South Africa CCI: Higher Middle Income (HM) data was reported at 21.000 % in Jun 2018. This records a decrease from the previous number of 27.000 % for Mar 2018. South Africa CCI: Higher Middle Income (HM) data is updated quarterly, averaging 3.133 % from Mar 1995 (Median) to Jun 2018, with 94 observations. The data reached an all-time high of 27.000 % in Mar 2018 and a record low of -28.000 % in Dec 1998. South Africa CCI: Higher Middle Income (HM) data remains active status in CEIC and is reported by Bureau for Economic Research. The data is categorized under Global Database’s South Africa – Table ZA.H006: Consumer Survey.

By 2030, the middle-class population in Asia-Pacific is expected to increase from 1.38 billion people in 2015 to 3.49 billion people. In comparison, the middle-class population of sub-Saharan Africa is expected to increase from 114 million in 2015 to 212 million in 2030.

Worldwide wealth

While the middle-class has been on the rise, there is still a huge disparity in global wealth and income. The United States had the highest number of individuals belonging to the top one percent of wealth holders, and the value of global wealth is only expected to increase over the coming years. Around 57 percent of the world’s population had assets valued at less than 10,000 U.S. dollars; while less than one percent had assets of more than million U.S. dollars. Asia had the highest percentage of investable assets in the world in 2018, whereas Oceania had the highest percent of non-investable assets.

The middle-class

The middle class is the group of people whose income falls in the middle of the scale. China accounted for over half of the global population for middle-class wealth in 2017. In the United States, the debate about the middle class “disappearing” has been a popular topic due to the increase in wealth to the top billionaires in the nation. Due to this, there have been arguments to increase taxes on the rich to help support the middle-class.

Seychelles recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

South Africa ZA: Income Share Held by Third 20% data was reported at 8.200 % in 2014. This records an increase from the previous number of 8.000 % for 2010. South Africa ZA: Income Share Held by Third 20% data is updated yearly, averaging 8.200 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 9.900 % in 2000 and a record low of 7.500 % in 2005. South Africa ZA: Income Share Held by Third 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The average for 2023 based on 49 countries was 9.09 percent. The highest value was in South Africa: 27.99 percent and the lowest value was in Thailand: 0.91 percent. The indicator is available from 1991 to 2023. Below is a chart for all countries where data are available.

In 2022, South African households had an average disposable income of over ****** South African rand (approximately ***** U.S. dollars). This was slightly higher than the previous year where the average disposable income was ****** South African rand (around ***** U.S. dollars). Within the observed period, the disposable income of households in the country was highest in 2018 at ****** South African rand (about ***** U.S. dollars), while it was lowest in 2004.

South Africa ZA: Income Share Held by Highest 20% data was reported at 68.200 % in 2014. This records a decrease from the previous number of 68.900 % for 2010. South Africa ZA: Income Share Held by Highest 20% data is updated yearly, averaging 68.200 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 71.000 % in 2005 and a record low of 62.700 % in 2000. South Africa ZA: Income Share Held by Highest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa ZA: Income Share Held by Lowest 10% data was reported at 0.900 % in 2014. This stayed constant from the previous number of 0.900 % for 2010. South Africa ZA: Income Share Held by Lowest 10% data is updated yearly, averaging 1.000 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 1.300 % in 2000 and a record low of 0.900 % in 2014. South Africa ZA: Income Share Held by Lowest 10% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The average for 2022 based on 5 countries was 29523 kilograms. The highest value was in South Africa: 124401 kilograms and the lowest value was in Serbia: 15 kilograms. The indicator is available from 1998 to 2022. Below is a chart for all countries where data are available.

