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.

South Africa Number of Households: by Income: Number Income data was reported at 139.000 Unit th in 2017. This records a decrease from the previous number of 149.000 Unit th for 2016. South Africa Number of Households: by Income: Number Income data is updated yearly, averaging 130.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 149.000 Unit th in 2016 and a record low of 98.000 Unit th in 2009. South Africa Number of Households: by Income: Number Income data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

In 2022, South African households had an average disposable income of over 50,500 South African rand (approximately 2,738 U.S. dollars). This was slightly higher than the previous year where the average disposable income was 50,343 South African rand (around 2,725 U.S. dollars). Within the observed period, the disposable income of households in the country was highest in 2018 at 51,236 South African rand (about 2,773 U.S. dollars), while it was lowest in 2004.

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 Number of Households: Black African: by Income: Remittances data was reported at 2,343.000 Unit th in 2017. This records a decrease from the previous number of 2,476.000 Unit th for 2016. South Africa Number of Households: Black African: by Income: Remittances data is updated yearly, averaging 2,195.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 2,574.000 Unit th in 2015 and a record low of 1,430.000 Unit th in 2012. South Africa Number of Households: Black African: by Income: Remittances data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

South Africa Number of Households: Black African: by Income: Business data was reported at 1,756.000 Unit th in 2017. This records an increase from the previous number of 1,695.000 Unit th for 2016. South Africa Number of Households: Black African: by Income: Business data is updated yearly, averaging 1,292.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 1,756.000 Unit th in 2017 and a record low of 177.000 Unit th in 2012. South Africa Number of Households: Black African: by Income: Business data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

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

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.

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 Number of Households: Black African: Female: by Income: Other Income Sources data was reported at 119.000 Unit th in 2017. This records an increase from the previous number of 113.000 Unit th for 2016. South Africa Number of Households: Black African: Female: by Income: Other Income Sources data is updated yearly, averaging 107.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 122.000 Unit th in 2015 and a record low of 75.000 Unit th in 2010. South Africa Number of Households: Black African: Female: by Income: Other Income Sources data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

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.

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

Abstract

In 2008, the South African Presidency embarked on an intensive effort to track changes in the well-being of South Africans by closely following about 28 000 people - young and old, rich and poor - over a period of years. This was undertaken through initiating the National Income Dynamics Study (NIDS). The NIDS survey is the first national panel study to document the dynamic structure of a sample of household members in South Africa and changes in their incomes, expenditures, assets, access to services, education, health, and other dimensions of well-being. A key feature of the panel study is its ability to follow people as they move out of their original 7 305 households. In doing this, the movement of household members as they leave and/or return to the household or set up their own households will be adequately captured in subsequent waves of this panel study.

The first “baseline” wave of NIDS was conducted by the Southern Africa Labour and Development Research Unit (SALDRU) based at the University of Cape Town's School of Economics. The first wave of fieldwork commenced in February 2008, and data and report released in July 2009. The design of NIDS envisaged data collection every two years.

Elsewhere in the world such surveys have been invaluable in promoting understanding of who is making progress in a society and who is not and, importantly, what factors are driving these dynamics. In addition, panel data is invaluable for the purposes of evaluating and monitoring the efficacy of social policies and programmes. This is because the panel allows researchers and policy analysts to see how households and individuals are impacted when they become eligible for these programmes.

Geographic coverage

The NIDS 2008 covered the whole of South Africa. The lowest level of geographic aggregation for the data is district municipality.

Analysis unit

The units of analysis in the NIDS 2008 survey are individuals and households.

Universe

The target population for NIDS 2008 was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified, two-stage cluster sample design was employed in sampling the households to be included in the base wave. In the first stage, 400 Primary Sampling Units (PSUs) were selected from Stats SA's 2003 Master Sample of 3000 PSUs. This Master Sample was the sample used by Stats SA for its Labour Force Surveys and General Household Surveys between 2004 and 2007 and for the 2005/06 Income and Expenditure Survey. Each of these surveys was conducted on non-overlapping samples drawn within each PSU.

The sample of PSUs for NIDS is a subset of the Master Sample. The explicit strata in the Master Sample are the 53 district councils (DCs). The sample was proportionally allocated to the strata based on the Master Sample DC PSU allocation and 400 PSUs were randomly selected within strata. It should be noted that the sample was not designed to be representative at provincial level, implying that analysis of the results at province level is not recommended.

Sample of dwelling units

At the time that the Master Sample was compiled, 8 non-overlapping samples of dwelling units were systematically drawn within each PSU. Each of these samples is called a "cluster" by Stats SA. These clusters were then allocated to the various household surveys that were conducted by Stats SA between 2004 and 2007. However, two clusters in each PSU were never used by Stats SA and these were allocated to NIDS.

