The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

Vulture populations are experiencing rapid declines across the globe. While the declines have been most precipitous in Asia, recent reports suggest African populations are likewise imminently threatened. As the factors underlying these general population trends are multifaceted and will vary in their relative intensity spatially, it is imperative that monitoring data across different vulture populations is assimilated if targeted conservation action is to prove most effective. In this study, we highlight a medium-term decline in the African White-backed Vulture Gyps africanus population inhabiting the southern Kalahari, South Africa, using a long-term behavioural data set collected from a habituated population of meerkats Suricata suricatta. Meerkats emit an alarm call on sighting airborne vultures, which elicits a group-level response, such that the rates at which this behaviour is recorded in meerkats provides a high-resolution proxy for local vulture abundance. Although unconventional, this sampling method uncovered a steady decline over 17 years in White-backed Vulture numbers that mirrors the temporal decline recently documented in other southern African populations.

Abstract

The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities. This report has two main objectives. Firstly, to present the key findings of the GHS 2005 in the context of the trends since the first GHS was conducted in 2002; and secondly, to provide a more in-depth analysis of the detailed questions related to selected service delivery issues.

Geographic coverage

The scope of the General Household Survey 2005 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2005 are individuals and households.

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the GHS 2005 a multi-stage stratified sample was drawn using probability proportional to size principles. The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of Census 2001. As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample was allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated. The master sample is divided into five independent clusters. In order to avoid respondent fatigue, the Labour Force Survey (LFS) is a rotating panel survey that is conducted twice yearly, whereas the GHS sample uses different clusters.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2005 questionnaire collected data on:

Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

87,5% of the expected 32 146 interviews were successfully completed and positive responses were obtained. It was not possible to complete interviews in 3,8 % of the sampled dwelling units. An additional 8,3% of all interviews were not conducted for various reasons, for instance the sampled dwelling units had become vacant or had changed status (e.g. they were used as shops or small businesses at the time of the enumeration but were originally listed as dwelling units).

Sampling error estimates

Estimation and use of standard error:

The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.

In 1800, the population of modern day area of South Africa was approximately 1.44 million. Like most of the continent, the population of South Africa increased gradually through most of the 19th century, reaching 4.71 million by the start of the 20th century. Beginning in the 20th century however, the population would begin to rise exponentially as industrialization, advances in medicine and health, and the spread of vaccinations allowed for lower child mortality rates and increased life expectancy among adults. The population of South Africa would continue to rise exponentially for almost a century, going from just under 5 million at the start of the 1900s to almost 45 million by 2000. However, since the early 2000s, South Africa’s population growth has slowed, the result of a significant decrease in fertility rates in the country in recent years. In 2020, South Africa is estimated to have a population of 59.31 million.

Abstract

This report has two main objectives: firstly, to present the key findings of the GHS 2011 in the context of the trends that were measured since the first GHS was conducted in 2002; and secondly, to provide a more in-depth analysis of the detailed questions related to selected service delivery issues.

Geographic coverage

All private households all nine provinces of South Africa.

Analysis unit

• Households
• Individuals

Universe

The survey covers all household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalised and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Periodicity of Data collection: Annual.

Sampling procedure

A multi-stage design was used, which is based on a stratified design with probability proportional to size selection of primary sampling units (PSUs) at the first stage and sampling of dwelling units (DUs) with systematic sampling at the second stage. After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2001 data (secondary stratification). Survey officers employed and trained by Stats SA visited all the sampled dwelling units in each of the nine provinces. During the first phase of the survey, sampled dwelling units were visited and informed about the coming survey as part of the publicity campaign. The actual interviews took place four weeks later. A total of 25 653 households (including multiple households) were successfully interviewed during face-to-face interviews.

Sampling deviation

The sample design for the GHS 2011 was based on a master sample (MS) that was originally designed for the QLFS and was used for the first time for the GHS in 2008. This master sample is shared by the Quarterly Labour Force Surveys (QLFS), General Household Survey (GHS), Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Surveys (IES). The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.

