Shocking unemployment statistics (but what do they mean?)
Someone threw a shocking figure at me today. They said that the unemployed under age 35 in South Africa comprise 75% of the unemployed population. I believe the figure comes from Adcorp.
Like many statistics that are bandied around for shock value, this one is more fluff than substance. Here’s why.
The real view of the numbers
Here’s the data from the 2007 community service.
2007 Community Survey Results | |||||
Age | Population | Unemployed | Unemployment rate | % of population | % of unemployed |
15-35 | 18,323,677 | 4,310,474 | 24% | 59% | 71% |
36-65 | 12,712,484 | 1,737,827 | 14% | 41% | 29% |
total | 31,036,161 | 6,048,301 | 19% | 100% | 100% |
2007 Community Survey Results | |||||
Age | Population | Unemployed | Unemployment rate | % of population | % of unemployed |
15-35 | 18,323,677 | 4,310,474 | 24% | 59% | 71% |
36-65 | 12,712,484 | 1,737,827 | 14% | 41% | 29% |
total | 31,036,161 | 6,048,301 | 19% | 100% | 100% |
2001 Census Results | |||||
Age | Population | Unemployed | Unemployment rate | % of population | % of unemployed |
15-35 | 17,167,552 | 4,606,165 | 27% | 67% | 60% |
36-65 | 11,259,573 | 2,217,904 | 20% | 33% | 40% |
total | 28,427,125 | 6,824,069 | 24% | 100% | 100% |
Two lessons from misleading statistics
- Unemployment is a serious problem in South Africa, and youth unemployment is more serious that more experienced worker unemployment. Specific plans to ensure appropriate skills are available and incentives exist to hire young workers in order to provide them with the invaluable experience that makes them more efficient and attractive as a factor of production should be a focus of economic and public policy.
- Shocking statistics need careful interpretation to ensure they paint an accurate, objective and reliable picture. If we are to set public policy based on hard evidence (as I believe we should) let’s make it is hard evidence and not the ideologically tainted headline grabbing statements so often prepared instead.
Some graphical insights
Who stops working?
Before we can consider gender differences in unemployment and economic status, it’s important to understand the different life expectancy of men and women:

More boys are born than girls. This graph shows that by age 15, there are still slightly more boys than girls because of this. This ratio stays more or less constant until around age 30, where it seems the ratio of men to women actually increases. This may reflect fatal complications of pregnancy and child-birth (I’m not sure). The slight decrease between ages 18 and 25 reflects the “accident hump” in mortality where due to risky, testosterone-infused and culturally enforced behaviours, more young men die than eminently more sensible young women.
However, soon after age 30, the ratio plummets as men die with significantly higher probability than for women. This, incidentally, is the reason annuities are more expensive for women to buy than men – women live longer in retirement years.
These differences in mortality and therefore life expectancy have an impact on employment. The same pregnancy and child-birth stage that potentially gave rise to a temporarily increased mortality rate for women also has an effect on employment, since some women who have the opportunity to cease working (temporarily or permanently) to raise a family do so.

The above, rather busy, graph highlights a few interesting points:
- More females are unemployed than males at almost all ages. (Unemployment means “actively seeking work but unable to find it” and therefore does not include those women who choose not to work.) Women are still discriminated against in terms of employment. Whether some of this is fair (due to a very few jobs where physical strength is a prerequisite ) is up for debate, but it’s clear the problem is larger than be explained by reasonable means.
- From the early 20s, fewer women are economically active (employed or unemployed). This remains at a fairly steady 15% extra of women are not economically active compared with men at ages above 30. This reflects a mix of family responsibilities and cultural / societal expectations for women not to work.
- The rate of departure from the workforce increases far earlier for women than men, with an accelerating rate starting from around age 32 for women and only 43 or so for men.
So, the decrease in unemployment overall as age increases may initially be due to experience making them more employable, but rapidly becomes a factor of women leaving the work-force initially, then followed by men a decade or so later.
It’s hard to know from this data why so many men (and women) leave the workforce in their 40s. It would seem unlikely that a significant portion of this is due to “early retirement” on sufficient savings to relax.
Are children providing for parents even at this age? Are these frustrated workers who, on being retrenched, eventually give up looking for work and become “not economically active” and dependent on grants? What does this mean for our tax burden and government expenditure on social welfare?
I’ll continue to analyse this data over time, but I think that’s more than enough for one post. Isn’t it incredible what insights and data are available when the underlying info is shared comprehensively rather than popping sounds-bites?
On the point of how statistics can create inaccurate perceptions, take a look at this nerdy TED talk: http://www.ted.com/talks/lang/eng/peter_donnelly_shows_how_stats_fool_juries.html
I think whenever Bayesian probabilities are used, one should be careful not to assume that the reader has sufficient prior knowledge to interpret the stats correctly.