I’m not sure what to do with this information, but it’s remarkable all the same.

I’m continually disappointed that Moneyweb continues to partner with this distraction.

As I’ve done in the past, I’ve analysed very quickly some of the results of the most recent competition. As background to that, the basic rules are:

1. Put up R20,000 of your own money
2. Trade over three months in currencies, commodities single stock futures and some index trackers.
3. Whoever has the most at the end wins a million rand
4. Everyone keeps what is left of their initial “investment”

So let’s be clear, there are no long-term investment learnings here.

The winner did return 165.5% over 3 months, which is not an impressive performance even though it might look like it.  The point is, given the volatility of the investment universe available for the competition and the encouragement towards rampant risk-taking, it’s entirely pedestrian performance.  It’s very likely an individual’s performance will be good given the wide range of possible outcomes.

Let’s look at some other statistics

These are not performance statistics of which to be proud. They are similar to the losses incurred in prior competitions.

So in short, the competition cost the entrants in total just over a million rand. Losing a million rand is a great way to Make a Million.

Some of the stories around in the news did prompt me to take a look at the South African population pyramids as estimated by Stats SA. Now these figures are extrapolations based on demographic models and the results of the 2001 national census.

As an actuary interested in economic policy I was disappointed with myself that I was surprised by the shape of these pyramids. Some striking messages to say the least.

population pyramid all

The overall population pyramid is relatively typical of developing countries with a significant portion of the population under 15 and a significant majority under 35. The growth rate is clearly already slowing, but a surge of youth, many to poor families with limited prospects for good education is still working its way through our population ranks.

An delegate attending a recent CFA Travelling Conference was muttering something about “the problem with South Africa is that different groups have different growth rates and this causes a problem because some groups already have high unemployment so where will the jobs come from?” Newsflash everyone – more people equates to more consumers as well as more workers. The problem is one of public education that ensures even children born to poor families have great education and gain the skills to be productive. High growth rates make the challenge of ramping up public education to the right level and scale required more difficult, but this is an education issue primarily not a population growth issue.

population pyramid black

I’m including the “black population pyramid” just for completeness.  Given the massive proportion of the population that is black/african, it looks pretty much the same as the overall population pyramid. What is highlighted here, although is also apparent in the total population pyramid, is the sharp decline from the 30-34 band to the 35-39 band. Now, unless there was a sudden spurt of fertility or a sudden decline in infant mortality 40 years ago, this almost certainly reflects HIV/AIDS deaths. Seeing it here as that sharp decline in our most economically productive age group is quite horrifying.

population pyramid indian

The indian/asian pyramid is already in the shrinking stage. As higher incomes and education give rise to lower birth rates, the population shrinks in textbook fashion.

population pyramid white

The white population pyramid is a mix of standard-shrinking (similar to Indian/Asian) and a shocking emigration-bite / brain drain in the 25 to 40 year age bracket. I feel confident saying this is emigration not HIV/AIDS deaths given the low HIV prevalence in the white population.

These emigrated people will generally be the educated, productive, skilled workers (educated with large state subsidies much of the time) and is a big part of the sluggish economic growth in South Africa.

The size and impact of that brain drain has never been as apparent to me as this population pyramid shows.

He continued: “Over the past decade, private hospital costs have increased by 121%, while over the same period, specialist costs have increased by 120%.”

Anyone who measures growth over long periods without using compound annual rates can’t be taken seriously. Abusing numbers for shock value is a sure sign of a weak argument or a lack of appreciation for long-term issues.

121% over nine years (2001 to 2009) equates to an average cumulative annual growth rate of 9.2%. Now medical price inflation of 9.2% is high given inflation over the period and modest real growth in GDP and salaries. But 9.2% tells a very different story to a layperson than 121%. The 9.2% is more useful, more comparable to inflation, more easily able to be understood. 121% is more shocking.

I was really encouraged to read this in a story, quoting Matlala from HASA:

He pointed out that while the green paper said private healthcare costs had increased 121% between 2001 and 2009, this should be contextualised against the backdrop of contributions to public healthcare increasing by more than 100% over the same period.

“Even the price of bread has increased 111% over the decade… We have to face up to the fact that the cost of living has gone up, including healthcare,” Matlala said.

Finally, someone quoted acknowledging that the 121% figure is utterly misleading.

Incidentally, 111% over 9 years is equivalent to an 8.7% annually compounded growth rate, just 0.6% per annum below healthcare cost increases. 