South Africa ZA: Income Share Held by Lowest 20% data was reported at 2.400 % in 2014. This records a decrease from the previous number of 2.500 % for 2010. South Africa ZA: Income Share Held by Lowest 20% data is updated yearly, averaging 2.600 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 3.100 % in 2000 and a record low of 2.400 % in 2014. South Africa ZA: Income Share Held by Lowest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

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Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

Apnea of prematurity (AOP) is a common complication among preterm infants (<37 weeks gestation), globally. However, access to caffeine citrate (CC) that is a proven safe and effective treatment in high income countries is largely unavailable in low-and-middle income countries, where most preterm infants are born. Therefore, the overall aim of this study was to describe the demand, policies, and supply factors affecting the availability and clinical use of CC in LMICs. A mixed methods approach was used to collect data from diverse settings in LMICs including Ethiopia, Kenya, Nigeria, South Africa, and India. Qualitative semi-structured interviews and focus group discussions were conducted with different health care providers, policymakers, and stakeholders from industry. Additional data was collected using standard questionnaires. A thematic framework approach was used to analyze the qualitative data and descriptive statistics were used to summarize the quantitative data. The findings indicate that there is variation in in-country policies on the use of CC in the prevention and treatment of AOP and its availability across the LMICs. As a result, the knowledge and experience of using CC also varied with clinicians on Ethiopia having no experience of using it while those in India have greater knowledge and experience of using it. The in turn influenced the demand and our findings show that only 29% of eligible preterm infants are receiving CC in these countries. There is an urgent need to address the multilevel barriers to accessing CC for management of AOP in Africa. These include cost, lack of national policies and therefore lack of demand stemming from its clinical equivalency with aminophylline. Practical ways to reduce the cost of CC in LMICs could potentially increase its availability and use. Methods Study design, setting, population, sampling We conducted a landscape evaluation involving stakeholders in Africa (Ethiopia, Kenya, Nigeria, South Africa) and South Asia (India – five states of Delhi; Bihar, Uttar Pradesh, Telangana and Madhya Pradesh) on CC availability and use from 1 July 2022 to 31 December 2022. We used a mixed methods study design to understand the complexity of CC availability and use across these LMICs. We selected a geographically and culturally diverse countries with high annual preterm births (~200,000). The selection of stakeholders within each focus country was by convenience and/or purposive sampling. We selected health facilities providing care for preterm infants and were able to provide the data required to achieve the study’s objectives. Proximity and ease of data collection was also factored into selection by research teams. Data collection Qualitative The research teams conducted key informant interviews and focus group discussions (FGD’s) with stakeholders in newborn health. The interviews with healthcare providers sought to explore their experience of using CC as a treatment for AOP. Interviews with WHO and Ministry of Health officials sought to understand current global and national health policies and CC’s inclusion in the essential drug list for using CC to treat AOP. Interviews with major drug suppliers and distributors of CC aimed to determine the current local market pricing of CC and its alternatives within and between countries. Also, to evaluate the factors determining the end-customer price of CC. The available average end-customer price per country was used to determine the daily cost of managing AOP for aminophylline and CC. We compared the average daily cost between aminophylline and cc for both public and private hospitals in each country. The dosing regimen for CC was a loading dose of 20 mg/kg/dose and a daily maintenance dose of between 5 to 10 mg/kg/day. The dosing regimen for aminophylline was a loading dose of 6 mg/kg administered intravenously (IV), followed by a maintenance dose of 2.5 mg/kg/dose/IV administered every 8 hours. Interviews and FGD’s were done in person or virtually over video or audio teleconferencing based on the preferences of the participants. All interviews were conducted in English. teams were situated in each country of focus and had previous training and experience conducting qualitative interviews and FGDs and in qualitative data analysis. The interviews and FGDs were semi structured using guide with a set of open-ended questions, in a set order and allowing for in-depth insights into the subject area. These guides were pilot tested across the 3 countries prior to data collection. Quantitative Additional interviews were conducted using standard questionnaires and had been piloted and refined in these settings prior to being used for data collection.The research team surveyed 107 providers: 20 from Ethiopia, 18 from India, 23 from Kenya, 28 from Nigeria, and 18 from South Africa. Providers were from 45 private or public health facilities across the five study countries. Of these, 12 (27%) were primary or secondary public, 7 (16%) were primary or secondary private, 25 (56%) were tertiary public, and 1 (2%) tertiary private Demand forecast for caffeine citrate. A demand forecast was conducted to determine the amount of CC needed per country. Using data from demographic health survey data from each country, we estimated the proportion of infants who would be eligible for CC treatment. Given AOP risk can be as high as 80% in preterm infants with birthweight ≤1500g (very low birth weight (VLBW)), we estimated that all VLBW infants met eligibility criteria for treatment with CC. We limited this forecast to public facilities where limited government funding constrains drug availability. We applied country-specific policies and assumptions to determine the percentage of VLBW infants who received or had a missed opportunity for CC treatment. These assumptions included, availability of CC, VLBW infants born in secondary facilities will be transferred to a tertiary center capable of providing AOP treat; some transfers will be unsuccessful and even when successful, AOP treatment will be unavailable. Data management and analysis All interviews were transcribed verbatim by an experienced transcriber. Authors reviewed the interview transcripts for errors. A coding framework was generated, and an emergent thematic analysis approach was used to analyze the data, to identify patterns and themes. Descriptive statistics were used to summarize the quantitative data.

