As of 2023, the GDP of Africa was estimated at roughly 3.1 trillion U.S. dollars. This was the highest value since 2010 when the continent's GDP amounted to approximately 2.1 trillion U.S. dollars. The GDP value in Africa generally followed an upward trend in recent years and was estimated to exceed 4.2 trillion U.S. dollars by 2027.

Leading the charge: the three leading African economies

Among the African countries, in 2021, Nigeria had the highest GDP with approximately 442 billion U.S. dollars. South Africa and Egypt followed. These three countries have the largest economies for various reasons. The most notable factors are their population size, natural resources, and level of economic development. Furthermore, Africa was projected to have a real GDP growth rate of 3.9 percent in 2023. Libya was the economy experiencing the highest growth rate in that year.

The Sub-Saharan African economy on the rise

A global comparison showed that Sub-Saharan Africa had the smallest GDP among all world regions in 2021, amounting to 1.87 trillion U.S. dollars. A closer look revealed that Sub-Saharan Africa had a GDP per capita of 1,626.3 U.S. dollars in 2021, again the lowest worldwide. However, the region's economy was forecast to experience continued growth in the following years, with the real GDP increasing by 3.7 percent in 2023.

The Gross Domestic Product (GDP) in South Africa expanded 0.50 percent in the third quarter of 2025 over the previous quarter. This dataset provides - South Africa GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of 2022, the real GDP growth rate in Africa was estimated at 3.7 percent, decreasing compared to the previous year when it stood at 4.8 percent. Africa's real GDP is projected to keep a stable and constant growth trend between 2023 and 2027.

Negative impact of COVID-19

Starting in 2020, the spread of the coronavirus (COVID-19) caused economic stagnation and recession in most world regions. Economies on the African continent were also negatively affected, as the health crisis determined disruptions across all economic sectors. In 2020, Africa’s real GDP dropped to minus 1.8 percent, an exceptional negative growth rate registered on the continent. Southern Africa was the most affected region, followed by Central and Western Africa, respectively.

Forecast economic growth in Africa

In 2021 and 2022, Africa’s economy showed signs of recovery after the COVID-19 crisis. Growth was expected to continue in the following years, with the total GDP increasing from around three trillion U.S. dollars in 2020 to four trillion U.S. dollars in 2027. The African economy was set to grow at a rapid pace, especially compared to other world regions. By 2027, Sub-Saharan Africa’s GDP is estimated to record a growth rate of over four percent, while the European Union’s economy would expand by less than two percent.

South Africa ZA: GDP: Growth data was reported at 1.320 % in 2017. This records an increase from the previous number of 0.565 % for 2016. South Africa ZA: GDP: Growth data is updated yearly, averaging 3.116 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 7.940 % in 1964 and a record low of -2.137 % in 1992. South Africa ZA: GDP: Growth 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: Gross Domestic Product: Annual Growth Rate. Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average;

South Africa ZA: GDP: Growth: GNI per Capita data was reported at -0.208 % in 2017. This records an increase from the previous number of -1.035 % for 2016. South Africa ZA: GDP: Growth: GNI per Capita data is updated yearly, averaging 1.184 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 4.951 % in 1964 and a record low of -4.301 % in 1983. South Africa ZA: GDP: Growth: GNI per Capita 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: Gross Domestic Product: Annual Growth Rate. Annual percentage growth rate of GNI per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GNI per capita is gross national income divided by midyear population. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average;

The Gross Domestic Product (GDP) in South Africa was worth 400.26 billion US dollars in 2024, according to official data from the World Bank. The GDP value of South Africa represents 0.38 percent of the world economy. This dataset provides the latest reported value for - South Africa GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, the South African mining sector's added value to the Gross Domestic Product (GDP) experienced an increase of *** percent from the previous year. The downward trend was already registered in 2018, when the sector's contribution to the GDP decreased by *** percent. However, there was a significant increase of **** percent recorded in 2021 due to post-pandemic (COVID-19) recovery efforts.

