South Africa's Standard Bank Group ranked as the leading bank in Africa, according to the level of tier 1 capital, which consists of core capital, reserves, retained earnings, and minority interests. With operations in ** countries in the continent, the bank group registered a capital of ** billion U.S. dollars as of the end of 2023. The National Bank of Egypt followed, with * billion U.S. dollars in tier 1 capital as of June 2023.

South Africa's ******************* was the largest bank in Africa as of 2024, with total assets worth nearly *** billion U.S. dollars. Operating in 20 countries on the continent, the bank group also led the African banking sector by tier 1 capital. Ranking as the second-biggest bank in Africa, the National Bank of Egypt accumulated an asset value of around *** billion U.S. dollars in 2021. Overall, South Africa concentrated four out of the top 10 institutions with the largest assets in Africa. Main banking markets As of 2021, the total assets of the banking sector in Sub-Saharan Africa corresponded to **** percent of the region's GDP. The ratio, which offers an insight into the relationship between services provided by banks and the economy' size, increased substantially compared to previous years. Among countries, South Africa dominates the African banking industry with financial assets worth around *** billion U.S. dollars in 2021. Additionally, the aggregate tier 1 capital of major South African banks reached roughly **** billion U.S. dollars in 2022. North African nations, such as Egypt and Morocco, follow as main players in Africa’s banking sector. Financial inclusion has improved in Africa Around **** out of 10 Africans had a bank account in 2023, according to Statista forecasts. The banking penetration rate on the continent almost doubled compared to 2013 and might keep increasing in the coming years. By 2025, the share of people with a bank account is expected to reach ** percent. Among financial institutions, the Standard Bank Group and the National Bank of Egypt counted the highest number of customers in Africa, each with around ** million clients in 2020.

As of 2024, the National Bank of Egypt and Attijariwafa Bank were the leading banks in North Africa in terms of tier 1 capital. These institutions accumulated tier 1 capital of around seven billion and six billion U.S. dollars, respectively. Moreover, Egypt's Banque Misr registered an approximate value of five billion U.S. dollars, ranking it third in the North African region. Tier 1 capital consists of core capital, reserves, retained earnings, and minority interests.

South Africa holds an outstanding role in the African banking industry. As of 2022, the aggregate tier 1 capital from the major South African banks reached **** billion U.S. dollars. The South African Standard Bank Group had alone a capital worth roughly **** billion U.S. dollars, ranking as the leading bank in the continent. Egypt, Morocco, Nigeria, and Kenya followed in terms of aggregate tier 1 capital, composing the main banking markets in Africa.

As of 2024, the SARB South African central bank ranked as the third largest in the region by Assets Under Management (AUM). The CBL of Libya and the BoA in Algeria ranked in joint first, with each central bank managing 81 billion U.S. dollars in AUM.

As of 2022, Nigeria's Zenith Bank and Access Bank were the leading banks in West Africa in terms of tier 1 capital. Each accumulated tier 1 capital of around **** billion and *** billion U.S. dollars, respectively. Moreover, First Bank of Nigeria registered an approximate value of **** billion U.S. dollars, ranking it third in the West African region. Tier 1 capital consists of core capital, reserves, retained earnings, and minority interests.

This statistic shows a ranking of the Sub-Saharan African markets with the strongest mobile banking penetration rates in 2011. During that time, ** percent of Kenyan adults reported using a mobile phone for money transactions, making the Kenya the country with the strongest mobile banking penetration in Sub-Saharan Africa.

In terms of assets, the National Bank of Egypt and Banque Misr were the leading banks in Northern Africa as of 2024. These institutions registered total assets of around 155 billion and 104 billion U.S. dollars, respectively. Moreover, Morocco's Attijariwafa Bank accumulated assets that reached approximately 67 billion U.S. dollars, ranking it third in the North African region that year.

Standard Bank Group was the leading banking service provider in South Africa in terms of assets. In 2023, the assets owned by the bank were around 169.9 billion U.S. dollars. FirstRand and Absa Bank followed, with assets of around 93.4 billion and 87.1 billion U.S. dollars, respectively.

