The Norm Chronicles (32 page)

Given a lot of warning and a big budget, space technology might be used to prevent the collision. For bigger asteroids up to 100 m in diameter, with decades of preparation,
slow push
might be used to put the NEO into a different orbit, which is best done by slowing or speeding up rather than sideways nudges. Using a ‘gravity tractor’ – harnessing the gravitational attraction of an adjacent spacecraft – may be more feasible than actually shoving the rock. Spacecraft have already had close encounters with asteroids: the Hayabusa mission even landed briefly, collected particles and returned to earth.
¹³

With decades of warning, asteroids above 100 m and even up to 1 km across might be shifted by
kinetic impact
methods – i.e., ramming with multiple spacecraft. Anything bigger than 1 km would require hundreds of spacecraft to hit it, or alternatively a
nuclear detonation
close by. For 500m asteroids this could be set up in years rather than decades if the political will was behind it.

Anything above 10 km across, the size that wiped out the dinosaurs, is considered essentially unstoppable. Although these apocalyptic scenarios make good, or at least popular, films, the NRC conclude that the main risk is from an unexpected airburst of a small object, less than 50 m, but adds: ‘However, as all NEOs have not yet been detected and characterized, it is possible (though very unlikely) that an NEO will “beat the odds” and devastate a city or coastline in the near future.’ And there’s not much you can do about it.

As for man-made junk, about 5,400 tonnes of rubbish has come down over the past 40 years, and there were 28 re-entries of satellites in 2011. So far nobody has been injured, even when 40 tonnes rained down on the US after the Columbia space shuttle broke up. NASA afterwards estimated that there was a 1-in-4 chance someone would have been hit. And when the remnants of the Upper Atmosphere Research Satellite (UARS) came to Earth in September 2011, NASA said there was a ‘one in 3,200 chance of anyone being hit’.

But how are such calculations made? The satellite had been up there for 20 years. It stopped working in 2005 and weighed 5,700 kg, about the size and weight of a double-decker bus. NASA said it would break into 26 objects that would survive re-entry, weighing 532 kg in total, about the weight of eight washing machines. These would be spread over about 300 miles but cover a total damage area of around 22 square metres (around three car-parking spaces), but they had no idea where the debris would land. As one commentator said, you’d think these boffins would have better control of their satellites – it’s not rocket science.

The largest object weighed 158 kg, about the weight of an adult gorilla (though that sounds a bit soft – better to think of a couple of washing machines tied together, travelling at 100 m.p.h.). This does not sound encouraging, but the Earth is a big place, with a surface area of 500 million square kilometres (or 500,000,000,000,000 square metres), and so assuming the 22 square metres of bits can land anywhere, there is around a 1 in 23,000,000,000,000 (23 trillion) chance that any particular point will be hit.

So if an individual – let’s call him Norm – happens to be occupying that point, minding his own business, then, assuming a random landing
place, there is around a 1 in 23,000,000,000,000 chance that Norm will be hit – the same chance as flipping a coin 44 times in a row and coming up heads every time, or slightly better than the chance of winning the National Lottery twice in a row.

But there are about 7,000,000,000 other people on Earth, and so the chance that anybody at all will be hit is 7,000,000,000/23,000,000,000, 000 which is one in 3,200, which is just what NASA quoted.
*

This chance is low, essentially because people don’t cover much of the Earth. It may not seem like that when you are up against a stranger’s armpit on the Northern Line, but as anyone taking an intercontinental flight will notice, the globe is covered by an awful lot of not-very-much. If each of us claims 1 square metre, that’s 7,000 square kilometres in total, which is only 1/70,000 of the Earth’s surface. So if everyone in the world went to the Glastonbury festival, they would only cover Somerset and Wiltshire combined, although you can’t even begin to imagine the state of the toilets.

The calculation for falling people would be similar. Let’s assume one falling dead body, horizontally occupying say 2 square metres, every seven years, and let’s assume an at-risk area about the size of the London borough of Richmond (about 60 square kilometres, with a population of almost 200,000). This gives us a fairly straightforward if crude probability. Norm is comfortable with that. Prudence isn’t, it being somehow more real than the end of the world. It works out at about a 1-in-150 chance that one of the 200,000 population will be in the way, every seven years, and, if you happen to live there, a 1-in-30-million chance that it will be you personally, or a 1-in-210-million chance every year.

