Two of Statistics SA's most complex outputs were published earlier this week: the gross domestic product (GDP) and mid-year population estimates.
Neither of these is the result of a single survey. Monthly data on, for example, retail or wholesale trade or manufacturing are based on single monthly sample surveys.
Estimation of the GDP and mid-year population, on the other hand, involve integration of a range of data sets and sophisticated statistical processes.
Mid-year population estimates are compiled in the years between each population census. According to this week's release, South Africa's population at the middle of 2005 is estimated at 46.9 million.
Africans are in the majority (about 37.2 million) and constitute about 79 percent of the population. The white population is estimated at 4.4 million, the coloured population 4.1 million and the Asian population 1.1 million.
KwaZulu-Natal has the largest share of the population (20.6 percent), followed by Gauteng (19.2 percent) and Eastern Cape (15 percent). Northern Cape has the smallest share (1.9 percent).
The inputs for the cohort-component method of estimation used in the estimates are derived from detailed substantive analyses of trends in fertility, mortality and migration.
This requires an intensive analysis of the available data and its quality. This approach adjusts general reported fertility to age-specific fertility rates, which, in turn, are used as input for estimating the average annual number of births.
In another derivation, the estimation of mortality and additional deaths due to HIV/Aids requires multiple iterations, as controls for the adjustment of sero-prevalence data are needed to make the data applicable to the whole population.
It is often difficult to make plausible migration assumptions due to inadequate data. Mid-year estimates for 2005 incorporate assumptions using published and adjusted migration data from Stats SA and other sources. Internal migration patterns show a shift to three main areas.
KwaZulu-Natal, Western Cape and Gauteng have positive net migration, with the largest number of persons expected to migrate into Gauteng (about 520 000) for the period 2001 to 2006.
Eastern Cape and Limpopo are expected to have the largest negative net migration, with Eastern Cape expected to experience negative net migration of about 320 000 for the same period.
In a projection, the size and composition of the future population is estimated. Although there are crude estimation methods, such as inflating subpopulations at one date by an assumed overall mean annualised growth rate, most serious estimation efforts use a cohort-component approach.
In the latest estimations, the overall HIV-prevalence rate is about 10 percent (an HIV-positive population of 4.5 million). However, this differs for various age groups: the prevalence rate for adults aged 15 to 49, for example, is estimated at 17 percent.
In the mid-year estimates, an Aids impact model has been used.
The age and sex distribution of those infected with HIV.
Similarly, estimation of GDP involves integration of multiple data sources, and application of complex statistical models and methodologies.
In summary, this week's release reported that real GDP at market prices increased by 3.5 percent during the first quarter compared with the fourth quarter of 2004. The real annualised economic growth rates during the four quarters of 2004 were 3.8 percent, 4.4 percent, 5.7 percent and 4 percent respectively.
This estimation involved integration of a number of data sources for each industrial sector. Different data sources were used, depending on whether an annual or benchmark estimate, a regional estimate, or a quarterly estimate was required.
For example, in estimating the contribution of electricity, water and gas to GDP, results from the annual economic activity survey from Stats SA, annual reports from Eskom and water boards, and local government statistics were used for annual estimates; data from local authorities and Eskom was used in compiling regional estimates; while quarterly estimates were based on relevant producer price indices, and Stats SA's monthly release on electricity generation and consumption.
These data sources were integrated and interrogated to estimate contribution to GDP by just one of the 10 standard industrial sectors.
In addition to these, Stats SA has in the past week released data in a number of other series, which have direct impact on the GDP: consumer and production price indices, electricity available for distribution and statistics on tourist accommodation.