About this dataset

Context

This list ranks the 11 cities in the Banks County, GA by Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Black Population: This column displays the rank of cities in the Banks County, GA by their Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Black Population: The Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Banks County Hispanic Black Population: This tells us how much of the entire Banks County, GA Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

South Africa Composite Leading Index: sa: 2000=100: Trading Partner Countries: USA data was reported at 100.000 2000=100 in Jan 2013. This records an increase from the previous number of 99.800 2000=100 for Dec 2012. South Africa Composite Leading Index: sa: 2000=100: Trading Partner Countries: USA data is updated monthly, averaging 67.000 2000=100 from Jan 1960 (Median) to Jan 2013, with 637 observations. The data reached an all-time high of 121.300 2000=100 in Jul 2007 and a record low of 35.100 2000=100 in Nov 1960. South Africa Composite Leading Index: sa: 2000=100: Trading Partner Countries: USA data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.S003: Composite Business Cycle Indicators: Seasonally Adjusted. Rebased from 2000=100 to 2010=100 Replacement series ID: 356228902

South Africa Composite Leading Index: sa: 2000=100: Domestic data was reported at 133.270 2000=100 in Feb 2013. This records an increase from the previous number of 133.200 2000=100 for Jan 2013. South Africa Composite Leading Index: sa: 2000=100: Domestic data is updated monthly, averaging 84.990 2000=100 from Jan 1960 (Median) to Feb 2013, with 638 observations. The data reached an all-time high of 133.330 2000=100 in Feb 2011 and a record low of 30.900 2000=100 in Jul 1961. South Africa Composite Leading Index: sa: 2000=100: Domestic data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.S003: Composite Business Cycle Indicators: Seasonally Adjusted. Rebased from 2000=100 to 2010=100 Replacement series ID: 355221502

South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: Others data was reported at 124.800 2010=100 in Jun 2017. This records an increase from the previous number of 124.300 2010=100 for May 2017. South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: Others data is updated monthly, averaging 72.450 2010=100 from Jan 1960 (Median) to Jun 2017, with 690 observations. The data reached an all-time high of 124.800 2010=100 in Jun 2017 and a record low of 33.400 2010=100 in Jan 1960. South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: Others data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.S003: Composite Business Cycle Indicators: Seasonally Adjusted. Rebased from 2010=100 to 2015=100 Replacement series ID: 398018717

In terms of total assets, Togo's Ecobank Transnational Incorporated and Nigeria's Access Bank were the leading banks in West Africa as of 2021. Each institution registered assets of around 25.9 billion and 22.7 billion U.S. dollars, respectively. Moreover, United Bank for Africa in Nigeria accumulated assets that reached approximately 20.2 billion U.S. dollars. This ranked it third in the West African region, and fourth considering its tier 1 capital.

The Middle East and Africa prepaid card market is experiencing robust growth, driven by the increasing adoption of digital payment solutions, financial inclusion initiatives, and the expanding e-commerce sector across the region. A CAGR exceeding 9% indicates a significant market expansion, projected to reach substantial value by 2033. Key segments driving this growth include general-purpose prepaid cards, catering to a broad consumer base, and gift cards, fueled by rising consumer spending and gifting trends. Government initiatives promoting financial inclusion, particularly in underserved communities, are further stimulating market expansion. The open-loop card segment holds considerable potential, allowing users greater flexibility in spending compared to closed-loop options. While the retail sector remains a dominant end-user, corporate and government adoption is steadily increasing, indicating promising growth avenues. Geographical analysis shows varying growth rates across countries, with Saudi Arabia and the UAE potentially leading due to their advanced digital infrastructure and relatively high per capita income. South Africa and other MEA nations also contribute significantly, albeit with varying levels of penetration. The competitive landscape features a mix of international players like Visa and American Express, alongside regional banks such as Alawwal Bank and First Abu Dhabi Bank, creating a dynamic market environment. Challenges include the need to enhance financial literacy among certain segments of the population to fully realize the potential of prepaid cards. Addressing concerns regarding security and fraud remains crucial for sustainable growth. Furthermore, regulatory frameworks and infrastructure limitations in some parts of the region may pose obstacles to widespread adoption. However, the overall growth trajectory remains positive, with a strong potential for continued expansion driven by evolving consumer behavior, technological advancements, and supportive government policies. The market is poised for significant growth in the coming years, presenting ample opportunities for both established players and new entrants. Recent developments include: In September 2022, One of Egypt's leading banks and a fintech company jointly created Telda prepaid cards, which were powered by Mastercard's debut. The ground-breaking payment solution is the result of a fruitful partnership between Telda, a rapidly expanding Egyptian fintech start-up, and Banque du Caire, one of the nation's top financial institutions., In April 2022, The Central Bank of Egypt has given the top fintech and electronic payments startup, OPay Egypt, the initial go-ahead to issue prepaid cards using its OPay app.. Notable trends are: Digital and Mobile Banking is Driving the Market.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

