As of 2024, around 13.2 million people in South Africa are living in extreme poverty, with the poverty threshold at 2.15 U.S. dollars daily. This means that 139,563 more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over 13.4 million South Africans will live on a maximum of 2.15 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 1,109 South African rand per month (around 62.14 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 33 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 50 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 60 U.S. dollars) per month was considered poor. Furthermore, individuals who have roughly 796 South African rand (approximately 43 U.S. dollars) a month available for food were living below the poverty line, according to South African national standards.

Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 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.

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.

During the South African apartheid, Black people were forced to move to homelands during the 1960s and 1970s, resulting in one of history’s largest segregation policy experiments. We examine how and why relocation to the homelands affected human capital attainment. Exploiting the staggered timing of homeland establishment in a cross-cohort identification strategy, we find that moving to the homelands during childhood significantly reduces educational attainment, labor earnings and employment rates in adulthood. The data suggest an important role for place effects. Moving to the homelands in childhood implies greater exposure to poorer neighborhoods and it disproportionally reduces human capital attainment.

According to monthly surveys conducted in South Africa, September 2024 revealed that a 33 percent share of the respondents in the country were worried about poverty and social inequalities. During the period under review, the share of participants in South Africa concerned about social injustices and poverty fluctuated between 27 percent, observed in December 2022, and 38 percent, reaching a peak in August 2023.

South Africa poverty rate for 2014 was 61.60%, a 0.7% increase from 2010.

• South Africa poverty rate for 2010 was 60.90%, a 1.5% decline from 2008.
• South Africa poverty rate for 2008 was 62.40%, a 9.4% decline from 2005.
• South Africa poverty rate for 2005 was 71.80%, a 2.5% decline from 2000.

Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 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.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For countries with an active poverty monitoring program, the World Bank—in collaboration with national institutions, other development agencies, and civil society—regularly conducts analytical work to assess the extent and causes of poverty and inequality, examine the impact of growth and public policy, and review household survey data and measurement methods. Data here includes poverty and inequality measures generated from analytical reports, from national poverty monitoring programs, and from the World Bank’s Development Research Group which has been producing internationally comparable and global poverty estimates and lines since 1990.

South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 23.500 % in 2014. This stayed constant from the previous number of 23.500 % for 2010. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 23.500 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 25.500 % in 2000 and a record low of 20.300 % in 2005. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % 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: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. 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.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

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.

This dataset consists of the individual-level survey data (from the South African Social Attitudes Survey 2009) plus linked area-level data concerning exposure to socio-economic inequality and neighbourhood poverty rates. These data have been anonymised by rounding the area-level variables and replacing the area-level identifier codes with new unique identifiers to ensure respondent confidentiality.

This project is a collaboration between researchers from the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford and the South African Human Sciences Research Council (HSRC). The main aim of the project is to investigate whether citizens' attitudes to inequality in South Africa are associated with their experience of inequality at the local level. Analyses will be undertaken using two currently under-utilised micro-datasets in South Africa. Attitudinal perspectives on inequality will be explored using the nationally representative South African Social Attitudes Survey (SASAS). The unequal spatial distribution of poverty and deprivation will be explored using the South African Indices of Multiple Deprivation (SAIMD) which are derived from the 2001 Census and the 2007 Community Survey (CS 2007). The analysis of these microdata will be used to gain a better appreciation of the spatial patterns of poverty, deprivation and inequality, the nature and distribution of attitudes to economic inequality and redistribution, and importantly the relationship between them. The project will also build quantitative data analysis skills among social researchers in South Africa. The findings will be used to promote discussion about policies and programmatic interventions to address deprivation, poverty and inequality.

Abstract

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need. The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Geographic coverage

Langa in Cape Town, Diepsloot in Johannesburg and Lugangeni, a rural village in the Eastern Cape.

Analysis unit

Households and individuals

Universe

The survey covered households in the three geographic areas.

