Ageing, Well-being and Development Project 2002-2008
Brazil, South Africa, 2002 - 2009
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Reference ID
bra-zaf-idpm-awdp-2002-2008-v1
Producer(s)
Armando Barrientos, Peter Lloyd-Sherlock
Metadata
Created on
Oct 04, 2013
Last modified
Oct 08, 2020
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Identification
Survey ID Number
bra-zaf-idpm-awdp-2002-2008-v1
Title
Ageing, Well-being and Development Project 2002-2008
Translated Title
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
Country
| Name | Country code |
|---|---|
| Brazil | bra |
| South Africa | zaf |
Study type
Service Provision Assessment [hh/spa]
Abstract
The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.
Kind of Data
Sample survey data
Unit of Analysis
Households and individuals
Version
Version Description
v1: Edited, anonymised dataset for public distribution
Version Date
2009
Version Notes
Prior to the addition of the 2008/9 data, this dataset was known as the Non-Contributory Pensions and Poverty Study (NCPPS).
Scope
Notes
Individual: Demographic characteristics, education, mortality, disability, grants, employment status, pensions, remittances, financial activity, subjective health, illness, savings, assets, expenditure, disaster exposure, alcoho/drugs, community facilities
Household: Dwelling characteristics, water, plumbing, electricity
Household: Dwelling characteristics, water, plumbing, electricity
Topics
| Topic | Vocabulary |
|---|---|
| Aid Effectiveness | World Bank |
| Debt Management | World Bank |
| Education | World Bank |
| Access to Finance | World Bank |
| Migration & Remittances | World Bank |
| Health | World Bank |
| Social Protection (includes Pensions, Safety Nets, Social Funds) | World Bank |
| Disability | World Bank |
| Labor Markets | World Bank |
| Poverty | World Bank |
Keywords
| Keyword |
|---|
| ageing |
| well-being |
| demography |
Coverage
Universe
South Africa: the survey covered all members of black households in the rural Eastern Cape and black and coloured households in urban Western Cape.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Armando Barrientos | Institute for Development Policy and Management, University of Manchester |
| Peter Lloyd-Sherlock | School of Development Studies, University of East Anglia |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Helena Legido-Quigley | Project co-ordinator | |
| João Saboia | Instituto de Economica, Universado Federal de Rio de Janeiro | Brazilian partner |
| Maria Lucia Teixeira Werneck Viana | Instituto de Economica, Universado Federal de Rio de Janeiro | Brazilian partner |
| Monica Ferreira | The Albertina and Walter Sisulu Institute of Ageing in Africa, University of Cape Town | South African partner |
| Valerie Møller | Institute of Social and Economic Research, Rhodes University | South African partner |
| Mark Gorman | HelpAge International | Partner |
| Amanda Heslop | HelpAge International | Partner |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| UK Department for International Development, Economic and Social Research (ESCOR) | DfiD | Funding for the 2002 NCPSS |
| UK Cross-Council Research Programme on the New Dynamics of Ageing | Financial support |
Sampling
Sampling Procedure
South Africa:
In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:
1. Predominantly black or coloured EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Statssa) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)
These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:
1. Geographical areas by population group of head of household for person weighted (African/Black or Coloured)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).
These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.
All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.
Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.
Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.
The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly-selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.
The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.
Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban coloured (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban coloured households.
Brazil:
Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"
The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.
The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.
To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:
1. At least 100 homes in the sector
2. At least 60% of households whose income was, at most, equal to two times the minimum wage
3. A minimum of 8% of elderly (60 or older) in the population.
Sectors fulfilling the above criteria were chosen randomly within each sector. As a way to diversify the selection of households, a target was set between 16 and 20 households with elderly members (at least one aged 60 years or more). Researchers surveyed the chosen sectors until the quota was fulfilled. The sectors randomly selected could not always be surveyed, however, given the level of urban violence that hit Rio de January in the year of initial data collection. Territorial disputes between drug gangs prevented access to some sectors. These were replaced by sectors reserved for such eventualities. Given the fact that the majority of pensions in Brazil are contributory, randomly surveying households meant that there was the possibility of only obtaining a very small number people with non-contributory pensions. Thus, the selected households were supplemented with records from the beneficiaries of non-contributory pensions in their municipalities. The records, however, were very inaccurate, including deceased beneficiaries, non-existent addresses and many other problems. Despite the difficulties, the existence of the records is allowed the collection of a reasonable number of recipients of pensions and non-contributory retirement funds.