Research instrument

Most questions in the GHS questionnaire are pre-coded, i.e. there are a set number of choices from which one or more must be selected. For open-ended 'write-in' questions, the description will state that post-coding occurred and explain how this was done. Most variables have been pre-coded from the questionnaire and are not repeated in the variable description. Where the coding is not apparent, the description either provides the codes or indicates where code lists are to be found. One limitation of th study mentions that, it is important to note that the questionnaires for the GHS series were revised extensively in 2009 and that some questions might not be exactly comparable to the data series before then. The details of the questions included in the GHS questionnaire are covered in four sections, each focusing on a particular aspect. Depending on the need for additionalinformation, the questionnaire is adapted on an annual basis. New sections may be introduced on a specific topic for which information is needed or additional questions may be added to existing sections. Likewise, questions that are no longer necessary may be removed. The GHS questionnaire has undergone some revisions over time. These changes were primarily the result of shifts in focus of government programmes over time. The 2002–2004 questionnaires were very similar. Changes made to the GHS 2005 questionnaire included additional questions in the education section with a total of 179 questions. Between 2006 and 2008, the questionnaire remained virtually unchanged. In preparation for GHS 2009. Extensive stakeholder consultation took place during which the questionnaire was reviewed to be more in line with the monitoring and evaluation frameworks of the various government departments. Particular sections that were modified substantially during the review were the sections on education, social development, housing, agriculture, and food security. Even though the number of sections and pages in the questionnaire remained the same, questions in the GHS 2009 were increased from 166 to 185 between 2006 and 2008. Following the introduction of a dedicated survey on Domestic Tourism, the section on tourism was dropped for GHS 2010. Due to a further rotation of questions, the GHS 2011 questionnaire contained 166 questions as follows:

Contents of the GHS 2011 questionnaire

Section Number of Details of each section questions Cover page Household information, response details, field staff information, result codes, etc. Flap 6 Demographic information (name, sex, age, population group, etc.) Section 1 55 Biographical information (education, health, disability, welfare) Section 2 20 Economic activities Section 3 65 Household information (type of dwelling, ownership of dwelling, electricity, water and sanitation, environmental issues, services, transport, etc.) Section 4 20 Food security, income and expenditure (food supply, agriculture, expenditure, etc.) All sections 166 Comprehensive coverage of living conditions and service delivery

Cleaning operations

Historically the GHS used a conservative and hands-off approach to editing. Manual editing, and little if any imputation was done. The focus of the editing process was on clearing skip violations and ensuring that each variable only contains valid values. Very few limits to valid values were set and data were largely released as it was received from the field. With GHS 2009, Stats SA introduced an automated editing and imputation system that was continued for GHS 2010 and GHS 2011. The challenge was to remain as much as possible true to the conservative approach used prior to GHS 2009 and yet, at the same time, to develop a standard set of rules to be used during editing which could be applied consistently across time. When testing for skip violations and doing automated editing, the following general rules are applied in cases where one question follows the filter question and the skip is violated:

• If the filter question had a missing value, the filter is allocated the value that corresponds with the subsequent question which had a valid value. • If the values of the filter question and subsequent question are inconsistent, the filter question’s value is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the question subsequent to the filter question is dealt with by either setting it to missing and imputing or if that fails printing a message of edit failure for further investigation, decision-making and manual editing.

In cases where skip violations take place for questions where multiple questions follow the filter question, the rules used are as follows: • If the filter question has a missing value, the filter is allocated the value that corresponds with the value expected given the completion of the remainder of the question set. • If the filter question and the values of subsequent questions values were inconsistent, a counter is set to see what proportion of the subsequent questions have been completed. If more than 50% of the subsequent questions have been completed the filter question’s value is modified to correspond with the fact that the rest of the questions in the set were completed. If less than 50% of the subsequent questions in the set were completed, the value of the filter question is set to missing and imputed using either the hot-deck or nearest neighbour imputation techniques. The imputed value is then once again tested against the skip rule. If the skip rule remains violated the questions in the set that follows the filter question are set to missing.