Kind of data

Sample survey data

Sampling procedure

To create the sampling frame for the Financial Diaries, the researchers echoed the method used in the Rutherford (2002) and Ruthven (2002), a participatory wealth ranking (PWR). Within South Africa, the participatory wealth ranking method is used by the Small Enterprise Foundation (SEF), a prominent NGO microlender based in the rural Limpopo Province. Simanowitz (1999) compared the PWR method to the Visual Indicator of Poverty (VIP) and found that the VIP test was seen to be at best 70% consistent with the PWR tests. At times one third of the list of households that were defined as the poorest by the VIP test was actually some of the richest according to the PWR. The PWR method was also implicitly assessed in van der Ruit, May and Roberts (2001) by comparing it to the Principle Components Analysis (PCA) used by CGAP as a means to assess client poverty. They found that three quarters of those defined as poor by the PCA were also defined as poor by the PWR. We closely followed the SEF manual to conduct our wealth rankings, and consulted with SEF on adapting the method to urban areas.

The first step is to consult with community leaders and ask how they would divide their community. Within each type of areas, representative neighbourhoods of about 100 households each were randomly chosen. Townships in South Africa are organised by street - with each street or zone having its own street committee. The street committees are meant to know everyone on their street and to serve as stewards of all activity within the street. Each street committee in each area was invited to a central meeting and asked to map their area and give a roster of household names. Following the mapping, each area was visited and the maps and rosters were checked by going door to door with the street committee.

Two references groups were then selected from the street committee and senior members of the community with between four and eight people in each reference group. Each reference group was first asked to indicate how they define a poor household versus those that are well off. This discussion had a dual purpose. First, it relayed information about what each community believes is rich or poor. Second, it started the reference group thinking about which households belong under which heading.

Following this discussion, each reference group then ranked each household in the neighbourhood according to their perceived wealth. The SEF methodology of wealth ranking is de-normalised in that reference groups are invited to put households into as many different wealth piles as they feel in appropriate. Only households that are known by both reference groups were kept in the sample.

The SEF guidelines were used to assign a score to each household in a particular pile. The scores were created by dividing 100 by the number of piles multiplied by the level of the pile. This means that if the poorest pile was number 1, then every household in the pile was assigned a score of 100, representing 100% poverty. If the wealthiest pile was pile number 6, then every household in that pile received a score of 16.7 and every household in pile 5 received a score of 33.3. An average score for both reference groups was taken for the distribution.

One way of assessing how good the results are is to analyse how consistent the rankings were between the two reference groups. According to the SEF methodology, a result is consistent if the scores between the two reference groups have no more than a 25 points difference. A result is inconsistent if the difference between the scores is between 26 and 50 points while a result is unreliable is the difference between the scores is above 50 points. SEF uses both consistent and inconsistent rankings, as long as they use the average across two reference groups - this would mean that 91% of the sample could be used. However, because only used two reference groups were used, only the consistent household for the final sample selection was considered.

To test this further,the number of times that the reference groups put a household in the exact same category was counted. The extent of agreement at either end of the wealth spectrum between the two reference groups was also assessed. This result would be unbiased by how many categories the reference groups put households into.

Following the example used in India and Bangladesh, the sample was divided into three different wealth categories depending on the household's overall score. Making a distinction between three different categories of wealth allowed the following of a similar ranking of wealth to Bangladesh and India, but also it kept the sample from being over-stratified. A sample of 60 households each was then drawn randomly from each area. To draw the sample based on a proportion representation of each wealth ranking within the population would likely leave the sample lacking in wealthier households of some rankings to draw conclusions. Therefore the researchers drew equally from each ranking.

Mode of data collection

Face-to-face [f2f]

The dataset consists of two exposure indices which were developed to reflect people's lived experience of inequality in South Africa. The two indices measure exposure to socio-economic inequality from the perspectives of the 'poor' and the 'non-poor', respectively. Both exposure indices are presented at 'Datazone' neighbourhood level and are based on data derived from the South African 2001 Census.

This project is a collaboration between researchers from the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford and the South African Human Sciences Research Council (HSRC). The main aim of the project is to investigate whether citizens' attitudes to inequality in South Africa are associated with their experience of inequality at the local level. Analyses will be undertaken using two currently under-utilised micro-datasets in South Africa. Attitudinal perspectives on inequality will be explored using the nationally representative South African Social Attitudes Survey (SASAS). The unequal spatial distribution of poverty and deprivation will be explored using the South African Indices of Multiple Deprivation (SAIMD) which are derived from the 2001 Census and the 2007 Community Survey (CS 2007). The analysis of these microdata will be used to gain a better appreciation of the spatial patterns of poverty, deprivation and inequality, the nature and distribution of attitudes to economic inequality and redistribution, and importantly the relationship between them. The project will also build quantitative data analysis skills among social researchers in South Africa. The findings will be used to promote discussion about policies and programmatic interventions to address deprivation, poverty and inequality.