Is any of this worrying? As ever, it depends on the kind of person you are as much as it depends on the data. In the Lars von Trier film
Melancholia
one of the sisters is alarmed by the prospect of doom, the other is relaxed. Trier was reportedly fascinated by a comment of his therapist that depressed people often remain calm when confronted by threatening or stressful situations – on the grounds that life is awful anyway. The
German philosopher Schopenhauer made this the basis of a pessimistic view of life in which the only escape from a pointless, eternal failure to gratify human will is through aesthetic contemplation, ideally of music, like Wagner’s.

For Norm, who not only isn’t miserable enough simply to shrug at existential risk but also feels insufficiently happy for great optimism – he’s a middling kinda guy – anxiety about falling objects is more to do with the enormous uncertainty that surrounds the data.

Being average, he should be precisely vulnerable to the average risk of death from an asteroid strike. As he knows, this is the deliciously convenient measure of 1 MicroMort in a lifetime. As he also understands, this is one of the most startling exposures imaginable of the deficiencies of an average, an average that takes in the kind of bolt from the blue – or whatever colour space is – so small that it might dent your car or take out a roof-tile and so rare that the world’s media come to take pictures and that it might possibly finish you off if it happens to choose your 1 square metre among the 500,000,000,000,000 square metres on earth, and he combines this probability with the theoretical probability of total wipe-out all round. In other words, it is almost nothing combined with everything.

To produce from that a lifetime risk for any individual of 1 MicroMort is arithmetically sound but entirely pointless to everyday life. In other words, this average risk tells us almost nothing, even for
l’homme moyen
Norm. He’s not afraid of death, he’s afraid for his faith.

21
UNEMPLOYMENT

‘I’
M SORRY
to tell you, Norm, that we’re making you redundant.’

‘What?’

‘We’re making you …’

‘Yes, I heard …’

‘… redundant.’

‘… what you said. But I mean no. I mean you can’t.’

‘Norm, you’ve been a great asset, but …’

‘No, I mean you can’t
make
me redundant. I’m either redundant or I’m not, but you can’t
make
me redundant.’

‘…?’

‘You can’t
make
someone unneeded if they’re needed, the condition is pre-defined, it can’t be imposed, it’s … illogical, completely illogical, it’s like saying we’re going to make you …’

‘Norm …’

‘… six feet tall. It’s basically blatantly against the law, which means it’s not even allowed. And you’re meant to consult me anyway, and then
if
I were to be redundant because the job’s not there any more …’

‘Norm …’

‘… then you can get rid of me, dismiss me, but you can’t
make
me redundant. You can’t.’

‘Norm, we’re thinking of giving you the chop because your job’s toast and you’re scrap. What do you say?’

‘Ah. OK … I see.’

‘Good. You’re out.’

‘Right … Yes … Got it …’

‘When?’ said Norm.

‘How about tomorrow?’

So here he was, coming in to leave. This is what you call a low-probability, high-impact event, he said to himself. In which case, he rather feared he’d miscalculated the probability, as well as the impact.

So. Well. Norm sat at his desk and pulled up his striped socks. He sorted a few papers and put them in the recycling, deleted some emails, made sure one or two people were aware of one or two important bits and bobs and popped in to see what’s-her-name in personnel. Norm made a few calls. Norm had a drink at lunch with some of the youngsters, who gave him a nice pen and a card. Norm knocked on the boss’s door and said goodbye. ‘All the best Norm,’ he said. Norm put on his duffle coat. Norm walked past the other desks – ‘Cheers Norm’ – and dropped his ID at reception. He walked through the revolving door and stood outside on the pavement.

There had been a couple of occasions in life when he had tried feeling truly miserable, but his heart wasn’t in it. Perhaps now? The thought briefly cheered him up. Then that night he dreamed of a man in a duffle coat circling the drain. He hadn’t expected it, that was all. And so … erm.

