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.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

The Afrobarometer is a comparative series of public attitude surveys that collects and disseminates data regarding Africans' views on democracy, governance, the economy, civil society, and related issues. The data are collected from nationally representative samples in face-to-face interviews in the language of the respondent's choice. Standard topics for the Afrobarometer include attitudes toward and evaluations of democracy, governance and economic conditions, political participation, and national identity. In addition, Round 6 surveys included special modules on taxation; tolerance; crime, conflict and insecurity; political corruption; interregional relations; perceptions of China; use of technology; and social service delivery. This particular data collection was concerned with the attitudes and opinions of the citizens of South Africa, and also includes a number of "country-specific questions" designed specifically for respondents of the South Africa survey. These "country-specific questions" solicited respondents' opinions on topics such as trust in political and non-political organizations, the government performance, race-based unequal treatment, changes in government, and citizenship. The surveys also collect a large set of socio-demographic indicators such as age, gender, education level, poverty level, language and ethnicity, and religious affiliation, as well as political party affiliation. Afrobarometer Round 6 surveys were implemented in 36 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

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

National coverage

Analysis unit

Units of analysis in the survey were households and individuals

Universe

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

Kind of data

Sample survey data [ssd]

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.

Data appraisal

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

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

As of 2019, approximately 18 million South Africans vulnerable to poverty or in need of state support received social grants, relief assistance or social relief paid by the government. The largest group that received social grants were Black and Coloured South Africans.

In 2021, Southern Africa's richest ** percent held around ** percent of the total wealth. Furthermore, the richest one percent in the region held over ** percent. The other African regions had a slightly smaller share of wealth with the wealthiest people. For instance, in West Africa, the richest ** percent held close to ** percent of the wealth, while the richest one percent held ** percent. On the other hand. The poorest ** percent in all the regions held lower than ***** percent of the wealth.