As of 2024, around **** million people in South Africa are living in extreme poverty, with the poverty threshold at **** U.S. dollars daily. This means that ******* more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over **** million South Africans will live on a maximum of **** U.S. dollars per day. Who is considered poor domestically? Poverty is measured using several matrices. For example, local authorities tend to rely on the national poverty line, assessed based on consumer price indices (CPI) of a basket of goods of food and non-food components. In 2023, the domestic poverty line in South Africa stood at ***** South African rand per month (around ***** U.S. dollars per month). According to a survey, social inequality and poverty worried a significant share of the South African respondents. As of September 2024, some ** percent of the respondents reported that they were worried about the state of poverty and unequal income distribution in the country.   Eastern Cape residents received more grants South Africa’s labor market has struggled to absorb the country’s population. In 2023, almost a third of the economically active population was unemployed. Local authorities employ relief assistance and social grants in an attempt to reduce poverty and assist poor individuals. In 2023, almost ** percent of South African households received state support, with the majority share benefiting in the Eastern Cape.

As of 2024, an individual living in South Africa with less than 1,109 South African rand (roughly 62.14 U.S. dollars) per month was considered poor. Furthermore, individuals having 796 South African rand (approximately 44.60 U.S. dollars) a month available for food were living below the poverty line according to South African national standards. Absolute poverty National poverty lines are affected by changes in the patterns of household consumers and fluctuations in prices of services and goods. They are calculated based on the consumer price indices (CPI) of both food and non-food items separately. The national poverty line is not the only applicable threshold. For instance,13.2 million people in South Africa were living under 2.15 U.S. dollars, which is the international absolute poverty threshold defined by the World Bank. Most unequal in the globe A prominent aspect of South Africa’s poverty is related to extreme income inequality. The country has the highest income Gini index globally at 63 percent as of 2023. One of the crucial obstacles to combating poverty and inequality in the country is linked to job availability. In fact, youth unemployment was as high as 49.14 percent in 2023.

South Africa ZA: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 55.500 % in 2014. This records an increase from the previous number of 53.200 % for 2010. South Africa ZA: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 58.800 % from Dec 2005 (Median) to 2014, with 4 observations. The data reached an all-time high of 66.600 % in 2005 and a record low of 53.200 % in 2010. South Africa ZA: Poverty Headcount Ratio at National Poverty Lines: % of Population 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. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

In 2023, the international poverty (based on 2017 purchasing power parities (PPPs)) and the lower-income poverty rate (3.65 U.S. dollars in 2017 PPP), was highest for Mozambique within the Southern Africa region, with 74.7 percent and 88.7 percent, respectively. However, the upper middle-income poverty rate was highest for Zambia, at 93 percent.

Historical dataset showing South Africa poverty rate by year from 1993 to 2014.

South Africa ZA: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 37.600 % in 2014. This records an increase from the previous number of 35.800 % for 2010. South Africa ZA: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 47.800 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 53.900 % in 1996 and a record low of 35.800 % in 2010. South Africa ZA: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population 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. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa ZA: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data was reported at 18.900 % in 2014. This records an increase from the previous number of 16.500 % for 2010. South Africa ZA: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data is updated yearly, averaging 25.000 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 33.800 % in 1996 and a record low of 16.500 % in 2010. South Africa ZA: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population 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: Poverty. Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

South Africa ZA: Number of People Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP data was reported at 229,000.000 Person in 2010. This records a decrease from the previous number of 307,000.000 Person for 2005. South Africa ZA: Number of People Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP data is updated yearly, averaging 229,000.000 Person from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 307,000.000 Person in 2005 and a record low of 112,000.000 Person in 2000. South Africa ZA: Number of People Pushed Below the $1.90: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 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: Poverty. Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure; ; Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.; Sum;

In 2025, around ***** million people in Africa were living in extreme poverty, with the poverty threshold at **** U.S. dollars a day. The number of poor people on the continent dropped slightly compared to the previous year. Poverty in Africa is expected to decline slightly in the coming years, even in the face of a growing population. The number of inhabitants living below the extreme poverty line would decrease to around *** million by 2030.

15.1 (%) in 2014. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -1.230 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -1.230 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate 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. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Abstract

South Africa's first Living Conditions Survey (LCS) was conducted by Statistics South Africa over a period of one year between 13 October 2014 and 25 October 2015. The main aim of this survey is to provide data that will contribute to a better understanding of living conditions and poverty in South Africa for monitoring levels of poverty over time. Data was collected from 27 527 households across the country. The survey used a combination of the diary and recall methods. Households were asked to record their daily acquisitions in diaries provided by Statistics SA for a period of a month. The survey also employed a household questionnaire to collect data on household expenditure, subjective poverty, and income.

Geographic coverage

The survey had national coverage.