South Africa ZA: GDP: Growth: Gross Capital Formation data was reported at 1.620 % in 2017. This records an increase from the previous number of -8.058 % for 2016. South Africa ZA: GDP: Growth: Gross Capital Formation data is updated yearly, averaging 3.048 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 32.217 % in 1980 and a record low of -20.726 % in 1982. South Africa ZA: GDP: Growth: Gross Capital Formation 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: Gross Domestic Product: Annual Growth Rate. Annual growth rate of gross capital formation based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and 'work in progress.' According to the 1993 SNA, net acquisitions of valuables are also considered capital formation.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average;

In 2023, the value added by general government services to the Gross Domestic Product (GDP) in South Africa was *** percent. This represents an increased of *** percent from the previous year. Since 2018, the sector's contribution to GDP has been decreasing, although the growth rate has fluctuated annually.

South Africa ZA: GDP: Growth: Gross Value Added: Industry: Manufacturing data was reported at -0.150 % in 2017. This records a decrease from the previous number of 0.909 % for 2016. South Africa ZA: GDP: Growth: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 3.159 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 15.797 % in 1964 and a record low of -10.627 % in 2009. South Africa ZA: GDP: Growth: Gross Value Added: Industry: Manufacturing 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: Gross Domestic Product: Annual Growth Rate. Annual growth rate for manufacturing value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

As of 2024, final consumption expenditure in South Africa totaled approximately **** million South African rand and ******* U.S. dollars. At the same time, expenditure on GDP reached **** million South African rand and ******* U.S. dollars. Both figures represented the highest spending during the observed period. Economic growth is driven internally, as external financial flows such as personal remittances accounted for the smallest share of GDP in South Africa in 2023.

This data accompanies a report which analyzes media and internet market concentration in South Africa from 2017–2024 across 16 industries, combining qualitative and quantitative data. It finds persistent dominance in telecoms, broadcasting, and core internet services—such as Telkom in wireline, Vodacom and MTN in mobile, MultiChoice in pay-TV, and Media24 in print—while disruptive technologies, international platforms (e.g., WhatsApp, Starlink, Netflix), and rising internet access are gradually challenging these entrenched players.

South Africa ZA: GDP: Growth: Gross Value Added: Industry data was reported at 1.210 % in 2017. This records an increase from the previous number of -0.844 % for 2016. South Africa ZA: GDP: Growth: Gross Value Added: Industry data is updated yearly, averaging 1.869 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 10.111 % in 1964 and a record low of -5.995 % in 2009. South Africa ZA: GDP: Growth: Gross Value Added: Industry 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: Gross Domestic Product: Annual Growth Rate. Annual growth rate for industrial value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample excludes some prefectures because of security concerns: Bamingui-Bangoran, Basse-Kotto, Haute-Kotto, Haut-Mbomou, Mbomou, Nana-Grébizi,Ouham, Ouham-Pende,and Vakaga. The excluded areas represent about 40% of the estimated population.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

The paper focused on how energy consumption could promote inclusive growth and which type of energy consumption either renewable or non-renewable can lead to inclusive growth. The paper then proceeds to test empirically if the presence of foreign direct investment could help energy consumption to further enhance inclusive growth. The paper used 32 sub-Saharan African (SSA) countries over a 25-year time spanning 1995 to 2019. The is primarily based on the availability of data which was obtained from the World Bank (World Development Indicators), and the United States Energy Information Agency (EIA).

The dependent variable was proxied with an inclusive growth index where principal component analysis (PCA) was used in the generation of the index. The paper used 21 variables to create the index and these include access to clean fuels and technologies for cooking (% of the population); access to electricity (% of the population); mobile cellular subscriptions (per 100 people); contributing family workers, total (% of total employment); employment to population ratio, 15+, total (%) (modelled ILO); Immunization, DPT (% of children ages 12-23 months); mortality rate, under-5 (per 1,000 live births); nurses and midwives (per 1,000 people); physicians (per 1,000 people); the prevalence of underweight, weight for age (% of children under 5); primary education, duration (years), the proportion of seats held by women in national parliaments (%); pupil-teacher ratio, primary; school enrolment, primary (gross); gender parity index (GPI); school enrolment, secondary (gross), gender parity index; school enrolment, tertiary (gross) gender parity index; people using at least basic drinking water services (% of the population); people using at least basic sanitation services (% of the population); domestic general government health expenditure (% of general government expenditure); government expenditure on education, total (% of government expenditure); Gross Domestic Product per Capital (Constant, 2017, US$ PPP). All these variables were sourced from WDI.

To ensure that the index created is robust, the paper conducts various diagnostic tests such as determinant of the correlation matrix; Kaiser-Meyer-Olkin Measure of Sampling (KMO) and Bartlett test of sphericity.

