Business, Government, Higher Education, Not-for-Profit Organisations and Science Councils. Abstract: The National Survey of Research and Experimental Development (R&D) Survey collects data under strict confidentiality. Aggregate data are used primarily to inform policy and strategic planning at a national level. The National R&D Survey 2016/17 collected primary data from five survey sectors: Business sector which consists of companies, business associations and state owned enterprises. Government sector includes national and provincial departments, research institutes and museums. Higher Education Institutions sector is made up of universities, universities of technology and private higher education institutions. Some limited supplementary data from HEMIS was used in this sector. Not-for-Profit Organisations (NPO) sector. Science Councils sector. Some general organisational information was collected. The survey however focused on Human Resources and Financial data relating to in-house R&D conducted on the national territory of South Africa. In-house R&D personnel categories included: Researchers Technicians directly supporting R&D Other personnel directly supporting R&D Qualifications, gender and race Full-time-equivalents (FTE) on R&D In-house R&D expenditure categories included: Capital expenditure, labour costs and other current expenditure. Type of R&D (basic research, strategic basic research, applied research and experimental development) Provincial location Sources of funds Research fields (fields of science) Socio-Economic Objective Industry of R&D (Business sector only) The key users of the data and findings include government departments, especially DST and the OECD who use survey indicators in their annual time-series publication on Main Science and Technology Indicators (MSTI). Ad hoc requests for data are also accommodated and inform academic papers, reports and other outputs. Email survey Face-to-face interview Postal survey Telephone interview The universe of R&D performers was divided into the following five sectors as per the Survey Report 2001/2 dated September 2004: Business enterprises (BUS): The business sector of large, medium and small enterprises, including state-owned companies. Government (GOV): All government departments with an R&D component, government research institutes and museums. Higher education institutions (HEI): Higher education institutions, namely the 21 universities (and academic hospitals) and 15 technikons. Not-for-profit (NPO): Non-governmental and other organisations formally registered as not-for-profit institutions. Science councils (SCI): the eight science research councils, plus the Africa Institute of South Africa, as established through their individual Acts of Parliament The sampling method of each sector is briefly outlined below: Business Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Government Sector: was surveyed using a census approach. All national and provincial government departments, research institutions and museums performing R&D were included. Higher Education Sector: institutions, namely universities and universities of technology were included through a census survey. Not-for-Profit Organisations (NPO) Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Science Councils Sector: were surveyed using a census approach. Response rate = 100 x [Responses / (Responses + Non-responses - Out-of-scopes)], where non-responses include refusals and out-of-scopes include closed down, non-R&D performer, return to sender, untraceable, etc. Business sector response rate = 100 x [308/(308 + 232 - 122)] = 73.7% Not-for-profit sector response rate = 100 x [40/(40 + 33 - 9)] = 62.7% Government sector response rate = 100 x [49/(49 + 55 - 4)] = 49.0% Science Councils sector response rate = 100 x [13/(13 + 0 - 0)] = 100.0% Higher Education sector (Public) response rate = 100 x [17/(17 + 7 - 0)] = 70.8% Higher Education sector (Private) response rate = 100 x [6/(6 + 3 - 0)] = 66.7% Overall survey response rate = 100 x [433/(433 + 330 - 135)] = 68.9%

The yield on South Africa 10Y Bond Yield eased to 8.50% on December 2, 2025, marking a 0.01 percentage points decrease from the previous session. Over the past month, the yield has fallen by 0.35 points and is 0.39 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. South Africa 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on December of 2025.

The total consumer spending on healthcare in Northern Africa was forecast to continuously increase between 2024 and 2029 by in total ** billion U.S. dollars (+***** percent). After the fourth consecutive increasing year, the healthcare-related spending is estimated to reach **** billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case healthcare-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on healthcare in countries like Southern Africa and Western Africa.

South Africa NT Forecast: National Gov Expenditure: % of Nominal GDP data was reported at 29.800 % in 2020. This records an increase from the previous number of 29.700 % for 2019. South Africa NT Forecast: National Gov Expenditure: % of Nominal GDP data is updated yearly, averaging 29.700 % from Mar 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of 29.800 % in 2020 and a record low of 29.600 % in 2017. South Africa NT Forecast: National Gov Expenditure: % of Nominal GDP data remains active status in CEIC and is reported by National Treasury. The data is categorized under Global Database’s South Africa – Table ZA.F002: National Government Revenue and Expenditure: Forecast: National Treasury.