NORM GOT IT WRONG
. He thought the worst would never happen. It’s the same error some think wrecked the global finance industry in 2008 and contributed to a deep recession – a failure, as Norm puts it, to take high-impact, low probability events seriously. How wrong depends on what happens to him next because, at the extreme, unemployment can kill, as we’ll see.

Anyone can get a number wrong, especially if you’re trying to put a probability on something that never happened before. And because Norm had never been given the elbow until now, he didn’t think too hard about it. It wasn’t normal.

When he calls losing his job high-impact, low-probability, he borrows the language of Nassim Nicholas Taleb’s book
The Black Swan
: ‘I don’t particularly care about the usual’, Taleb has said.

If you want to get an idea of a friend’s temperament, ethics, and personal elegance, you need to look at him under the tests of severe circumstances, not under the regular rosy glow of daily life. Can you assess the danger a criminal poses by examining only what he does on an ordinary day? Can we understand health without considering wild diseases and epidemics? Indeed the normal is often irrelevant. Almost everything in social life is produced by rare but consequential shocks and jumps.
¹

Some events are easier to put out of mind than others. If they’re unusual enough or seem improbable enough, or it’s hard to work out how they might come about, is there also a temptation to dismiss them? Although it’s not as if unemployment doesn’t happen to other people. And it’s not as if there haven’t been financial crashes before. The point is that we get a little too used to what we’re used to. And so this isn’t really a failure of calculation (as with asteroids in the last chapter). This time it is a failure of imagination. We can’t foresee everything that might go wrong, so we take the easy route of assuming it won’t. The remedy isn’t just better numbers, it’s more varied stories that can take us imaginatively out of our comfort zone – one reason why some people are attracted to the idea of scenario planning as a way of thinking what future dangers might be.

Still, the numbers could have helped. In early 2008 in the UK about 1.6 million people were unemployed, a little over 5 per cent of the workforce. Four years later, after a deep recession, more than a million more were on the scrapheap, for an unemployment rate of about 8.5 per cent.

This is the usual way of saying how bad unemployment is, and by implication the risk that you won’t have a job: simply look up the current rate. As expected, this risk went up as the economy went down. The effect of recession was that about 3 or 4 more people out of every 100 who wanted a job couldn’t find one.

But this way of talking about the risk can be misleading. Over four years there were not 1 million lost jobs. There were more like 15 million, about half of them by people who wanted another one. The number of times that someone was sacked, made redundant, their contract ended
or they were otherwise shown the door was many times greater than the number of people counted as unemployed.

This is not because the numbers are fiddled. And it is not because we’re counting people who went straight from one job to another. It is because in any economy the job market is a gigantic revolving door through which millions of people pass in both directions, into work and out, spending varying periods on either side. So these 15 million are all people who genuinely had no job for at least long enough to be counted.

When we measure unemployment, we typically measure the number of people who are on the wrong side of the door at any one time. But they are only the net change in the stock of unemployed, the million more who were out of work at that moment, compared with four years earlier. These numbers do not capture the immensity of the flow, or the huge numbers who have felt the chill of being on the wrong side at some time.

This is true not only in recessions. It happens in good times too. Thinking about the great scale of job churn gives a better measure of the numbers of people who experience unemployment, and is a better way of describing what for many is a state of vast and perpetual risk.
²

The statistics that capture this flow and describe the average person’s chance of losing their job are experimental. The Office for National Statistics doesn’t track the same people for long enough to be sure that the numbers are accurate. But they’ll do, as a rough guide.

They suggest that before the 2008–9 recession, when the economy was by general consent doing OK, the risk of losing a job was between 1 and 1.5 per cent every quarter, much as it had been for years: this is known as the hazard of unemployment, just as ‘hazard’ is also used for the current risk of death (see
Chapter 17
, on lifestyle). That is, 1 or 2 people in every 100 with a job would lose it every three months. Though because this is an average, some are more likely to go than others – those with low educational qualifications for example, or those in more casual trades – and some will go more than once.