Upon returning in 2008, the Brazilian fieldwork team managed to discover 340 of the Rio de Janeiro families and 303 of the Ilheus families, a success rate of 67.3% and 60.4% respectively. 643 out of the original 1006 households were discovered in the same household. Beyond that, 28 additional families interviewed in 2002 were discovered in a different household within the same area. This meant that 165 replacement households were required in the Rio de Janeiro area and 199 in Ilheus. 363 new families, therefore, are included in the 2008 sample.
In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:
1. Predominantly black or coloured EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Statssa) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)
These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:
1. Geographical areas by population group of head of household for person weighted (African/Black or Coloured)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).
These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.
All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.
Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.
Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.
The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly-selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.
The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.
Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban coloured (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban coloured households.
Brazil:
Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"
The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.
The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.
To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:
1. At least 100 homes in the sector
2. At least 60% of households whose income was, at most, equal to two times the minimum wage
3. A minimum of 8% of elderly (60 or older) in the population.
Sectors fulfilling the above criteria were chosen randomly within each sector. As a way to diversify the selection of households, a target was set between 16 and 20 households with elderly members (at least one aged 60 years or more). Researchers surveyed the chosen sectors until the quota was fulfilled. The sectors randomly selected could not always be surveyed, however, given the level of urban violence that hit Rio de January in the year of initial data collection. Territorial disputes between drug gangs prevented access to some sectors. These were replaced by sectors reserved for such eventualities. Given the fact that the majority of pensions in Brazil are contributory, randomly surveying households meant that there was the possibility of only obtaining a very small number people with non-contributory pensions. Thus, the selected households were supplemented with records from the beneficiaries of non-contributory pensions in their municipalities. The records, however, were very inaccurate, including deceased beneficiaries, non-existent addresses and many other problems. Despite the difficulties, the existence of the records is allowed the collection of a reasonable number of recipients of pensions and non-contributory retirement funds.
Upon returning in 2008, the Brazilian fieldwork team managed to discover 340 of the Rio de Janeiro families and 303 of the Ilheus families, a success rate of 67.3% and 60.4% respectively. 643 out of the original 1006 households were discovered in the same household. Beyond that, 28 additional families interviewed in 2002 were discovered in a different household within the same area. This meant that 165 replacement households were required in the Rio de Janeiro area and 199 in Ilheus. 363 new families, therefore, are included in the 2008 sample.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2002-10-01 | 2002-11-18 | Brazil - Wave 1 |
| 2002-11-21 | 2002-12-31 | Western Cape, South Africa - Wave 1 |
| 2008-11-04 | 2009-01-20 | Rio de Janeiro, Brazil - Wave 2 |
| 2009-01-20 | 2009-02-01 | Ilheus, Brazil - Wave 2 |
| 2009-03-31 | South Africa - Wave 2 |
Data Collection Mode
Face-to-face [f2f]
Access policy
Contacts
| Name | URL | |
|---|---|---|
| DataFirst Helpdesk | support@data1st.org | Link |
Access conditions
Public use files, available to all
Citation requirements
Barrientos, A, and Lloyd-Sherlock, P. Ageing, Well-being and Development Project 2002-2008. [dataset]. Version 1. Manchester: Institute for Development Policy and Management [producer], 2009. Cape Town: DataFirst [distributor], 2013. DOI: https://doi.org/10.25828/f6eq-g424
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Manager, DataFirst | University of Cape Town | support@data1st.org | Link |
Disclaimer and copyrights
Copyright
(c) 2009, IDPM
Metadata production
DDI Document ID
ddi-zaf-bra-datafirst-awdp-2002-2008-v1
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | Metadata creator |
Date of Metadata Production
2020-10-08
DDI Document version
Version 5