Response rate

Response rates per province, 2011

Province Per cent Western Cape 91,3 Eastern Cape 98,9 Northern Cape 94,1 Free State 97,3 KwaZulu-Natal 99,2 North West 97,0 Gauteng

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.

Abstract

The GHS is an annual household survey conducted by Stats SA since 2002. The survey replaced the October Household Survey (OHS) which was introduced in 1993 and was terminated in 1999. The survey is an omnibus household-based instrument aimed at determining the progress of development in the country. It measures, on a regular basis, the performance of programmes as well as the quality of service delivery in a number of key service sectors in the country. The GHS covers six broad areas, namely education, health and social development, housing, household access to services and facilities, food security, and agriculture. This report has three main objectives: firstly, to present the key findings of GHS 2015. Secondly, it provides trends across a fourteen year period, i.e. since the GHS was introduced in 2002; and thirdly, it provides a more in-depth analysis of selected service delivery issues. As with previous reports, this report will not include tables with specific indicators measured, as these will be included in a more comprehensive publication of development indicators, entitled Selected development indicators (P0318.2).

Geographic coverage

The General Household Survey 2015 had national coverage.The lowest level of geographic aggregation for this dataset is Province and Metro.

Analysis unit

The units of anaylsis for the General Household Survey 2015 are individuals and households.

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers’ hostels. The survey does not cover other collective living quarters such as students’ hostels, old-age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalised and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

The General Household Survey (GHS) uses the Master Sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household-based surveys having design requirements that are reasonably compatible with the GHS. The GHS 2015 collection was based on the 2013 Master Sample. This Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates.

The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Caution must be exercised when interpreting the results of the GHS at low levels of disaggregation. The sample and reporting are based on the provincial boundaries as defined in December/January 2006. These new boundaries resulted in minor changes to the boundaries of some provinces, especially Gauteng, North West, Mpumalanga, Limpopo, Eastern Cape and Western Cape. In previous reports the sample was based on the provincial boundaries as defined in 2001, and there will therefore be slight comparative differences in terms of provincial boundary definitions.

Details of the sampling proceedure can be found in Report No. P0318 available from Statistics Souoth Africa and attached to this Survey as an external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single survey was adminsitered for each household.

Teh Questionnaire comprises the following main sections:

A: Particulars of the dwelling B: Households at the selected dwelling unit C: Field staff D: Survey period E: Response details

Section 1: Household Specific Functioning Section 2: Health and General Functioning Section 3: Social Security and Religion Section 4: Economic Activities Section 5: General Household Information and Service Delivery Section 6: Communication and Transport Section 7: Health, welfare and Food Security Section 8: Household Livelihoods Section 9: Mortality in the last 12 months Section 10: Interviewer summary section

Response rate

Province / Metropolitan Area Response Rates
National 90,48
Western Cape 91,67
Non Metro 93,17
City of Cape Town 91,03
Eastern Cape 94,77
Non Metro 96,66
Buffalo City 92,54
Nelson Mandela Bay 89,52
Northern Cape 95,00
Free State 95,00
Non Metro 95,37
Mangaung 94,07
KwaZulu-Natal 95,23
Non Metro 96,58
eThekwini 92,87
North West 94,99
Gauteng 78,01
Non Metro 93,62
Ekurhuleni 81,76
City of Johannesburg 71,11
City of Tshwane 75,47
Mpumalanga 97,24
Limpopo 98,83

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Personal Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

The general characteristics of the screened population.

Abstract

The World Values Survey aims to attain a broad understanding of socio-political trends (i.e. perceptions, behaviour and expectations) among adults across the world.

Geographic coverage

National The sample was distributed as follows: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas)

Analysis unit

Individual

Universe

The sample included adults 16 years+ in South Africa

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample had to be representative of urban as well as rural populations. Roughly the distribution was as follows: - South Africa: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas).