Description: This data set contains data on the R&D expenditure by province, type of R&D, research fields, socioeconomic objectives, sources of funds and province. R&D personnel data is also included. The data is recorded for each of the following economic sectors of the South African economy: Business, Government, Higher Education, Not-for-Profit Organisations and Science Councils. Subsequent to the dissemination of version 1 of the 2013-14 data files, some of the information has been anonymised and the revised data files are disseminated as Version 2. Abstract: The National Survey of Research and Experimental Development (R&D) Survey collects data under strict confidentiality. Aggregate data are used primarily to inform policy and strategic planning at a national level. The National R&D Survey 2014/15 collected primary data from five survey sectors: Business sector, made up of companies, business associations and state owned enterprises. Government sector, made up of national and provincial departments, research institutes and museums. Higher Education Institutions sector, made up of universities, universities of technology, technikons and private higher education institutions. Some limited supplementary data from HEMIS was used in this sector. Not-for-Profit Organisations (NPO) sector. Science Councils sector. Some general organisational information was collected. The survey however focused on Human Resources and Financial data relating to in-house R&D conducted on the national territory of South Africa. In-house R&D personnel categories included: Researchers Technicians directly supporting R&D Other personnel directly supporting R&D Qualifications, gender and race Full-time-equivalents (FTE) on R&D In-house R&D expenditure categories included: Capital expenditure, labour costs and other current expenditure. Type of R&D (basic research, strategic basic research, applied research, and experimental development) Provincial location Sources of funds Research fields (fields of science) Socio-Economic Objective Industry of R&D (Business sector only) Email survey Face-to-face interview Postal survey Telephone interview The National R&D Survey 2014/15 collected primary data from five survey sectors: Business enterprises (BUS): The business sector of large, medium and small enterprises, including state-owned companies. Government (GOV): All government departments with an R&D component, government research institutes and museums. Higher education institutions (HEI): Higher education institutions, namely the 21 universities (and academic hospitals) and 15 technikons. Not-for-profit (NPO): Non-governmental and other organisations formally registered as not-for-profit institutions. Science councils (SCI): the eight science research councils, plus the Africa Institute of South Africa, as established through their individual Acts of Parliament. The sampling methods of the various sectors are briefly outlined below: Business Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Government Sector: was surveyed using a census approach. All national and provincial government departments, research institutions and museums performing R&D were included. Higher Education Sector: institutions, namely universities and universities of technology were included through a census survey. Not-for-Profit Organisations (NPO) Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Science Councils Sector: were surveyed using a census approach. Response rate = 100 x [Responses / (Responses + Non-responses – Out-of-scopes)], where non-responses include refusals and out-of-scopes include closed down, non-R&D performer, return to sender, untraceable, etc. Business sector response rate = 100 x [373/(373 + 60 - 48)] = 96.9% Not-for-profit sector response rate = 100 x [46/(46 + 34 - 11)] = 66.7% Government sector response rate = 100 x [70/(70 + 57 - 11)] = 60.3% Science Councils sector response rate = 100 x [13/(13 + 0 - 0)] = 100.0% Higher Education sector (Public) response rate = 100 x [19/(19 + 4 - 0)] = 82.6%

The real total consumer spending in Western Africa was forecast to continuously increase between 2024 and 2029 by in total **** billion U.S. dollars (+**** percent). According to this forecast, in 2029, the real total consumer spending will have increased for the fourth consecutive year to ***** billion U.S. dollars. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending in countries like Eastern Africa and Southern Africa.