It was sometimes necessary to re-list a PSU when the situation on the ground had drastically changed to an extent that the information recorded on the listing books no longer reflected the situation on the ground. In these cases, the PSU was re-listed and a new sample of dwelling units selected. However, the downside of re-listing a PSU is that the chance of sample overlap with dwelling units that are in other surveys is increased. The extent of this overlap cannot be quantified as the lists are no longer comparable. There is anecdotal evidence that sample overlap might have occurred in some PSUs.

Individual respondent selection

Fieldworkers were instructed to interview all households living at the selected address/dwelling unit. If they found that the dwelling unit was vacant or the dwelling no longer existed they were not permitted to substitute the dwelling unit but recorded this information on the household control sheet.

The household control sheet is a two page form. This form was completed for every dwelling unit that was selected in the study, regardless of whether or not a successful interview was conducted. Where more than one household resided at the selected dwelling unit, a separate household control sheet was completed for every household and they were treated in the data as separate units. In order to qualify as separate households they should not share resources or food. Lodgers and live-in domestic workers were considered separate households.

All resident household members at selected dwelling units were included in the NIDS panel, providing that at least one person in the household agreed to participate in the study. The household roster in the household questionnaire was used to identify potential participants in the study. Firstly, respondents were asked to list all individuals that have lived under this "roof" or within the same compound/homestead at least 15 days during the last 12 months OR who arrived in the last 15 days and this was now their usual residence. In addition the persons listed should share food from a common 'pot' and share resources from a common resource pool. All those listed on the household roster are considered household members.

All resident household members became NIDS sample members. In addition, non-resident members that were "out of scope" at the time of the survey also became NIDS sample members. Out-of-scope household members were those living in insititutions (such as boarding school hostels, halls of residence, prisons or hospitals) which were not part of the sampling frame. These individuals had a zero probability of selection at their usual place of residence and were thus included in the NIDS sample as part of the household that had listed them as non-resident members. These two groups constitute the permanent sample members (PSMs) and should have had an individual questionnaire (adult, child or proxy) completed for them. These individuals are PSMs even if they refused to be interviewed in the base wave.

An initial sample of 9600 dwelling units was drawn with the expectation of realizing 8000 successful interviews. However, during the initial round of fieldwork for Wave 1 we did not achieve the target number of households. Therefore we went back to the field to attempt to overturn refusals in 48 PSUs and to visit 24 new dwelling units in 32 of these areas. Stats SA drew an additional 24 dwelling units from their Master Sample in predominantly White and Asian PSUs in order to improve representation of these population groups in the data.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were administered for the National Income Dynamics Study 2008:

HOUSEHOLD QUESTIONNAIRE: This covered household characteristics, household roster, mortality history, living standards, expenditure, consumption, negative events, positive events, agriculture ADULT QUESTIONNAIRE: This was administered to all people in sampled households who were 15-years old or older on the day of the interview. The Adult Questionnaire collected data on demographics, education, labour market participation, income, health, well-being, numeracy and anthropometric measurements CHILD QUESTIONNAIRE: This asked questions of household members who were 14-years old or younger, and covered education, health, family support, grants and numeracy and anthropometric data PROXY QUESTIONNAIRE: These were completed where possible for adults who were unavailable or unable to answer their own adult questionnaire

Cleaning operations

Initially the intention was that data capture would be done in-house. However, by early March 2008 it became evident that data capture was proceeding too slowly and Citizen Surveys was awarded the tender for the work.All questionnaires were double captured and anomolies reconciled. Regular data dumps enabled the checking of captured data against hard copies of the questionnaires.

Response rate

Response rates in phase 1 of Wave 1 of the NIDS survey were disappointing and phase 2 was embarked upon to realise a more acceptable base wave sample. A detailed analysis of household level and individual level response rates follows. Item non-response rates are not addressed here. Such non-response is flagged in the data and is appropriately discussed in the context of specific analyses in the Discussion Paper series.

Household response rates were calculated using the number of visited dwelling units as the denominator and the number of participating households as the numerator. In the instances where response rates are given by race the predominant race group of the PSU is assigned to all households in that PSU. This is done because, by definition, non participating households were not interviewed and we did not gather information about the race of their members from the questionnaires.

Every effort was made to correctly

South Africa Number of Households: Female: by Income: Business data was reported at 708.000 Unit th in 2017. This records an increase from the previous number of 655.000 Unit th for 2016. South Africa Number of Households: Female: by Income: Business data is updated yearly, averaging 499.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 708.000 Unit th in 2017 and a record low of 79.000 Unit th in 2012. South Africa Number of Households: Female: by Income: Business data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.