IntroductionInformation pertaining to multimorbidity is frequently informed by studies from high income countries and it is unclear how these findings relate to low and middle income countries, where the burden of infectious disease is high. South Africa has a quadruple burden of disease which includes a high HIV prevalence and a growing burden of non-communicable diseases. This study aimed to analyse the prevalence and patterns (disease classes or clusters) of multimorbidity in South Africa.MethodsA secondary analysis of individuals over the age of 15 years who participated in the Fifth South African National HIV Prevalence, Incidence, Behavior and Communication Survey, 2017 (SABSSM 2017) was done. Six disease conditions were identified in the analysis (cancer, diabetes, heart disease, hypertension/high blood pressure, tuberculosis, and HIV). Chi-square tests were used to test for the differences in disease prevalence by sex. Common disease patterns were identified using a latent class analysis.ResultsThe sample included 27,896 participants, of which 1,837 had comorbidity or multimorbidity. When taking population-weighting into account, multimorbidity was present in 5.9% (95% CI: 5.4–6.4) of the population The prevalence of multimorbidity tended to be higher among females and increased with age, reaching 21.9% in the oldest age group (70+). The analyses identified seven distinct disease classes in the population. The largest class was “Diabetes and Hypertension” (36.3%), followed by “HIV and Hypertension” (31.0%), and “Heart disease and Hypertension” (14.5%). The four smaller classes were: “HIV, Diabetes, and Heart disease” (6.9%), “TB and HIV” (6.3%), “Hypertension, TB, and Cancer” (2.8%), and “All diseases except HIV” (2.2%).ConclusionAs the South African population continues to age, the prevalence of multimorbidity is likely to increase which will further impact the health care system. The prevalence of multimorbidity in the population was relatively low but reached up to 20% in the oldest age groups. The largest disease cluster was the combination of diabetes and hypertension; followed by HIV and hypertension. The gains in improving adherence to antiretrovirals amongst treatment-experienced people living with HIV, should be expanded to include compliance with lifestyle/behavioral modifications to blood pressure and glucose control, as well as adherence to anti-hypertension and anti-diabetic medication. There is an urgent need to improve the early diagnosis and treatment of disease in the South African population.

South Africa ZA: GDP: PPP data was reported at 765,567.480 Intl $ mn in 2017. This records an increase from the previous number of 742,237.973 Intl $ mn for 2016. South Africa ZA: GDP: PPP data is updated yearly, averaging 416,448.158 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 765,567.480 Intl $ mn in 2017 and a record low of 235,395.319 Intl $ mn in 1990. South Africa ZA: GDP: PPP 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: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. 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. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

