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 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.

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.

Poverty rate at national poverty line of South Africa went up by 4.32% from 53.2 % in 2010 to 55.5 % in 2014. Since the 6.76% drop in 2008, poverty rate at national poverty line dropped by 10.63% in 2014. National poverty rate is the percentage of the population living below the national poverty line. National estimates are based on population-weighted subgroup estimates from household surveys.

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

South Africa ZA: 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.

55.5 (%) in 2014. 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.

South Africa: Poverty, percent of population: The latest value from 2014 is 55.5 percent, an increase from 53.2 percent in 2010. In comparison, the world average is 25.08 percent, based on data from 48 countries. Historically, the average for South Africa from 2005 to 2014 is 59.35 percent. The minimum value, 53.2 percent, was reached in 2010 while the maximum of 66.6 percent was recorded in 2005.

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

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. 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 acute 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. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

37.3 (%) in 2014. 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.

According to monthly surveys conducted in South Africa, September 2024 revealed that a ** 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 ** percent, observed in December 2022, and ** percent, reaching a peak in August 2023.

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

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

The South African government initiated the Ilima-Letsema programme to promote sustainable agricultural activities and improve the livelihoods of households in farming communities. The purpose of the paper is to evaluate the Ilima-Letsema programme's contribution to job creation and poverty alleviation in the Midvaal Local Municipality of Gauteng Province, South Africa. The quantitative research approach and survey design were used to conduct the study. Data were collected from 196 beneficiaries of the programme through face-to-face interviews using structured questionnaires. Primary data were analyzed using descriptive statistics, T-test, Multiple Linear Regression (MLR), Correlation, Cochran's Q and McNemar tests. The results indicated that the Ilima-Letsema programme significantly increased farmers' income and created jobs. Net farm income was positively and significantly influenced by education level, farmland size and jobs created. Net farm income was a significant predictor of jobs created in the post-support era, whereas education level and farmland size had negative impact. In addition, the programme significantly uplifted the elite beneficiaries from the upper-bound poverty line (UPBL); however, it did not uplift poor farmers from the food poverty line (FPL) and lower bound poverty line (LBPL). Education, farmland size and income had a positive and significant correlation (p < 0.05) with the programme's ability to uplift the beneficiaries from FPL, UBPL and UPBL amounts. It is recommended that Ilima-Letsema's rollout and budget should be expanded to enable more farmers to generate income and create employment opportunities for unskilled laborers in the agricultural sector. Again, the criteria for the programme should be changed in favor of less educated and resource-poor smallholder farmers, and producers with low net income.

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

Abstract

The Project for Statistics on Living standards and Development was a 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.

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]

This study compares traditional poverty lines with a living income benchmark using data from two rural farming communities. Findings show household incomes fall significantly below the 11.79 USD PPP/AE/day benchmark, with food costs making up nearly two-thirds of the required income. Unlike traditional poverty lines, which reflect survival thresholds, the living income benchmark offers a more comprehensive view of rural poverty by accounting for food, housing, education, and healthcare. The results highlight the inadequacy of on-farm income and the need for off-farm earnings and social protection. The study advocates using the living income benchmark to inform rural poverty policy.

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.