A standard form of sampling instructions was sent to each agency to ensure uniformity in the sampling procedure. Markinor stratified the samples for each country by region, sex and community size. To this end, statistics and figures that were supplied to us by the agencies were used. However, we requested the agencies to revise these where necessary or where alternatives would be more effective. The agencies then supplied the street names for the urban starting points, and made suggestions for sampling procedures in rural areas where neither maps nor street names were available. From sample-point level, the respondent selection was done randomly according to a selection grid used by Markinor (the first two pages of the master questionnaire).

Substitution was permitted after three unsuccessful calls. Six interviews were conducted at each sample point. The male/female split was 50/50. The urban sample included all community sizes greater than 500 and the rural sample all community sizes less than 500. This is the definition of urban and rural used in South Africa.

Remarks about sampling: -Final numbers of clusters or sampling points: 500 -Sample unit from office sampling: Street Names

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was translated from the English questionnaire by a specialist translator The translated questionnaire was pre-tested. The pre-tests were part of the general pilots. In total 20 pilots were conducted. The English questionnaire from the University of Michigan was used to make the WVS. Extra questions were added at the end of the questionnaire. Also, country specific questions were included at the end of the questionnaire, just before the demographics.The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 16 and there was not any upper age cut-off for the sample.

Cleaning operations

Some measures of coding reliability were employed. Each questionnaire is coded against the coding frame. A minimum of 10% of each coders work is checked to ensure consistency in interpretation. If any discrepancies in interpretation are World Values Survey (1999-2004) - South Africa 2001 v.2015.04.18 discovered, a 100% check is carried out on that particular coders work. Errors were corrected individually and automatically.

Sampling error estimates

The error margins for this survey can be calculated by taking the following factors into account: - all samples were random (as opposed to quota-controlled) - the sample size per country (or segment being analysed) - the substitution rate per country (or segment being analysed) - the rates were recorded on CARD 1; col. 805 of the questionnaire. From the substitution rate, the response rate can be calculated.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA) which collects information about the labour market activities of individuals aged 15 years or older who live in South Africa. Prior to the introduction of the QLFS in 2008, the Labour force Survey (LFS) was the major source of labour market information. The LFS was conducted in March and September each year over the period 2000–2007 and replaced the annual October Household Survey (OHS) as the principal vehicle for collecting labour market information.

This report is the seventh annual report produced by Stats SA on the labour market in South Africa. The report includes, for the third time, an analysis of labour market dynamics (discussed in Chapter 2). As in previous reports, annual historical data are included in a statistical appendix. Objective The objective of this report is to analyse the patterns and trends of annual labour market results over the period 2008 to 2014. Data sources Quarterly Labour Force Survey – 2008 to 2014 (Average of the results for Quarters 1 to 4 each year).

Geographic coverage

the nine provinces of South Africa

Analysis unit

Individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) is based on a master sample of which there have been three so far. The design of the current master sample follows.

Current master sample The Quarterly Labour Force Survey (QLFS) frame has been developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a master sample of primary sampling units (PSUs) which comprises EAs that are drawn from across the country.

The sample is designed to be representative at provincial level and within provinces at metro/nonmetro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four subgroups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the redesigned Labour Force Survey (i.e. the QLFS) is based on a stratified twostage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Sample rotation Each quarter, a ¼ of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings will remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, say two quarters and a new household moves in then the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (unoccupied).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire of QLFS is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

Description: The data set for dissemination contains 728 variables and 17 926 cases of respondents aged 15 years and older who participated in the SANHANES-1 Adult Questionnaire.

The questionnaire covers the following sections: geographic information, biographic details of the respondent, non-communicable diseases, tuberculosis, nutrition, perceptions of respondent's general and mental health, as well as health care utilisation. Abstract: The South African National Health and Nutrition Examination Survey (SANHANES) was established as a continuous population health survey to address the changing health needs in the country and provide a broader and more comprehensive platform to study the health status of the nation on a regular basis.