South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 63.000 % in 2014. This records a decrease from the previous number of 63.400 % for 2010. South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 63.000 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 64.800 % in 2005 and a record low of 57.800 % in 2000. South Africa ZA: Gini Coefficient (GINI Index): World Bank Estimate 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. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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.

Abstract

The State of Giving project, established by the Centre for Civil Society (CCS) at the University of KwaZulu-Natal (UKZN), the Southern African Grantmakers’ Association (SAGA) and the National Development Agency (NDA), was initiated to generate information on and analyse the resource flows to poverty alleviation and development in South Africa. One component of the broader project was a focus on individual-level giving, which involved the design, implementation and analysis of a national sample survey on individual level giving behaviour. It thus speaks to both the urban and rural and the formal and informal dimensions of our social context. The survey collected data on who gives, why and how much they give, as well as what they give and the recipients of their giving.

Geographic coverage

The sample, a random stratified one comprising 3000 respondents, is representative of all South Africans aged 18 and above.

Analysis unit

Individuals

Universe

The population of interest in the survey was all South Africans aged 18 and above.

Kind of data

Sample survey data

Sampling procedure

A random stratified survey sample was drawn by Ross Jennings at S&T. The sample was stratified by race and province at the first level, and then by area (rural/urban/etc.) at the second level. The sample frame comprised 3000 respondents, yielding an error bar of 1.8%. The results are representative of all South Africans aged 18 and above, in all parts of the country, including formal and informal dwellings. Unlike many surveys, the project partners ensured that the rural component of the sample (commonly the most expensive for logistical reasons) was large and did not require heavy weighting (where a small number of respondents have to represent the views of a far larger community).

Randomness was built into the selection of starting points (from which fieldworkers begin their work) - every 5th dwelling was selected, after a randomly selected starting point had been identified - and into the selection of respondents, where the birthday rule was applied. That is, a household roster was completed, all those aged 18 and above were listed, and the householder whose birthday came next was identified as the respondent. Three call-backs were undertaken to interview the selected respondent; if s/he was unavailable, the household was substituted.

A second sample was drawn, specifically to boost the minority religious groups – namely Hindus, Jews and Muslims. They are separately analysed and reported as part of the broader project, since area sampling was used, disallowing us from incorporating them into the national survey dataset.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of focus groups were staged across the country in order to inform questionnaire design. Groups were recruited across a range of criteria, including demographic and religious differences, in order to ensure a wide range of views were canvassed. Direct input from focus group participants informed a series of robust design sessions with all the project partners, from which a draft questionnaire was designed. The questionnaire was piloted in two provinces, involving urban and rural respondents and covering all four race groups. The pilot included testing specific questions, and the overall methodological approach, namely our ability to quantify giving. After the pilot results had been assessed, the questionnaire was revised before going into field.

Sampling error estimates

1. "0" values in some variables Many of the variables have a "0" value in addition to the values for responses, e.g. variables with yes/no responses are coded "0" "1""2". There is no indication that the 0 represents "missing" (only Q75 specifies the use of "0" for none/nobody).

2. Variable Q9 (Question 9) Q8 lists the number of resident children under the age of 18. Q9 refers to this question with: "of these children aged below 16 living in your household". This should probably be "aged below 18", in line with Q8 The data only reflects children under 16, so the question should probably have been "of these children, how many below the age of 16 are (Q9A) children of the head of the household and (Q9B) children not born to the head of household, i.e. children born to others. It seems though, that Q8 and Q9 should match, with Q8 identifying children and Q9 identifying children of the household head. If specifying 16 rather than 18 in Q9 is an error, then this has been reflected in the data. This means that household members 17-18 years are listed, but the data does not record whether they are children of the household head.