The main variable of interest is energy consumption which was disaggregated into renewable energy consumption (% of total final energy consumption) and fossil fuel comprises coal, oil, petroleum, and natural gas products (non-renewable) sourced from EIA. The moderating variable is net Foreign Direct Investment Inflow (% GDP).

The control variables we sourced from WDI include gross fixed capital formation (% of GDP), labour force participation rate, total (% of total population ages 15-64) (modelled ILO estimate), trade (% of GDP) and GDP per person employed (constant 2017 PPP $).

South Africa ZA: GDP: Growth:(GDP) Gross Domestic Productper Capita data was reported at 0.067 % in 2017. This records an increase from the previous number of -0.735 % for 2016. South Africa ZA: GDP: Growth:(GDP) Gross Domestic Productper Capita data is updated yearly, averaging 1.219 % from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 5.084 % in 1964 and a record low of -4.430 % in 1992. South Africa ZA: GDP: Growth:(GDP) Gross Domestic Productper Capita 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: Gross Domestic Product: Annual Growth Rate. Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average;

Central African Republic CF: GDP: Growth: Gross Capital Formation data was reported at -1.529 % in 2023. This records an increase from the previous number of -4.535 % for 2022. Central African Republic CF: GDP: Growth: Gross Capital Formation data is updated yearly, averaging -3.032 % from Dec 2010 (Median) to 2023, with 14 observations. The data reached an all-time high of 92.260 % in 2014 and a record low of -27.963 % in 2017. Central African Republic CF: GDP: Growth: Gross Capital Formation data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Annual growth rate of gross capital formation based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and 'work in progress.' According to the 2008 SNA, net acquisitions of valuables are also considered capital formation.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;

Research ICT Africa (RIA) is a non-profit, public interest, research entity which undertakes research on how information and communication technologies are being accessed and used in African countries. The aim is to measure the impact on lifestyles and livelihoods of people and households and to understand how informal businesses can prosper through the use of ICTs. This research can facilitate informed policy-making for improved access, use and application of ICT for social development and economic growth. RIA collects both supply-side and demand-side data. On the demand-side nationally representative surveys are conducted on ICT use and demand in African countries. This survey dataset consists of data collected by household and business surveys conducted in 9 African countries in 2017 and 2018.

Context and Content

The dataset blends the University of Groningen's Penn World Table Productivity dataset, the Bank of Canada's Commodity Indices and the World Bank's GDP dataset. The blend is specifically created to answer the question: "What factors contribute most to, or are most indicative of, recessions in Africa?"

Content

The dataset has 49 feature variables and 1 target variable (i.e. the ‘growthbucket’ variable). There is a total of 486 samples in the dataset. 92.81% of the samples belong to the “0”, or “No_Recession” class. And, 7.82% of the samples belong to the “1” or “Recession” class. In short, the dataset has a class imbalance. It is useful for learning techniques for dealing with class imbalance like Cost-Sensitive Classification, Oversampling and Undersampling. For descriptions of the variables, see the attached file named: VariableDefinitions.csv

Years Covered: 2000 to 2017.

Countries Covered: 27 African Countries Including: Morocco, South Africa, Tanzania, Rwanda, Eswatini, Togo, Burkina Faso, Angola, Tunisia, Nigeria, Kenya, Burundi, Benin, Namibia, Central African Republic, Sudan, Gabon, Niger, Sierra Leone, Lesotho, Mauritania, Senegal, Mauritius, Botswana, Cameroon, Zimbabwe and Mozambique.

Acknowledgements

University of Groningen: Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt

Bank of Canada: Bank of Canada (2019). Commodity Price Index. [online] Bankofcanada.ca. Available at: https://www.bankofcanada.ca/rates/price-indexes/bcpi/ [Accessed 28 June 2019].

World Bank: World Bank (2019). GDP Growth (Annual %). [online] databank.worldbank.org. Available at: http://api.worldbank.org/v2/en/indicator/NY.GDP.MKTP.KD.ZG?downloadformat=excel [Accessed 27 June 2019].

Inspiration

A question: "What factors contribute most to, or are most indicative of, recessions in Africa?"

In 2023, the manufacturing sector contributed *** percent to the value added and gross domestic product (GDP) in South Africa. This represented a slight increase from the previous year, which recorded a negative growth of *** percent.