Eswatini is facing multiple challenges. It was already experiencing weak economic growth before the COVID-19 pandemic, a reflection of longstanding, deeply rooted issues such as fiscal unsustainability, declining private investment, weakening productivity and competitiveness, and falling export diversification and complexity, compounded by the impact of climate shocks. It shifted from a private investment–led higher-growth model to a government spending–led lower-growth model after the end of apartheid in South Africa. With weak investment in productive sectors, Eswatini’s job market failed to keep pace with an expanding, younger labor force, leading to a large informal sector. Eswatini’s public sector–driven growth model is unsustainable under current fiscally constrained conditions, and there is a need to reduce and reprioritize public spending. An assessment of existing sectoral data and consultations with Eswatini’s private sector and policy makers suggest that four sectors can help drive the export-led private sector growth model. To return to an export-led growth model, Eswatini needs to increase export competitiveness by advancing regulatory reforms and improvements in trade logistics that include regional collaboration to address trade facilitation constraints. Finally, given the country’s vulnerability to climate risks, policies to foster economic resilience amid extreme weather events (mainly droughts that affect agriculture) and improve disaster preparedness need to be pursued. The private sector must adapt to this challenge and work with the government to improve climate resilience.

The total consumer spending in Northern Africa was forecast to continuously increase between 2024 and 2029 by in total *** billion U.S. dollars (+***** percent). After the fourth consecutive increasing year, the consumer spending is estimated to reach ***** billion U.S. dollars and therefore a new peak in 2029. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending in countries like Southern Africa and Eastern Africa.

The total consumer spending on communication in Eastern Africa was forecast to continuously increase between 2024 and 2029 by in total *** billion U.S. dollars (+***** percent). After the fourth consecutive increasing year, the communication-related spending is estimated to reach **** billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case communication-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres roughly to group **, with the exception of information processing equipment (computers) which are here still aggregated into recreation. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on communication in countries like Northern Africa and Southern Africa.

Data is sourced from various health resources. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. HIV Prevalence and TB Success RateHIV prevalence amongst women attending antenatal clinics in the Western Cape (2012-2015) by district and yearHIV prevalence amongst women attending antenatal clinics in the province (2012-2015) by province and yearTB Programme Success Rate (2013/14-2018/19) by TB Measure2. Births and Maternal MortalitiesNeonatal in facility (0-28 days) mortality rate (2015/16-2018/19); by years and neonatal death rate in facility and mortality rate by 1,000 live births Facility maternal mortality rate (2002, 2005, 2008, 2011, 2014); by triennia (3 years) deaths by 1,000 live births in WC (incl count of maternal deaths, count of live births, and infant maternal mortality ration)(Child (under 5) and Infant (under 1) mortality rate (2011, 2012, 2013); filter years, Infant/Child age band; Years, District, Births and Deaths by age bandDelivery rate in facility to women under 20 years (2013/14-2018/19); filter by financial year (FY); delivery rate by FY, delivery rate, numerator (births to women <20), denominator (total births)3. Deaths and Life ExpectancyLeading underlying causes of death in the Western Cape (2012-2016) by years and cause of deathYears of life lost (YLL) by cause of death in the WC (2012-2016) by years and YLL cause of deathAverage Life Expectency (LE) at birth (2006, 2011, 2016) by year, province, and gender4. Travel time to facilitiesTravel time taken to health facility by households with expenditure less than R1200-SA (2013-2018); by year, province, and travel time to health facilityTravel time taken to health facility by households with expenditure less than R1200-WC (2013-2018); by year, province, population group, and travel time to health facilityPublication Date2 September 2021LineageData from various sources transformed to a BI format and used to develop dynamic Power BI dashboards reflecting Outcome Indicators: HIV prevalence amongst women attending antenatal clinics in the provinceAll DS-TB (drug-susceptible tuberculosis) client treatment success rateNeonatal in facility (0-28 days) mortality rateFacility maternal mortality rateDelivery rate in facility to women under 20 yearsLife Expectancy (LE)Leading underlying causes of death in the Western CapeTravel time taken to health facility by households with expenditure less than R1200 (SA and WC)Data Source2019 National Antenatal Sentinel HIV Survey, National Department of Health 2021;Annual report 2014/15-2020/21, DOH;District Health Information Systems;Mid-year population estimates, Stats SA; Life Expectancy Stats SA calculations;Mortality and Causes of Death in South Africa 2018, June 2021, Stats SA