A millionaire’s daughter, Laura Johnson, 19, was remanded in custody when she appeared in court in Bexley near London after being arrested behind the wheel of a car filled with stolen electrical goods and alcohol worth about £5000 (R56385.90).

So the goods were worth about GBP50,000 GBP5,000 (a good round number for an estimate) but worth exactly R56,385.90.  Yes, and 90c.

Accuracy is often important. Precision is sometimes important. Pretending to have either is silly.

I say “in large sectors of the economy” because it isn’t true to say that we have universal unemployment. In fact, a feature of structural unemployment is that it usually is not uniform throughout the economy (like cyclical unemployment often is).  I don’t know any actuaries or engineers who are unemployed for more than a brief period between jobs, and usually the jobs start and end back to back. There will be other examples too.

Unemployment is driven by education

Interesting that 75% of our unemployed are “unskilled”. (I heard this on the radio, so I don’t know that  the number is correct or it’s source, but it does map to my previous analysis based on census showing unemployment by education level attained.

• The unemployment rate for those with less than “matric with university exemption” is between 30% and 40%.
• Matric with university exemption unemployment is 23%
• The unemployment rate for this with better than “matric with university exemption” is on average below 10%.

Supply and Demand for Labour

Economic growth isn’t the only solution to unemployment; in fact it’s not even necessarily a solution.  Prior periods of strong economic growth added jobs only very slowly. We have massive, structural unemployment in this country. We are making some of the right noises with our government’s new jobs plan and jobs fund.

Education in South Africa is not performing as needed

However, given the obvious relationship to education, why don’t we take the problems of our education system seriously?

• Standards have dropped (although enrollment has increased, which is also critically important for improving average education attainment)
• While enrollment has increased, the percentage of GDP spent on education has dropped from 6.4% in 1994/1995 to 5.3% in 2006/2007. This is partly explained by an increase in government spending in total related to social programmes. The real expenditure per learner has increased by approximately 5% per year from 2000 to 2007. We still spend a high proportion of our budget on education, but with poor outcomes.
• We don’t seem to participate in as many international standardised tests as before (reflecting a lack of appreciation for wanting to know what our standards are actually like)
• Standards are too low so that passing isn’t sufficient for further education / job placement
• “Literacy” measures have improved, although this is measured as having completed 7 grades of education. Why is it so hard to appreciate the need to  use measures (or at least also use measures) independent of the system to measure the success of the system?
• The Department of Educations 2009 Macro report uses only 1999 and 2003 data from TIMSS.  It shows our results are very low, but have not decreased by much. We didn’t participate in 2007 (probably because it’s embarrassing coming bottom of the class), which is a shame. 2003 data is really not appropriate to be using in 2011 to evaluate performance of our education system. (It looks like we are at least signed up to participate in 2011, which is great news. We’ll have to wait till December 2012 to get the results though.)
• The Quality Improvement, Development, Support and Upliftment Programme has budget allocated per province to spend on improving education.  While the Free State managed to spend 99% of its budget and the Western Cape 96%, the Eastern Cape spent only 51%, Limpopo 36% and KZN an impressive 11%. This is also home to possibly the worst, most impossible to read, mixed-numbers, mixed-decimal point table I have ever seen.

What does this table even mean? Except that provinces are generally not even spending the budget allocated to improve underperforming schools. How is it even possible that the Eastern Cape has so little budget allocated to this problem when they are one of the biggest culprits when it comes to poor school infrastructure and poor education results?

International indicators of performance

TIMSS 1999 Rock-bottom in Maths and Science. 6 provinces results went backwards in Maths and Science. The DoE’s report indicates that our syllabus has the least overlap with the TIMSS assessment framework – this offered as an excuse for poor performance (which it is) but without even considering that maybe that is a problem in itself. Should our education system really be going about things in a different way to established international norms? Maybe, but I think that needs to be proved and the default position should be international standards unless otherwise proved.

PIRLS 2006 Rock-bottom in reading, although this is against primarily developed countries (and only above Kuwait and Qatar in terms of gender inequality of reading education) (Chapter 1 and full report) (Curiously, other measures including our own Department of Education and SACMEQ studies seem to show girls outperforming boys, rather than the other way around.)