Analysis unit

Households and individuals

Universe

The sample for the survey included all domestic households, holiday homes and all households in workers' residences, such as mining hostels and dormitories for workers, but excludes institutions such as hospitals, prisons, old-age homes, student hostels, and dormitories for scholars, boarding houses, hotels, lodges and guesthouses.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Survey 2014-2015 sample was based on the LCS 2008-2009 master sample of 3 080 PSUs. However, there were 40 PSUs with no DU sample, thus the sample of 30 818 DUs was selected from only 3 040 PSUs. Amongst the PSUs with no DU sample, 25 PSUs were non-respondent because 19 PSUs were not captured on the dwelling frame, and 6 PSUs had an insufficient DU count. The remaining 15 PSUs were vacant and therefore out-of-scope. Among the PSUs with a DU sample, 2 974 PSUs were respondent, 50 PSUs were non-respondent and 16 PSUs were out-of-scope. The scope of the Master Sample (MS) is national coverage of all households in South Africa. It was designed to cover all households living in private dwelling units and workers living in workers' quarters in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Living Conditions Survey 2014-2015 used three data collection instruments, namely a household questionnaire, a weekly diary, and the summary questionnaire. The household questionnaire was a booklet of questions administered to respondents during the course of the survey month. The weekly diary was a booklet that was left with the responding household to track all acquisitions made by the household during the survey month. The household (after being trained by the Interviewer) was responsible for recording all their daily acquisitions, as well as information about where they purchased the item and the purpose of the item. A household completed a different diary for each of the four weeks of the survey month. Interviewers then assigned codes for the classification of individual consumption according to purpose (COICOP) to items recorded in the weekly diary, using a code list provided to them.

Data appraisal

Anthropometric data collected during the survey are not included in the dataset.

Introduction/objectivesMore than half of South Africa’s population lives in poverty, with significant health disparities across different regions. This study investigates the effects of regional poverty and historical economic factors on the efficacy of public health expenditure to understand how socioeconomic contexts influence overall public health outcomes.MethodsOur study utilized annual data from 2005 to 2019 for 9 provinces, drawing from the General Household Survey, Health Systems Trust database, and National Treasury’s Intergovernmental Fiscal Review. The primary health outcome was life expectancy at birth, while public health expenditure per capita was the main independent variable. We developed the Provincial Index of Multiple Deprivation to assess poverty, incorporating dimensions such as health, education, and living standards. We employed a two-way fixed effects model to examine the complex relationships between regional poverty, public health spending, and health outcomes.ResultsThe study found that poverty levels moderate the impact of public health spending on health outcomes, as evidenced by varying results across different provincial regions. Health outcomes in poorer provinces were less influenced by public health spending than wealthier regions. Additionally, the study established that income per capita, along with its lagged values and the lagged values of public health expenditure per capita, did not significantly affect health outcomes as measured by life expectancy.Conclusion/recommendationsThe impact of health expenditure in South Africa is influenced by regional poverty levels. To maximize the effectiveness of health spending, equitable, region-specific interventions tailored to address the unique health challenges of each area should be implemented.

South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.550 % in 2014. South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.550 % from Dec 2014 (Median) to 2014, with 1 observations. South Africa ZA: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate 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: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

Abstract

The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Universe

All Household members.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

BackgroundOf the total 56 million deaths worldwide during 2012, 38 million (68%) were due to noncommunicable diseases (NCDs), particularly cardiovascular diseases (17.5 million deaths) cancers (8.2 million) which represents46.2% and 21.7% of NCD deaths, respectively). Nearly 80 percent of the global CVD deaths occur in low- and middle-income countries. Some of the major CVDs such as ischemic heart disease (IHD) and stroke and CVD risk conditions, namely, hypertension and dyslipidaemia share common modifiable risk factors including smoking, unhealthy diets, harmful use of alcohol and physical inactivity. The CVDs are now putting a heavy strain of the health systems at both national and local levels, which have previously largely focused on infectious diseases and appalling maternal and child health. We set out to estimate district-level co-occurrence of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia in South Africa.MethodThe analyses were based on adults health collected as part of the 2012 South African National Health and Nutrition Examination Survey (SANHANES). We used joint disease mapping models to estimate and map the spatial distributions of risks of hypertension, self-report of ischaemic heart disease (IHD), stroke and dyslipidaemia at the district level in South Africa. The analyses were adjusted for known individual social demographic and lifestyle factors, household and district level poverty measurements using binary spatial models.ResultsThe estimated prevalence of IHD, stroke, hypertension and dyslipidaemia revealed high inequality at the district level (median value (range): 5.4 (0–17.8%); 1.7 (0–18.2%); 32.0 (12.5–48.2%) and 52.2 (0–71.7%), respectively). The adjusted risks of stroke, hypertension and IHD were mostly high in districts in the South-Eastern parts of the country, while that of dyslipidaemia, was high in Central and top North-Eastern corridor of the country.ConclusionsThe study has confirmed common modifiable risk factors of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia. Accordingly, an integrated intervention approach addressing cardiovascular diseases and associated risk factors and conditions would be more cost effective and provide stronger impacts than individual tailored interventions only. Findings of excess district-level variations in the CVDs and their risk factor profiles might be useful for developing effective public health policies and interventions aimed at reducing behavioural risk factors including harmful use of alcohol, physical inactivity and high salt intake.

In 2025, over **** million people in Mozambique lived in extreme poverty (with less than **** U.S. dollars a day), the highest number within Southern Africa. The country also scored the highest share of its overall population living below the poverty line in the region. On the other hand, Botswana had the lowest number of just over ******* people living in impoverished conditions, accounting for ** percent of the overall population.

South Africa ZA: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: USD data was reported at 0.003 USD in 2010. This records a decrease from the previous number of 0.005 USD for 2005. South Africa ZA: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: USD data is updated yearly, averaging 0.003 USD from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.005 USD in 2005 and a record low of 0.003 USD in 2000. South Africa ZA: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: USD 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. Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, expressed in US dollars (2011 PPP); ; Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.; Weighted average;