Description: This data set contains the aggregated data for 2013-14 on the R&D expenditure per province by Business, Government, Higher Education, Not-for-Profit Organisations and Science Councils. R&D expenditure is aggregated by five survey sectors across nine provinces in South Africa. Abstract: The National Research and Experimental Development Survey (R&D) collects data on the R&D expenditure by five survey sectors across nine provinces in South Africa. The 2013-14 survey collected primary data from five survey sectors: Business sector, made up of companies, business associations and state owned enterprises. Government sector, made up of national and provincial departments, research institutes and museums. Higher Education Institutions sector, made up of universities, universities of technology, technikons and private higher education institutions. Some limited supplementary data from HEMIS was used in this sector. Not-for-Profit Organisations (NPO) sector. Science Councils sector. Some general organisational information was collected. The survey however focused on Human Resources and Financial data relating to in-house R&D conducted on the national territory of South Africa. In-house R&D personnel categories included: Researchers Technicians directly supporting R&D Other personnel directly supporting R&D Qualifications, gender and race Full-time-equivalents (FTE) on R&D In-house R&D expenditure categories included: Capital expenditure, labour costs and other current expenditure. Type of R&D (basic research, strategic basic research, applied research, and experimental development) Provincial location Sources of funds Research fields (fields of science) Socio-Economic Objective Industry of R&D (Business sector only) Email survey Face-to-face interview Postal survey Telephone interview The National R&D Survey 2013/14 collected primary data from five survey sectors: Business enterprises (BUS): The business sector of large, medium and small enterprises, including state-owned companies. Government (GOV): All government departments with an R&D component, government research institutes and museums. Higher education institutions (HEI): Higher education institutions, namely the 21 universities (and academic hospitals) and 15 technikons. Not-for-profit (NPO): Non-governmental and other organisations formally registered as not-for-profit institutions. Science councils (SCI): the eight science research councils, plus the Africa Institute of South Africa, as established through their individual Acts of Parliament. The sampling methods of the various sectors are briefly outlined below: Business Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Government Sector: was surveyed using a census approach. All national and provincial government departments, research institutions and museums performing R&D were included. Higher Education Sector: institutions, namely universities and universities of technology were included through a census survey. Not-for-Profit Organisations (NPO) Sector: a purposive sampling procedure was employed whereby all known and likely R&D performers were targeted. Science Councils Sector: were surveyed using a census approach. Response rate = 100 x [Responses/(Responses + Non-responses – Out-of-scopes)], where non-responses include refusals and out-of-scopes include closed down, non-R&D performer, return to sender, untraceable, etc. Business sector response rate = 100 x [373/(373 + 60 - 48)] = 96.9% Not-for-profit sector response rate = 100 x [46/(46 + 34 - 11)] = 66.7% Government sector response rate = 100 x [70/(70 + 57 - 11)] = 60.3% Science Councils sector response rate = 100 x [13/(13 + 0 - 0)] = 100.0% Higher Education sector (Public) response rate = 100 x [19/(19 + 4 - 0)] = 82.6%

South Africa NT Forecast: National Gov Expenditure incl Contingency Reserve data was reported at 1,652,192,000,000.000 ZAR in 2020. This records an increase from the previous number of 1,522,183,300,000.000 ZAR for 2019. South Africa NT Forecast: National Gov Expenditure incl Contingency Reserve data is updated yearly, averaging 1,465,699,350,000.000 ZAR from Mar 2017 (Median) to 2020, with 4 observations. The data reached an all-time high of 1,652,192,000,000.000 ZAR in 2020 and a record low of 1,307,422,500,000.000 ZAR in 2017. South Africa NT Forecast: National Gov Expenditure incl Contingency Reserve data remains active status in CEIC and is reported by National Treasury. The data is categorized under Global Database’s South Africa – Table ZA.F002: National Government Revenue and Expenditure: Forecast: National Treasury.

The total consumer spending on transportation in Northern Africa was forecast to continuously increase between 2024 and 2029 by in total **** billion U.S. dollars (+***** percent). After the fourth consecutive increasing year, the transportation-related spending is estimated to reach **** billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case transportation-related spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group **. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on transportation in countries like Western Africa and Southern Africa.