The SANHANES-1, was conducted in 2011-12 among 27 580 eligible individuals, of which 25 532 individuals completed the interview, 12 025 underwent physical examinations and 8 078 provided blood specimens for biomarker testing.

This survey provides critical information to map the emerging epidemic of NCDs in South Africa among other defined priorities of the National Department of Health and analyses their social, economic, behavioural and environmental determinants. Data on the magnitude of and trends in NCDs, as well as other existing/emerging health priorities, is essential to develop national prevention and control programmes, assessing the impact of interventions, and evaluating the health status of the country.

The primary objectives of the SANHANES-1 were to assess defined aspects of the health and nutritional status of South Africans with respect to the prevalence of NCDs (specifically cardiovascular disease, diabetes and hypertension) and their risk factors (diet, physical activity and tobacco use):

The knowledge, attitudes and behaviour of South Africans with respect to NCDs and tuberculosis;

The nutritional status of South Africans as it relates to food security, dietary intake/ behaviour including alcohol consumption, body image and weight management;

The perceptions of general and mental health (stress and trauma) and the utilisation of healthcare services;

The behavioural (smoking, diet, physical inactivity) and social determinants of health and nutrition (demographic, socio-economic status and locality) and relate these to the health and nutritional status of the population.

As of January 2024, there were 45.34 million active internet users in South Africa. According to the same report, close to 26 million internet users in the country used social media, around 42.8 percent of the total population. The future of internet usage in South Africa: projected growth and mobile dominance South Africa's digital population grew significantly during the last decade. In 2023, almost 44 million people were connected to the internet, up from around 25 million in 2013. Furthermore, the majority of the South African population, specifically 78.7 percent, utilized mobile devices to access the internet in 2022. This proportion will increase to over 90 percent by 2027. Additionally, the number of mobile internet users in South Africa was almost 47.8 million in 2022. Social media usage in South Africa: popularity and demographics The country's most popular social media platform during the third quarter of 2022 was Meta’s instant messaging application WhatsApp. Facebook and Instagram ranked second and third among South African internet users. Moreover, a closer look into the demographics of social media users in the country reveals that people between the ages of 25 to 34 years made up the highest share of users in South Africa.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA) which collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

In 2005, Stats SA undertook a major revision of the Labour Force Survey (LFS) which had been conducted twice per year since 2000. This revision resulted in changes to the survey methodology, the survey questionnaire, the frequency of data collection and data releases, and the survey data capturing and processing systems. The redesigned labour market survey, the QLFS, is now the principal vehicle for collecting labour market information on a quarterly basis.

This report is the sixth annual report produced by Stats SA on the labour market in South Africa. It is based largely on revised data for the period 2008 to 2013 from the QLFS that reflect new population benchmarks from the 2011 Population Census. The report includes, for the second time, an analysis of labour market dynamics (discussed in Chapter 2). A new area of interest has also been added which discusses key labour market outcomes in South Africa in a global context (Chapter 7). As in previous reports, annual historical data (revised) are included in a statistical appendix.

Objective The objective of this report is two-fold: first, to analyse the patterns and trends in annual labour market results over the period 2008 to 2013, and second, to analyse important aspects of the labour market in South Africa in an international context.

Data sources Quarterly Labour Force Survey – 2008 to 2013 (Quarters 1 to 4, based on revised data) Quarterly Employment Statistics – 2008 to 2013 (Quarters 1 to 4) International Labour Organisation, Key indicators of the labour market (KILM), 2013

Geographic coverage

the nine provinces of South Africa

Analysis unit

Individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) is based on a master sample of which there have been three so far. The design of the current master sample follows.

Current master sample The Quarterly Labour Force Survey (QLFS) frame has been developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a master sample of primary sampling units (PSUs) which comprises EAs that are drawn from across the country.

