Category Archives: statistics

More on Marriage Data

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Divorce is becoming less popular, the world didn’t end at the end of the millennium and StatsSA makes it hard for us to draw conclusions.

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.

Month of Marriage

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. Continue reading →

Know your (marital) status

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I’ve been exploring the 2007 Census data for South Africa, mostly just to see what is available. I was checking the reasonability of some marital status assumptions for pensions valuations, which got me looking at the marital status info from the Census.

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. Continue reading →

How not to lose money in Make a Million

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I have a clear strategy for how not to lose money playing the Make a Million competition. As I explain it, you may come up with some smart tactics to win the competition and enhance your returns, but you’re on you’re own there.

So, how does one not lose money with the Make a Million competition?

Don’t enter.


You are overwhelmingly like to lose money if you enter this competition. I’ve said this before, and I’ve been right before. I’m right again.

There’s also the little idea that the  structure of the Make a Million competition increases risks of  financial meltdown

Let’s look at some hard statistics to show what I mean.

Telling statistics (what they don’t show)

In the MaM presentation, the organisers include some interesting statistics about number of trades, trading activity and many other metrics.

They don’t show average returns or performance.

So let’s look at some of the numbers:

Raw return data (excluding prize money) based on 2009 MaM competition.

Average Return -11.49%
Expected Loss R 1,149
Median Return -15.06%
Mode Return -9.12%
Probability of breaking even 25.00%
Probability of earning less than 10% 83.00%
Probability of doubling money 1.78%
Probability of winning 0.20%

Suddenly the competition doesn’t look so great, does it?  (This isn’t the first time, here is my analysis of the Comedy and Tragedy that was the 2008 Make a Million competition.) Continue reading →

Interactive house price data (including South Africa)

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The Economist has a brilliant interactive chart showing nominal house price growth across a range of countries, including South Africa.

It’s clear, as we already know, that South African house price increases have been dramatic. Somehow though, seeing it on a graph with a range of other countries brings it into sharper focus. Our property market has been manic.

The Power of Misconceptions

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In broad terms, we are all mostly ignorant. Worse than ignorant, we have notions and views, firmly held, that are entirely incorrect.

We only complain about what we don’t like

Nobody complains to their boss that they are overpaid. Nobody complains that their pension or social security increases were above inflation last year.

We don’t understand the size of countries and continents

Africa is huge. More of an issue for Europeans and Americans, but the problematic views of the size of Africa due to mapping “projections” used to represent an almost-sphere onto a flat map are almost universally held.

A map showing the real size of Africa as measured in square meters of surface area compared to other countries

The Real Size of Africa

We don’t understand our economy (or at least US students surveyed don’t understand their economy)

Bill Goffe surveyed his students [pdf] with worrying results:

  1. Students assumed 35% of workers earn minimum wage compared to the 2007 actual statistic of 2.7%
  2. Students thought recent US inflation was around 11%, when the real answer is basically 0. (and google is apparently planning some sort of price index of their own)

Plenty more where those came from.

Then a few previous posts of mine highlighting these problems

Estimating the ERP is hard, but the range of common flaws is astounding.

We like to complain about electricity prices, when we haven’t figured out that we pay for it all anyway.

Unemployment, mystified

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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%
The first thing to note is that overall, the unemployment rate is exceptionally high – this post doesn’t refute that. High, structural unemployment is possibly the most serious problem our economy faces. Continue reading →

Property investment – the value of data over opinions

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Lightstone have a trick up their sleeves. Their raison d’être is collecting, analysing, understanding and packaging data for themselves and others to use to understand past, current and future property valuations.

Their housing price index is more robust (and more independent) than those of the banks based off their own data and target markets. Rather than consider only the average price of houses sold in that particular month (which is a function of house price growth / decline but also how the type, condition, size and location of the houses sold that month differ from the prior month and year) they consider repeat sales where the same property has been bought and sold more than once.

This data is combined or “chain-linked” to provide a continuous measure of house price inflation over time.

House Price Inflation 2010

House Price Inflation 2010 source: lightstone.co.za

The result of all of this data, best-in-class methodology and analysis? When Lightstone says “opportunities abound in local market” I actually listen. Since their business model is to sell information, I’m more likely to trust what they say.

More on cars and colour

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In researching my previous post on accurately measuring the risks associated with vehicle crimes based  on colour, I stumbled across another colour related risk measure.

Red cars, supposedly, attract more than their fair share of traffic fines.

Turns out this is incorrect.  Snopes.com has (as usual) an excellent article on red cars, including references to research showing red cars are not more likely to be fined than other vehicles. Unfortunately, the underlying research isn’t available online (as far as I could find).