In 2025, around 438.6 million people in Africa were living in extreme poverty, with the poverty threshold at 2.15 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 426 million by 2030.

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services.

Dates: 2008 – ongoing. First 5 “waves” implemented by SALDRU.

Funding: The Presidency (2008 – 2013); The Department of Planning, Monitoring and Evaluation (2014 – Present).

SALDRU people: Murray Leibbrandt, Ingrid Woolard, Cecil Mlatsheni and Reza C. Daniels.

Coverage: Nationally representative of the South African population.

Initial Sample size (2008): Approximately 28 000 individuals.

Data: The survey’s questionnaires, technical documents and reports for Wave 1, Wave 2, Wave 3, Wave 4 and Wave 5 are available for download from DataFirst’s Open Data Portal. NIDS produces public release data, which is also available for download from DataFirst’s Open Data Portal and secure data, which can only be accessed through DataFirst’s Secure Research Data Centre.

Included sections: Household Living Standards; Household Composition and Structure; Mortality; Household Food and Non-food Spending and Consumption; Household Durable Goods, Household Net Assets; Agriculture; Demographics; Birth Histories and Children; Parents and Family Support; Labour Market Participation and Economic Activity; Income and Expenditure; Grants; Contributions Given and Received; Education; Health; Emotional Health; Household Decision-making; Wellbeing and Social Cohesion; Anthropometric Measurements; Personal Ownership and Debt.

Geographic coverage

The NIDS data is nationally representative. The survey began in 2008 with a nationally representative sample of over 28,000 individuals in 7,300 households across the country. The survey is repeated every two years with these same household members, who are called Continuing Sample Members (CSMs). The survey is designed to follow people who are CSMs, wherever they may be in SA at the time of interview. The NIDS data is therefore, by design, not representative provincially or at a lower level of geography (e.g. District Council).

Analysis unit

Households and individuals

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

NIDS is a national panel (longitudinal) survey which began with a sample of 28 000 South Africans. NIDS' cycles of data collection, referred to as "waves" were undertaken. In Wave 1 (2008), 400 Enumerator Areas, comprising of 7296 households were selected for inclusion in the NIDS sample. 300 fieldworkers spread out across all nine provinces of the country in search of the 28 226 people that formed part of these selected households; successfully interviewing 26 776 of these individuals during Wave 1.

In subsequent waves, the original sample members are tracked and re-interviewed. Anyone that they live with at the time is also interviewed. In Wave 2 (2010-2011) 28 537 individuals were interviewed; in Wave 3 (2012) 32 582 were interviewed; and in Wave 4 (2014-2015) 37 368 were interviewed. Data collection for Wave 5 took place in 2017 and included a sample "top-up" to increase the number of white, Indian and high income respondents who had experienced low baseline response rates in Wave 1 and higher attrition rates between Waves 1-4. During Wave 5, 39,434 individuals were successfully interviewed, of which, 2016 were from the "top-up" sample. The data for Wave 5 was released at the end of August 2018.

More information on NIDS sampling refer to NIDS Technical Paper Number 1 http://www.nids.uct.ac.za/publications/technical-papers/108-nids-technical-paper-no1/file

Mode of data collection

Face-to-face [f2f]

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of black households in the rural Eastern Cape and black and colored households in urban Western Cape.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or colored EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Stats SA) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Colored)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban colored (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban colored households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least

In 2023, according to the Gini coefficient, household income distribution in the United States was 0.47. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. What is the Gini coefficient? The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of one represents perfect economic inequality. The Gini coefficient helps to visualize income inequality in a more digestible way. For example, according to the Gini coefficient, the District of Columbia and the state of New York have the greatest amount of income inequality in the U.S. with a score of 0.51, and Utah has the greatest income equality with a score of 0.43. The Gini coefficient around the world The Gini coefficient is also an effective measure to help picture income inequality around the world. For example, in 2018 income inequality was highest in South Africa, while income inequality was lowest in Slovenia.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

South Africa had the highest inequality in income distribution in 2023 with a Gini score of 63. Its South African neighbor Namibia followed in second. The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, a value of 100 absolute inequality. All the 20 most unequal countries in the world were either located in Africa or Latin America & The Caribbean.

South Africa Number of Households: Black African: Female: by Income data was reported at 5,651.000 Unit th in 2017. This records a decrease from the previous number of 5,823.000 Unit th for 2016. South Africa Number of Households: Black African: Female: by Income data is updated yearly, averaging 5,232.000 Unit th from Jul 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 5,823.000 Unit th in 2016 and a record low of 2,310.000 Unit th in 2009. South Africa Number of Households: Black African: Female: by Income data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.H007: Number of Households: by Income.