3. Variable Q21 (Question 21) “What do you think is the most deserving cause that you support or would support if you could?” There are 14 values for Q21 (1-14).According to the report (Everatt, D. and G. Solanki. 2005. A Nation of givers: Social giving amongst South Africans) this and other open-ended questions were later categorised and given numeric codes. However, a codebook was not included with the documentation provided to DataFirst

4. Variable Q22 (Question 22) “Is there one cause or charity or organisation you would definitely NOT give money to?” There are 14 values for Q22 (1-14). Again, this requires a code list for explanation.

5. Variable Q29 (Question 29) Q28 deals with the giving of goods/food/clothes. Q29 provides a breakdown of these items, and Q28Q29L lists time/labour as one of these. It seems that Q29L is incorrectly listed as a sub-set of goods/food/clothes. Also, giving time to causes is dealt with extensively in Q30A-Q and Q31A-Q, so this variable seems out of place.

6. Variable Q39 (Question 36) This concerns the giving of food, goods, or other forms of help to beggars/street children/people asking for help, but the question text does not specifically mention these forms of help, so can be misleading.

7. Variable Q44 (Question 44) Q44 asks the respondent to complete the sentence "Help the poor because…." There are 8 values for this variable (0-7 and 11). Again, a code list is required to explain these values.

8. Variable Q59 (Question 59) This question has three coded responses (1-3) so should have three values (or 4, with a “missing” value). There are 12 values for this variable, though (59A-59L). It is possible that this variable has been swopped with Q60 (However, Q60 only has 11 options in the questionnaire)

9. Variable Q60 (Question 60) The variable from this question only has 4 values, but there are 11 possible responses to this question (60A-60K). This variable could have been swopped with Q59 (In which case, the extra value needs explanation, as Q59 only has 11 options in the questionnaire.

10. Variables Q67 - Q82 From this point on the order of variables seems wrong, as the responses don't match the number of values listed in the questionnaire. The variables seem to refer to the next question along, e.g. Variable Q67 seems to have data emanating from Question 68, and so on. The data in the revised dataset has been corrected to reflect this.

11. There is no variable Q83 in the dataset, although there is a question 83 in the questionnaire. This seems to support the above explanation. Data users are requested to provide any additional findings on this that come to light in their research.

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.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank sponsored 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 data on the conditions under which South Africans live in order to provide policymakers with the data necessary for development planning. 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

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered 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 for the households in ESDs.

Kind of data

Sample survey data

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 demographics, 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, 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.

A literacy assessment module (LAM) was administered to two respondents in each household, (a household member 13-18 years old and a one between 18 and 50) to assess literacy levels.

Data appraisal

The data collected in clusters 217 and 218 are highly unreliable and have therefore been removed from the dataset currently available on the portal. Researchers who have downloaded the data in the past should download version 2.0 of the dataset to ensure they have the corrected data. Version 2.0 of the dataset excludes two clusters from both the 1993 and 1998 samples. During follow-up field research for the KwaZulu-Natal Income Dynamics Study (KIDS) in May 2001 it was discovered that all 39 household interviews in clusters 217 and 218 had been fabricated in both 1993 and 1998. These households have been dropped in the updated release of the data. In addition, cluster 206 is now coded as urban as this was incorrectly coded as rural in the first release of the data. Note: Weights calculated by the World Bank and provided with the original data are NOT updated to reflect these changes.

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.

This research explores how the lived relationships between alcohol control (as a debate, field of study, policies and practices), poverty and development in South Africa (SA) are manifested among Cape Town's (CT) poorest residents. While SA's urgent alcohol control debate is principally cast as a matter of public health, it also broaches concerns over urban and social development, poverty alleviation, security and post-apartheid social policy. This research therefore focuses on the practices and consequences of drinking as a platform from which to develop a renewed approach to the contemporary politics of the developing city. Drawing on policy analysis, health survey data, stakeholder interviews counterposed with in-depth interviews and small group meetings across four case study townships and informal settlements in CT, the project examines how the lived experiences of drinking are understood and taken up in the policymaking process. By this means, it also interrogates the conditions under which these practices become "problematic". The projects thus aims to contribute to prescient debates in development studies, geography, urban studies and public health; and provide a qualitative body of research to the ongoing popular and policy debate on alcohol and its relationship to broader urban concerns in CT, SA and beyond