The sample is designed to be representative at provincial level and within provinces at metro/nonmetro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four subgroups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the redesigned Labour Force Survey (i.e. the QLFS) is based on a stratified twostage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Sample rotation Each quarter, a ¼ of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings will remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, say two quarters and a new household moves in then the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (unoccupied).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire of QLFS is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

Employment Rate in South Africa decreased to 40.30 percent in the first quarter of 2025 from 41.10 percent in the fourth quarter of 2024. This dataset provides - South Africa Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The Future of Business Survey is a new source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the monthly survey - a partnership between Facebook, OECD, and The World Bank - provides a timely pulse on the economic environment in which businesses operate and who those businesses are to help inform decision-making at all levels and to deliver insights that can help businesses grow. The Future of Business Survey provides a perspective from newer and long-standing digitalized businesses and provides a unique window into a new mobilized economy.

Policymakers, researchers and businesses share a common interest in the environment in which SMEs operate, as well their outlook on the future, not least because young and innovative SMEs in particular are often an important source of considerable economic and employment growth. Better insights and timely information about SMEs improve our understanding of economic trends, and can provide new insights that can further stimulate and help these businesses grow.

To help provide these insights, Facebook, OECD and The World Bank have collaborated to develop a monthly survey that attempts to improve our understanding of SMEs in a timely and forward-looking manner. The three organizations share a desire to create new ways to hear from businesses and help them succeed in the emerging digitally-connected economy. The shared goal is to help policymakers, researchers, and businesses better understand business sentiment, and to leverage a digital platform to provide a unique source of information to complement existing indicators.

With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Geographic coverage

Argentina Australia Belgium Brazil Canada Colombia Egypt France Germany Ghana India Indonesia Ireland Israel Italy Kenya Mexico Nigeria Pakistan Philippines (the) Poland Portugal Russian Federation (the) South Africa Spain Taiwan Turkey United Kingdom of Great Britain and Northern Ireland (the) United States of America (the) Viet Nam

Analysis unit

The study describes small and medium-sized enterprises.

Universe

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

Twice a year in over 97 countries, the Facebook Survey Team sends the Future of Business to admins and owners of Facebook-designated small business pages. When we share data from this survey, we anonymize responses to all survey questions and only share country-level data publicly. To achieve better representation of the broader small business population, we also weight our results based on known characteristics of the Facebook Page admin population.

A random sample of firms, representing the target population in each country, is selected to respond to the Future of Business Survey each month.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMEs invited.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error (sampling frame of page owners does not include all relevant businesses but also may include individuals that don't represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

Abstract

The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Health Systems and Innovation Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 2 (2014/15) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content: - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Verbal Autopsy questionnaire Section 1: Information on the Deceased and Date/Place of Death Section 1A7: Vital Registration and Certification Section 2: Information on the Respondent Section 3A: Medical History Associated with Final Illness Section 3B: General Signs and Symptoms Associated with Final Illness Section 3E: History of Injuries/Accidents Section 3G: Health Service Utilization Section 4: Background Section 5A: Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilisation 6000 Social Networks 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

• Proxy Questionnaire Section1 Respondent Characteristics and IQ CODE Section2 Health State Descriptions Section4 Chronic Conditions and Health Services Coverage Section5 Health Care Utilisation

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 31 of the 32 federal states in Mexico. Colima was excluded. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older was selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). All 211 PSUs selected for wave 1 were included in the wave 2 sample. A sub-sample of 211 PSUs was selected from the 797 WHS PSUs for the wave 1 sample. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0 The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 6549 surveyed TSU: Individual = 6342 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the SAGE Wave 1 Questionnaires with some modification and new additions, except for verbal autopsy. SAGE Wave 2 used the 2012 version of the WHO Verbal Autopsy Questionnare. SAGE Wave 1 used an adapted version of the Sample Vital Registration iwth Verbal Autopsy (SAVVY) questionnaire. A Household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to 50 plus households only. In follow-up 50 plus household if the death occured since the last wave of the study and in a new 50 plus household if the death occurred in the