On average, it takes a person 11 minutes to walk one kilometre. On average, a Maltese person lives 82.6 years life, earns €18,660 per annum, spends 13.3 nights on foreign holidays, spends €24 a week in restaurants, and has a 27% chance of dying of cancer. But who is Mr Average? I have yet to come across somebody who satisfies these six criteria of averageness.
The truth is that neither you nor I are an average. If I were to sit in a room with Jeff Bezos the ‘average’ net worth of that room would be $187.1 billion. Do I have $93.5 million? No.
Asking questions like, “What does the average person look like?” or “How much does the average person weigh?” make sense. A population’s looks and weight are normally distributed – so your average is indeed a representative value. Roughly half the people are below average, half above. This is because the population is a normal distribution. This is characterised by a fat middle and tapering edges. So the average gives you a reasonable metric to build around, but still, the average person isn’t anyone in particular.
So where was this concept of average or mean born? It began with Astronomy. Every astronomer wanted to figure out the distance between planets and the sun. Everyone tried their own methods. In the end, they had dozens of measurements. How to figure out which one is correct? How could they minimise error? The answer was taking the mean of all the values. They believed that positive errors would balance out the negative errors. They were thinking in terms of the normal distribution. And indeed, the normal distribution makes sense here.
Soon thereafter came the second use case for averages: estimation. In Matthew 14: 13-21, we are told that Christ fed 5,000 men. Are we supposed to believe that Matthew had a clicker and actually counted them one by one? Of course not. Standing across the field as evening was falling, and with people’s stomachs grumbling, he wouldn’t have wanted to spend the time counting the crowd. He estimated their numbers instead. How? Using the average – the representative value. He must have counted the men in a small group, multiplied by the number of groups, and he had the count.
Our brains still think in terms of the normal distribution. But normal distributions aren’t the norm. Enter the power law distribution territory, where averages stop making sense. Here, the data is skewed to one side, say a situation where 80% of people are below the average. This may sound ridiculous, but that’s where what we see, and what we think it means, diverge.
The average is calculated by adding up all of the individual values and dividing this total by the number of observations. The median is calculated by taking the “middle” value, the value for which half of the observations are larger and half are smaller. But the more skewed the distribution, the greater the difference between the median and mean, and the greater the emphasis that should be placed on using the median as opposed to the mean.
Indeed, that’s why the 80/20 principle was so revolutionary. What is it? Called the Pareto Principle, or the 80/20 rule, this states that for many phenomena 80% of the result comes from 20% of the effort. The principle was named after Vilfredo Pareto ─ an Italian economist ─ who, back in 1895, noticed that about 80% of Italy’s land belonged to 20% of the country’s population.
Let’s see how this applies to a very important statistic. Take the average household disposable income. In 2020 it was €31,266. If income were a normal distribution among Maltese households, it would mean that hundreds of thousands of Maltese would tend to be earning that amount. Wrong.
We know that, taking into account the size and composition of a Maltese family, the median national equivalised income was €16,240. This already makes you suspect that the mean does not provide the best central location for the data because the skewed data is dragging it away from the typical value. It is skewed by some large salaries at one end and some pretty low ones at the other end. The distribution looks nothing like the bell in the chart.
So averages are misleading when used to compare different groups or when there are numerous outliers in the data. In fact, we know that the ratio of total income received by the 20% of the population with the highest income to that received by the 20% of the population with the lowest income (the 80/20 ratio), was 4.67. That means that the top 20% were earning 4.67 times as much as the bottom 20% or in real Euros a gap between €36,772 at the higher end and €9,156 at the lower end.
The truth is that the average income is crap. It’s just a number that you can nationally feel good about, while it’s actually hoarded, untaxed, and often not even in the country. Do we want to judge our economic system by a result where a few thousand people grab almost five times as much as the other hundreds of thousands? Fail.
And yet all of this crap makes GDP go up. Such is the tyranny of averages. Your wealth and the wealth of the man picking your pocket by not paying his taxes and VAT are combined, along with the wealth of the company ruining your environment, and hiding it all in some offshore double-blind.
Then politicians can spit the laundered figure back at you, while you’re literally scrounging a living. Then they express surprise why the population is dissatisfied, and think a bit of positive PR will fix it. On most occasions, they will find a more positive statistic in the mass of data available and exploit it as if it should make the poor sods earning less than €6,496 happier.
In this kind of situation, the poor sods concerned become stereotypes. We say that they are people at risk of poverty. Nor poor, but “at risk of being poor”, as if a household of 2.3 people on average (there we go again) could possibly live a decent life on €17 a day. Just to give you an idea, this whole household’s daily consumption might consist of two cans of beans, two loaves of bread, five eggs, a carton of milk, five slices of bacon, 300 grams of chicken, 50 grams of cheddar, 200 grams of tomatoes and some butter, on top of which it might pay €2.50 each day for its electricity and rent.
Mr Average has probably never actually met Mr Poor, so it is quite easy to say things about the stereotype, such as that the poor man in question is probably a lout who spends his day drinking beer rather than working, or that the poor woman is the type who prefers to spend the little she earns on the lottery or on doing her fingernails.
In summary, we oversimplify and rationalise what we want to believe. This makes our conscience rest easy, so we can continue enjoying our juicy steak Diane over a half bottle of Nexus Merlot.