The relevance of racial identity or population group in statistical collection is a much-debated issue in South Africa and elsewhere.
Some identifier of population group is, at least for a time, probably required to measure and monitor progress in redressing the inequalities of South Africa's past. However, population group or "race" as a variable in statistical collection and analysis is inadequate on its own.
This is why Statistics SA incorporates other factors in the derivation of social, economic and development indicators.
In a personal communication, PG MarÉ, a professor of sociology at the University of KwaZulu-Natal, has raised important questions about the use of race as a category in monitoring change.
MarÉ, who is exploring the setting up a centre to study "race thinking", has pointed out that the "self-classification" of population group implemented after the legal basis for apartheid race classification was repealed is often problematic.
Personnel at institutions as diverse as hospitals recording births and universities recording admissions, as well as equity and line managers in companies, often "classify" individuals on the basis of surnames, language spoken, appearance, accent, place of residence and the like.
This is not "self-classification", and is often based on the prejudices of the classifier, thereby tending to perpetuate these prejudices.
MarÉ also wonders whether racial classification or categorisation provides the best tool to measure progress. He suggests that an instrument that also addresses what is being redressed, and in relation to which social group, would reveal more about society.
He draws attention to the levels of imprecision in race-based identification and the social costs of maintaining a race identifier as the main basis for measuring change.
These important points notwithstanding, it remains so that the majority of South Africa's population, and by far the majority of those most disadvantaged by apartheid, readily identify themselves as "black" or "African".
The task for statisticians, then, is to link this identification to other variables, so that measuring, monitoring and analysis can be enhanced.
Stats SA's recently published labour force survey (LFS), undertaken in September 2004, provides some interesting examples of how this can be done.
At the highest level, the LFS reports an official national unemployment rate of 26.2 percent. However, this global figure becomes more meaningful as additional variables or identifiers are attached to it.
Attach, for example, sex as a variable, and the LFS reports that the official national unemployment rate for men is 23.1 percent, and for women 30.2 percent.
Add the variable of province, and one finds the highest unemployment rate for men in the Eastern Cape, at 27.7 percent, and the lowest in the Western Cape (17 percent); for women, the highest rate of unemployment is in Free State (33.6 percent) and the lowest in the Western Cape (20.5 percent).
Significant divergences from the national figure of 26.2 percent are found when one race or population group is attached to the national profile of unemployment. The unemployment rate for black Africans is 31.3 percent, coloureds 21.8 percent, Indians 13.4 percent and whites 5.4 percent.
Interestingly, between March and September 2004, unemployment among black Africans decreased (by 2.9 percentage points), as it did among Indians (by 3.1 percentage points). In the same period, unemployment among coloureds increased (by 3.8 percentage points), and there was a marginal increase in unemployment among whites (0.3 of a percentage point).
Attaching age group and gender to official unemployment by population group, the LFS reports that, in most 10-year age groups, more women are unemployed than men.
However, more African men between the ages of 45 and 54 are unemployed than African women in the same age group; more coloured men are unemployed than coloured women in the age group 45 to 54; and more white men are unemployed than white women in the age group 15 to 24.
An increasing number of variables can be attached to the national profile of unemployment to obtain a more nuanced picture.
Population group is one of these. So are geography (province, for example), sex, age group, education level, type of housing, access to services, length of time unemployed and many others.
This allows for the development of a complex approach to monitoring and measurement in which a self-defined racial identity - with all its imprecisions and ambiguities - is present as a variable, but enhanced by the addition of a range of other social, economic and demographic variables.
Pali Lehohla is South Africa's statistician-general and head of Stats SA. For more information on Stats SA and its statistical outputs, visit www.statssa.gov.