South Africa’s SACMEQ scores didn’t  change significantly from 200 to 2007 – whereas only one peer country had a reading score decline and 2 had mathematics scores decline. and 14 countries’ scores increased across maths and science. The percentage of students not sharing a textbook declined from 45.5% in 2000 to 45.0% in 2007. We are not making progress.

The link between enrollment and attainment levels is clear, but more clearly than that South Africa is massively underperforming.Curiously, the DoE’s report seems to suggest that a reason for our low attainment is high enrollment, whereas the graph above shows that high enrollment is associated with high attainment for those enrolled. My guess at the cause of this strong correlation is that “countries that take education seriously focus on both enrollment and attainment”.

Some South African problems with matric pass rate measurement

The pass rate of matric exams is a function of the quality of education (higher means higher pass rate), standards (higher means lower pass rate) and selection of learners who write the exam (restricting it to only relatively strong students artificially increases the pass rate). We should not celebrate an increase in the pass rate unless we have shown that standards of the exam have not been decreased and that learners weren’t discouraged from writing the exam. I would suggest that the standard of the test is more likely to vary from one year to the next rather than miraculous changes in education quality levels for learners who have had 12 years (or more…) of education to get there.

For example, this graph worries me no end.

As the number of candidates writing the exam goes up, so the pass rate comes down. This reflects progress in keeping learners in school on the one hand, but also likely over-eager promotion of students at lower grades. As the number of candidates reduces (as steps are taken to ensure non-trivial promotion) the pass rate trends back up again. From this graph it's hard to know whether the pass rate tells us anything at all.

I’m not going to show the real/nominal expenditure per capita because the glaring errors in the “2006 real expenditure per capita column” suggests to me that the creator of the table needs to go back to school.

Conclusions?

This post has been more of a collection of observations and thoughts and extracts from other research. In summary:

1. Education is key to unemployment and growth. Unless we recognise that education is the most important long-term solution to structural unemployment we can’t take the problem seriously. Skilled citizens by and large are employed. Unskilled citizens are unemployed at 30% to 40% levels.
2. Government spends significant budget on education, but with poor outcomes. Some budget available is not being spent at all.
3. Measures of literacy and matric pass rates are fundamentally flawed. We need to place increased focus on outcomes compared to international standards if our measures are to be useful.
4. We need to participate more, and more regularly, in international comparatives, no matter how embarrassing the current results are.
5. Current performance against international (and other African country) performance is poor and not improving.
6. The quality of research and ability to recognise the problems in our education system by the Department of Education itself needs to be improved.

In short, the French are less productive than the Americans, they just work less (for good and bad reasons).  This highlights the dangers of using measures and analytics lightly without understanding/allowing for other differences.

Census data isn’t the most useful for understanding marriage and divorce patterns and trends. It’s available too infrequently, relies on self-reported status and only shows a snapshot of the population at a point in time without explicitly showing the change from one state to another.

The great thing about the census data is most of it is freely availably from StatsSA so you can slice and dice it and analyse it exactly how you want.

StatsSA also puts out a Marriages and divorces 2008, with the info taken directly from registrations. I don’t have access to the underlying data, but here are some snippets that can be useful to compare and contrast with popular notions in the media.

December is the most popular month to be married

It’s a huge pity StatsSA doesn’t release the raw data, as their analysis is sadly lacking in the focus on reporting absolute numbers with comparing this against a relevant denominator in a ratio. Knowing how many divorces there are for someone who has been married twice before isn’t very helpful unless we understand how many marriages of that type there are in the first place. Knowing how many divorces involved children doesn’t help really unless we know how many marriages at that point in time were in a family with children. This tells us nothing about the relative likelihood of divorcing with or without kids.

I’ve tried to piece together some of this by comparing this transition data with the snapshot data from the census, but I have concerns that inconsistencies between the data sets may be skewing the results enough to limit their usefulness.

I’ve left out civil unions for no reason other than there is too late to analyse reliably.  There were only 80 in 2007, growing significantly to 732 in 2008, but still too few to draw conclusions on.

