It’s been 25 years since democracy dawned in SA. But apartheid’s legacies still scar the country. Poverty remains high; inequality remains extreme — and both follow racial lines. The same is true for unemployment. By conservative official standards, it stands at a staggering 27,6%, and it disproportionately affects black South Africans.

Effective policies to combat SA’s plight require good ideas, resources and political will. They also, however, need to build on a solid understanding of the problem in the first place. How can defects in the labour market be tackled if we don’t know what’s actually going on there? Good and relevant statistics are central to effective policies.

Unemployed people queue for UIF payments. Picture: SUNDAY TIMES
Unemployed people queue for UIF payments. Picture: SUNDAY TIMES

Unfortunately, there are limits to how well SA’s statistics can capture the situation in the country.

The international gold standard for employment figures comes from the International Labour Organisation in Geneva. According to its template, you count as unemployed if you (a) don’t work (a lot) for money, (b) are available for work, and (c) are actively looking for a job.

This definition originated in the 1920s. It was clearly built on the image of white, male factory workers in Europe and North America. If these men lost their jobs, that spelt social and political trouble. Unemployment statistics functioned as a thermometer for how well such factory economies hummed along.

Immediately after apartheid ended, SA’s statisticians tried to move away from the traditional definition of unemployment. For instance, they experimented with various more extended definitions of unemployment that would capture what they now call the “discouraged work seekers”.

But, as our recent research found, the country’s appetite for statistical creativity has slowly waned. It has increasingly embraced the ill-fitting international standard. But this global standard risks creating a skewed image of SA’s labour market woes. Complacency and poorly crafted policies may be the result.

Unique local context

There are a number of problems with sticking to narrow, global definitions of “unemployment”, “job searching” or “industry” in the SA context.

For instance, the spatial legacies of apartheid mean that for many South Africans, industrial jobs are hundreds of kilometres from where they live. Many people don’t actively look for jobs because they know all too well there are none to be found anywhere near. For all intents and purposes, they are unemployed, and in a particularly hopeless situation. But they would not pass the official International Labour Organisation definition — and so they drop out of the stats.

In rural areas, many people — and women in particular — labour on the land to grow their own food. They might greatly prefer a salaried job to escape poverty. But their hand-to-mouth existence may prohibit the luxury of an extensive “job search”. They, too, would fail the statistical litmus test.

Simply put, international unemployment standards would ignore a big part of SA’s labour market problems. This would do an injustice to women and the black population, in particular.

The other problem with existing definitions is that nuance tends to fall by the wayside. Take “discouraged work seeker”. It’s an ambiguous category. Whether you qualify depends on your own judgment: maybe you don’t look for a job actively, but would you like one? Not everyone answering “no” is voluntarily jobless. And people might answer “yes” even though they indeed lack personal effort to find work.

Such vagaries are at odds, however, with the exactness that’s expected from statistics. Statistics privilege easy-to-quantify indicators, and important nuances disappear.

Bold creativity needed

This tendency to ignore soft, hard-to-measure issues is amplified by politicians who happily pounce on statistics that displease them.

Stats SA has over the years often been criticised — unfairly, we think — for unemployment figures that tried to paint a more realistic picture. Yet less than rock-solid statistics have invited the charge that Stats SA would inflate the numbers intentionally, or otherwise manipulate them.

Once Stats SA narrowed the definition, criticism came from the other side — in this case the political opposition. To safeguard its credibility, the agency had little other option than to seek refuge in an international definition that would withstand local criticism, even if it fails to do local circumstances justice.

SA faces a genuine dilemma in its statistics. It is impossible to capture the complex ills of its economy and society in single headline figures. Such numbers, and the colourful graphs they generate, ready to be tweeted, are tempting. But all too often, embracing this strategy biases the numbers against people who fall between the cracks.

Our research suggests that the country’s statisticians should be bold and paint a picture that does justice to the colourful quilt that is the “rainbow nation”. And politicians have a duty to respect these efforts if they are genuinely committed to effective policies.

When the new SA was born in the mid-1990s, it was carried by a spirit of daring and breaking the mould. May that spirit — even in a field as boring as statistics — prevail in the future, as well.

• Mügge is a professor of political arithmetic at the University of Amsterdam and Alenda-Demoutiez is a postdoctoral researcher at the department of political science at the University of Amsterdam

This article was first published by The Conversation.