Acronym: WDIType: Time SeriesTopics: Agriculture and Food Security, Climate Change, Economic Growth, Education, Energy and Extractives, Environment and Natural Resources, Financial Sector, Development,GenderHealth Nutrition and Population,Macroeconomic Vulnerability and Debt,Poverty, Private Sector Development, Public Sector Management, Social Development, Social Protection and Labor, Trade, Economy Coverage: High Income IBRD IDA Low Income Lower Middle Income Upper Middle IncomeLanguages Supported: English Arabic Chinese French SpanishNumber of Economies: 217Geographical Coverage: World East Asia & Pacific American Samoa Australia Brunei Darussalam Cambodia China FijiFrench Polynesia Guam Hong Kong SAR, China Indonesia Japan KiribatiKorea, Dem. People's Rep. Korea, Rep. Lao PDR Macao SAR, China Malaysia Marshall IslandsMongolia Myanmar Nauru New Caledonia New Zealand Northern Mariana Islands PalauPapua New Guinea Philippines Samoa Singapore Solomon Islands Thailand Timor-LesteTonga Tuvalu Vanuatu Vietnam Europe & Central Asia Albania Andorra Armenia AustriaAzerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech RepublicDenmark Estonia Faroe Islands Finland France Georgia Germany Gibraltar Greece GreenlandHungary Iceland Ireland Isle of Man Italy Kazakhstan Kyrgyz Republic Latvia LiechtensteinLithuania Luxembourg Macedonia, FYR Moldova Monaco Montenegro Netherlands NorwayPoland Portugal Romania Russian Federation San Marino Serbia Slovak Republic SloveniaSpain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United KingdomUzbekistan Latin America & Caribbean Antigua and Barbuda Aruba Argentina Bahamas, TheBarbados Belize Bolivia Brazil Cayman Islands Chile Costa Rica Colombia Cuba CuraçaoDominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana HaitiHonduras Jamaica Mexico Nicaragua Panama Paraguay Peru Puerto RicoSint Maarten (Dutch part) St. Kitts and Nevis St. Martin (French part) St. LuciaSt. Vincent and the Grenadines Suriname Trinidad and Tobago Turks and Caicos IslandsUruguay Venezuela, RB Virgin Islands (U.S.) Middle East & North Africa Algeria BahrainEgypt, Arab Rep. Djibouti Iraq Iran, Islamic Rep. Israel Jordan Kuwait Lebanon Libya MaltaMorocco Oman Qatar Saudi Arabia Syrian Arab Republic United Arab Emirates TunisiaYemen, Rep. Bermuda Canada United States South Asia Afghanistan Bangladesh BhutanIndia Pakistan Nepal Maldives Sri Lanka Angola Benin Botswana Burkina Faso BurundiCabo Verde Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep.Côte d'Ivoire Ethiopia Eritrea Equatorial Guinea Gabon Gambia, The Ghana GuineaGuinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania MauritiusMozambique Namibia Niger Nigeria Rwanda São Tomé and Principe Seychelles SenegalSierra Leone Somalia South Africa South Sudan Sudan Swaziland Tanzania Togo UgandaZambia Zimbabwe

South Africa ZA: GNI: PPP data was reported at 742,598.771 Intl $ mn in 2017. This records an increase from the previous number of 721,683.700 Intl $ mn for 2016. South Africa ZA: GNI: PPP data is updated yearly, averaging 407,099.911 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 742,598.771 Intl $ mn in 2017 and a record low of 226,701.382 Intl $ mn in 1990. South Africa ZA: GNI: PPP 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: Purchasing Power Parity. PPP GNI (formerly PPP GNP) is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. Gross national income 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. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

South Africa ZA: Income Share Held by Second 20% data was reported at 4.800 % in 2014. This records an increase from the previous number of 4.700 % for 2010. South Africa ZA: Income Share Held by Second 20% data is updated yearly, averaging 4.900 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 5.600 % in 2000 and a record low of 4.700 % in 2010. South Africa ZA: Income Share Held by Second 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa ZA: Income Share Held by Highest 10% data was reported at 50.500 % in 2014. This records a decrease from the previous number of 51.300 % for 2010. South Africa ZA: Income Share Held by Highest 10% data is updated yearly, averaging 50.500 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 54.200 % in 2005 and a record low of 44.900 % in 2000. South Africa ZA: Income Share Held by Highest 10% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa ZA: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data was reported at 27.993 % in 2016. This records a decrease from the previous number of 28.241 % for 2015. South Africa ZA: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data is updated yearly, averaging 7.298 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 29.718 % in 2014 and a record low of 2.649 % in 1978. South Africa ZA: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region 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: Exports. Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;