The Tyranny of Numbers: Why Counting Can’t Make Us Happy. David Boyle
their heart of hearts anyway. Even anthropologists, who need a strong dose of interpretation provided by the wisdom, understanding and imagination of a researcher on the ground, began to lose themselves in matrices and figures. Scientists have to simplify in order to separate out the aspect of truth they want to study – and it’s the same with any other discipline that uses figures.
Often it’s only the figures that matter, even when everybody knows they are a little dodgy. One paper on this phenomenon by the economist Gerry Gill – called ‘OK the data’s lousy but it’s all we’ve got’ – was a quote by an unnamed American economics professor explaining his findings at an academic conference. Which is fine, of course, unless the data is wrong – because people’s lives may depend on it. ‘Yet professionals, especially economists and consultants tight for time, have a strongly felt need for statistics,’ says Chambers. ‘At worst, they grub around and grab what numbers they can, feed them into their computers, and print out not just numbers but more and more elegant graphs, bar-charts, pie diagrams and three-dimensional wonders of the graphic myth with which to adorn their reports and justify their plans and proposals.’
Chambers found that there were twenty-two different erosion studies in one catchment area in Sri Lanka, but the figures on how much erosion was going on varied by as much as 8,000-fold. The lowest had been collected by a research institute wanting to show how safe their land management was. The highest came from a Third World development agency showing how much soil erosion was damaging the environment. The scary part is that all the figures were probably correct, but the one thing they failed to provide was objective information. For that you need interpretation, quality, imagination.
‘In power and influence, counting counts,’ he wrote. ‘Quantification brings credibility. But figures and tables can deceive, and numbers construct their own realities. What can be measured and manipulated statistically is then not only seen as real; it comes to be seen as the only or the whole reality.’ Then he ended up with a neat little verse that summed it all up:
Economists have come to feel
What can’t be measured, isn’t real. The truth is always an amount Count numbers, only numbers count.
But the distinctions really get blurred when politicians start using numbers. Waiting lists up 40,000, Labour’s £1000 tax bombshell, fertility down to 1.7, 22 Tory tax rises – elections are increasingly a clash between competing statistics. It’s the same all over the world. Figures have a kind of spurious objectivity, and politicians wield them like weapons, swinging them about their heads as they ride into battle. They want to show they have a grasp of the details, and there is something apparently hard-nosed about quoting figures. It sounds tough and unanswerable.
But most of the time, the figures also sound meaningless. The public don’t take them in, and they simply serve as a kind of aggressive decoration to their argument. But, as politicians and pressure groups know very well, a shocking figure can every so often grasp the public’s imagination. In the UK, the best-known election policy for the 1992 general election – repeated in the 1997 election – was the Liberal Democrat pledge to put 1p on income tax for education. It sounded clear and costed, but it was the perfect example of a number being used for symbolic effect. It implied real commitment and risk: the 1p meant almost nothing. ‘If relying on numbers didn’t work,’ said Andrew Dilnot of the Institute of Fiscal Studies in a recent BBC programme, ‘then in the end a whole range of successful number-free politicians would appear.’
They haven’t appeared yet. The problem for politicians is that they have to use figures to raise public consciousness, but find that the public doesn’t trust them – and the resulting cacophony of figures tends to drown out the few that are important. The disputes of political debate have to be measurable, but they get hung up about measurements that only vaguely relate to the real world.
Take rising prices. You can’t see them or smell them, so you need some kind of index to give you a handle on what is a real phenomenon. You can’t hold them still while you get out your ruler, yet the ersatz inflation figures have assumed a tremendous political importance. We think inflation is an objective measure of rising prices, when actually it is a measurement based on a random basket of goods which has changed from generation to generation. In the 1940s, it included the current price of wireless sets, bicycles and custard powder. In the 1950s, rabbits and candles were dropped in favour of brown bread and washing machines. The 1970s added yoghurt and duvets, the 1980s added oven-ready meals and videotapes, and the 1990s microwave ovens and camcorders. It’s a fascinating measure of our changing society, but it isn’t an objective way of measuring rising prices over a long period of time.
II
‘Oh the sad condition of mankind,’ moaned the great Belgian pioneer of statistics, Adolphe Quetelet. ‘We can say in advance how many individuals will sully their hands with the blood of their neighbours, how many of them will commit forgeries, and how many will turn poisoners with almost the same precision as we can predict the number of births and deaths. Society contains within it the germ of all the crimes that will be committed.’
It’s a frightening thought, just as it was frightening for Quetelet’s contemporaries to hear him say it in the 1830s. But he and his contemporaries had been astonished by how regular the suicide statistics were. Year after year, you seemed to be able to predict how many there would be. There were the occasional bumper years, like 1846, 1929 and other economic crash periods, but generally speaking it was there. People didn’t seem to be able to help themselves. Amidst a constant number of individuals, the same number would take it into their heads to murder as much as get married. Statistics were powerful.
Quetelet was among the most influential of the statisticians trying to solve the confusion of politics by ushering in a nice clean, unambiguous world, urging that we count things like the weather, the flowering of plants and suicide rates in exactly the same way. ‘Statistics should be the dryest of all reading,’ Bentham’s young disciple William Farr wrote to Florence Nightingale, explaining that they could predict with some certainty that, of the children he had registered as having been born in 1841, 9,000 would still be alive in 1921.
To help the process along, Quetelet invented the dryest of all people – the monstrous intellectual creation, l’homme moyen or Average Man. Mr A. Man is seriously boring: he has exactly average physical attributes, an average life, an average propensity to commit crime, and an average rather unwieldy number of children – which used to be expressed as the cliché 2.4. But Average Man only exists in the statistical laboratory, measured at constant room temperature by professional men with clipboards and white coats. The whole business of relying on numbers too much goes horribly wrong simply because Mr Average is the Man Who Never Was – counted by people who know a very great deal about their profession or science but precious little about what they are counting. The Man Who Never Was measured by the Men Who Don’t Exist. It’s the first and most important paradox of the whole business of counting:
Counting paradox 1: You can count people, but you can’t count individuals
Average Man belongs to the Industrial Revolution and the Age of the Masses, but we just don’t believe most of that Marxist stuff any more. It belongs in the twentieth-century world of mass production, where people were transformed into cogs in giant machines, as pioneered by the great American industrialist Henry Ford – the man who offered his customers ‘any colour you like as long as it’s black’. Mass production and Average Man had no space for individuality. Figures reduce their complexity, but the truth is complicated.
Now, of course, you can almost have your car tailor-made. You can mass-produce jeans using robots to designs which perfectly match the peculiarities of individual bodies on the other side of the world. The days have gone when clothes issued by the military didn’t fit, when you struggled to keep up with the speed of the production line, with your tasks individually timed for Average Person by the time and motion experts. And we can see more clearly how difficult it is to categorize these widely different individuals who make up the human race. But in the hands of a bureaucratic state, people who don’t conform to the norm get hounded and imprisoned. Or,