About Marriage

Unsurprisingly, bride and groom are older when marrying for the second time

1. 80% of bridegrooms are older than their brides when the brides and spinsters of widows. Nearly half of bridegrooms are younger than their brides then the brides are divorcees. Typically around 85% of widowers marry younger women, and this drops to only  just below 80% when the bride is a divorcee. A similar pattern occurs when the bridegroom is a divorcee.
2. The ages of both bride and bridegroom for customary marriages are 2 to 3 years younger than marriages overall.
3. There were approximately 180,000 marriages registered in 2007, when the national census estimated their to be 20.9m unmarried people between the ages of 16 and 80 (a crude assessment of most common marrying ages. Ideally we should look at rate of marriage per age band since this will definitely not be the same across all ages). Given that two people are needed to get married, this gives us 180,000 marriages out of 10.45m pairs (ignoring male:female ratios differences) which is an average marriage rate per year of 1.7%.
4. The number or marriages has been increasing almost every year from 140,000 in 1999 to 186,000 in 2008, which translates to a growth rate of around 3% per annum while our population growth over the same period is estimated by StatsSA to be a little more than 1%. From the graph below you can see a marked jump from 2001 to 2002. The growth rate excluding that jump is more like 0.9%, slightly below the average population growth rate.

What happened in 2002 that suddenly made everyone want to get married? World didn't end after all? Or some processing/data error. Maybe you should check that you are actually married if you were married in the last years of the previous millennium.

About Divorce

1. There were about 30,000 divorces in 2007, the same year the national census put the total number of married people as 10.7m. This translates to a divorce rate of just under 0.3% (or 3 divorces per 1000 marriage) per annum. That puts us about the same as the UK. Certainly makes me question the common wisdom that 1 in 2 marriages end in divorce. Using these figures suggests to me less than 1 in 3, maybe as low as 1.5, depending on whether the age of first marriage keeps increasing and what happens to life expectancy.
2. The number of divorces per year has been declining more or less steadily, from 37,000 in 1999 to just under 29,000 in 2008.
3. The StatsSA report doesn’t show number of marriages by population group, but the census shows 2.1m married white citizens and  6.8m married black citizens. The number of divorces is about equal in 2008, having declined from 14,785 in 1999 to 9,481 in 2008 for the white population but having significantly increased from 6,823 in 1999 to 10,110 in 2008 for the black population. Clearly very different population dynamics at work here, or serious problems with at least one of these data sets.

So what is really happening with marriage and divorce in South Africa?

These results confuse me. Yes, there is a slight increase in age at marriage, but divorce seems to becoming less popular and with mixed messages about marriage depending on what the 2001 jump is all about.

Some interesting descriptive graphs. First, let’s look at the total population. (Note this is exactly from the 2007 census, with population group, age and marital status as reported. I’ve excluded high ages as random error given the small population size makes the data uninteresting.)

The light red at the top is the divorced population, the darker red above married (civil/religious) is traditional marriage.

It’s hard to tell understand all the dynamics within the total population chart since the patterns are quite distinct within different population groups. These differences will probably soften over time, but for now they are still quite marked. 80% of the SA population is black, so let’s look at that first.

The first thing I noticed is the curious discontinuities at age 51 (suddenly fewer civil/religious married, more traditional, fewer living as if married, and a big spike in widows/widowers). Hard to paint a particular scenario where this makes sense, so this might reflect reporting bias for age around 50, but even that is hard to turn into a sensible story.

Then at age 60, there is a sudden jump in widows/widowers at the expense of civil/religious, traditional marriages and living together as married. There’s a similar spike at age 66. After this, the variability can more easily be explained by random error as the surviving population size decreases.

The proportion of the black population that is divorced peaks at just under 4% at age 51. Reported polygamous marriages are never more than 0.3% of the population except for a 0.5% at age 92 and some of 0.7% and 1.2% at very high ages (not shown on graph). It’s hard to tell whether this is purely sampling error or a function of traditional marriages being more prevalent in the past and the legacy of this is only visible with very old married couples who would have been married 50 to 80 years ago.

The white population graph shows quite different characteristics compared to the black population. Divorce prevalence is reaches nearly to 9% in the early to mid 50s. Traditional marriages are far less prevalent, and widows/widowers feature much later (as expected due to lower average mortality).

By age 32, 72% of the white population is married or living together as if married (compared to just 43% of the black population, 54% for coloured and 75% for the asian/indian population.

Due to the much smaller sample size, random variation is apparent at most ages.

The coloured population reflects the low traditional marriage of the white population, similar divorce rates to the white population, but with the timing of marriage and mortality rates more similar to the black population.

The asian/indian population is the smallest group in South Africa. This shows with the increased random variation between ages.

This population can be characterised by high and early levels of marriage, with on average 1 in 8 marriages being traditional and mortality similar to the white population.