Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration.
The results are used to ensure:
• equity in distribution of government services
• distributing and allocating government funds among various regions and districts for education and health services
• delineating electoral districts at national and local levels, and
• measuring the impact of industrial development, to name a few
The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.
Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included:
- To provide statistics on population, demographic, social, economic and housing characteristics;
- To provide a base for the selection of a new sampling frame;
- To provide data at lowest geographical level; and
- To provide a primary base for the mid-year projections.
Kind of data
Census/enumeration data [cen]
Unit of analysis
Producers and sponsors
Statistics South Africa
Dates of collection
Mode of data collection
About the Questionnaire :
Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.
The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses:
a) The needs of a broad range of data users in the country;
b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis;
c) The probable willingness and ability of the public to give adequate information on the topics; and
d) The total national resources available for conducting a census.
In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.
Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.
1. The Household Questionnaire is divided into the following sections:
- Household identification particulars
- Individual particulars
Section A: Demographics
Section B: Migration
Section C: General Health and Functioning
Section D: Parental Survival and Income
Section E: Education
Section F: Employment
Section G: Fertility (Women 12-50 Years Listed)
Section H: Housing, Household Goods and Services and Agricultural Activities
Section I: Mortality in the Last 12 Months
The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga
2. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:
- Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.
3. The Questionnaire for Institutions (English) is divided into the following sections:
- Particulars of the institution
- Availability of piped water for the institution
- Main source of water for domestic use
- Main type of toilet facility
- Type of energy/fuel used for cooking, heating and lighting at the institution
- Disposal of refuse or rubbish
- Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)
- List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)
4. The Post Enumeration Survey Questionnaire (English)
These questionnaires are provided as external resources.
Statistics South Africa
Data editing and validation system
The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).
The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.
The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.
The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases
substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following:
0 no imputation was performed; raw data were preserved
1 Logical editing was performed, raw data were blank
2 logical editing was performed, raw data were not blank
3 hot-deck imputation was performed, raw data were blank
4 hot-deck imputation was performed, raw data were not blank
The processing of over 15 million questionnaires commenced in January 2012, immediately after the completion of the reverse logistics in December 2011. Each box and its contents were assigned a store location in the processing centre via a store management system. Each time a box was required for any process it was called through this system. The processing phase was sub-divided in the following processes: primary preparation - where all completed questionnaires were grouped into clusters of 25 and the spine of the questionnaire cut off. Secondary preparation - where questionnaires were finally prepared for scanning, by removing foreign materials in between pages and ensure that all pages are loose. Scanning - questionnaires were put through a scanner to create an electronic image. Finally Tilling and completion - where any unrecognized reading/ badly-read image by the scanner had to be verified by a data capturer. This process took 8 months. Over 2 000 data processors working 3 shifts per day were employed for this phase to ensure that 225 million single pages are accounted for.
Other forms of data appraisal
Independent monitoring and evaluation of Census field activities
Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring and Evaluation Monitors from Monitoring and Evaluation division. These included field training, publicity, listing and enumeration. This was to make sure that the activities were implemented according to the plans and have independent reports on the same. They also conducted Census 2011 and the Post Enumeration Survey (PES) Verification studies to identify the out-of-scope cases within Census (a sample of 7 220 EAs) and the PES sample (600 EAs) as reported in the Census 2011 PES EA Summary Books.
Post-enumeration survey (PES)
A post-enumeration survey (PES) is an independent sample survey that is conducted immediately after the completion of Census enumeration in order to evaluate the coverage and content errors of the Census. The PES for Census 2011 was undertaken shortly after the completion of Census enumeration, from November to
December 2011, in approximately 600 enumeration areas (EAs) (which later increased to 608 due to subdivision of large EAs). The main goal of the PES was to collect high quality data that would be compared with Census data in order to determine how many people were missed in the Census and how many were counted more than once. A population Census is a massive exercise, and while every effort is made to collect information on all individuals in the country, including the implementation of quality assurance measures, it is inevitable that some people will be missed and some will be counted more than once. A PES assists in identifying the following types of errors:
• Coverage error: this includes both erroneous omissions (e.g. a household that was not enumerated) and erroneous inclusions (e.g. a household that moved into the enumeration area (EA) after Census but was still enumerated, or a household that was enumerated more than once).
• Content error: this refers to the errors on the reported characteristics of the people or households enumerated during Census.The errors may emanate from the following reasons:
• Failure to account for all inhabited areas in the EA frame;
• EA boundary problems;
• Incomplete listing of structures and failure to identify all dwellings within an EA;
• Failure to enumerate/visit all listed dwellings within an EA;
• Failure to identify all households within a dwelling unit in instances whereby a dwelling unit has more than one household;
• Failure to enumerate households (complete questionnaires) for all households due to refusals, unreturned questionnaires for self-enumeration, inability to contact households, etc);
• Failure to include all individuals within households;
• Failure to observe the inclusion rule based on a person’s presence on Census night (i.e. failure to apply the de facto rule accurately); and
• Lost questionnaires or damaged questionnaires that could not be processed.
Usually more people are missed during a Census, so the Census count of the population is lower than the true population. This difference is called net undercount. Rates of net undercount can vary significantly for different population groups depending on factors such as sex, age and geographic location. Stats SA obtains estimates of the net undercount, including the type and extent of content errors (reported characteristics of persons and households enumerated in the Census) using information collected through the PES.
- Sections 220.127.116.11 of the report provides more information on the preparation, methodology, sampling and implementation of the PES.
Statistics South Africa
Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source of the data; that it is specified that the application and/or analysis is the result of the user's independent processing of the data; and that neither the basic data nor any reprocessed version or application thereof may be sold or offered for sale in any form whatsoever without prior permission from Stats SA.
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.