South Africa Composite Leading Index: sa: 2015=100: Trading Partner Countries: Others data was reported at 109.300 2015=100 in Jun 2018. This records an increase from the previous number of 109.000 2015=100 for May 2018. South Africa Composite Leading Index: sa: 2015=100: Trading Partner Countries: Others data is updated monthly, averaging 57.450 2015=100 from Jan 1960 (Median) to Jun 2018, with 702 observations. The data reached an all-time high of 109.300 2015=100 in Jun 2018 and a record low of 28.000 2015=100 in Jan 1960. South Africa Composite Leading Index: sa: 2015=100: Trading Partner Countries: Others data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.S003: Composite Business Cycle Indicators: Seasonally Adjusted.

South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: USA data was reported at 122.900 2010=100 in Jun 2017. This records an increase from the previous number of 122.600 2010=100 for May 2017. South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: USA data is updated monthly, averaging 66.950 2010=100 from Jan 1960 (Median) to Jun 2017, with 690 observations. The data reached an all-time high of 122.900 2010=100 in Jun 2017 and a record low of 32.500 2010=100 in Jan 1960. South Africa Composite Leading Index: sa: 2010=100: Trading Partner Countries: USA data remains active status in CEIC and is reported by South African Reserve Bank. The data is categorized under Global Database’s South Africa – Table ZA.S003: Composite Business Cycle Indicators: Seasonally Adjusted. Rebased from 2010=100 to 2015=100 Replacement series ID: 398018707

The Middle East and Africa (MEA) Banking-as-a-Service (BaaS) market is experiencing robust growth, driven by the region's increasing digitalization and the rising adoption of fintech solutions. The market's expansion is fueled by several key factors. Firstly, the burgeoning mobile penetration and increasing smartphone usage across MEA are creating a large pool of potential BaaS users. Secondly, the region's governments are actively promoting financial inclusion initiatives, which directly contribute to the wider adoption of digital banking services facilitated by BaaS platforms. Thirdly, the cost-effectiveness and scalability offered by BaaS solutions are attracting both large enterprises and SMEs, accelerating market penetration. Furthermore, the growing demand for personalized financial products and services is pushing BaaS providers to innovate and offer more tailored solutions, further stimulating growth. Competition is intensifying with both established financial institutions and new fintech entrants vying for market share. However, challenges remain, including regulatory hurdles, cybersecurity concerns, and the need for robust infrastructure development in certain regions. Despite these, the overall market outlook for MEA BaaS is overwhelmingly positive, with significant potential for expansion in the coming years. The forecast period of 2025-2033 is expected to witness a continued expansion of the MEA BaaS market, propelled by increasing investments in digital infrastructure, supportive government policies, and evolving consumer preferences. The segments showing the most significant potential for growth include API-based BaaS due to its flexibility and integration capabilities, and Digital Banking Services driven by the rising demand for convenient and accessible financial services. Geographically, countries with high smartphone penetration and a young, tech-savvy population, like the UAE, Saudi Arabia, and Egypt, are expected to be key growth drivers. The adoption of BaaS by SMEs will also be a critical contributor to overall market expansion as it allows them to offer competitive financial products without significant upfront investment. However, maintaining customer trust and addressing concerns regarding data security and privacy will be crucial for continued growth and market stability. The market is likely to see further consolidation as larger players acquire smaller firms, leading to a more concentrated yet dynamic landscape. Recent developments include: In March 2022. Aazzur partnered with Treezor which exists as a European firm in Banking-as-a-Service (BaaS). The partnership will increase Aazzur's client base and develop its BaaS offering in the region by supplying its payment infrastructure for account and card management, while Aazur will provide front-end layers, integration, and value-added products for front-end and wealth services., In September 2022, Wio Bank launched its "platform bank" in the UAE, with its offering of apps, embedded finance, and BaaS. The launch of the platform bank is in line with the aim of UAE’s digital economy strategy to double the contribution of the digital economy to the country’s GDP.. Key drivers for this market are: Rise in Digital Banking the Region, Rise in Volume of Financial Transaction in the Region. Potential restraints include: Rise in Digital Banking the Region, Rise in Volume of Financial Transaction in the Region. Notable trends are: Rising Volume of Transaction in the Region.

Abstract

The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.