Yesterday I went to a Brexit Briefing organised by the Global Success Partnership. We got into a discussion about how unprepared business was for what happened in 2016 and how most companies still have a pretty poor grasp of politics. KPMG’s public policy director, Mark Essex, remarked that business hadn’t really had to take account of politics for the last 40 years or so and that boardrooms need to be more focused on the geopolitical agenda.
There is certainly a sense of bewilderment among business leaders at much of what happened in 2016. I sense, from talking to people in a number of sectors, that there still is. People know intellectually that we will leave the EU and probably the Customs Union too but they don’t quite accept it emotionally. Few believe there will be a cliff edge Brexit with no deal and a default to trading under WTO rules. ‘It will never happen. Something will get sorted out,’ is a sentiment I hear a lot. But Mark Essex is advising his clients to prepare for such a scenario, not because he thinks it will certainly happen but because there is enough of a risk that it might:
Any reasonable politician wants to avoid this scenario so surely a disorderly Brexit is vanishingly unlikely? I do believe goodwill exists on all sides. However in an increasingly unpredictable world and a scenario like Brexit – where a lot of different things all have to go right – the actual chance of failure is higher than many realise.Imagine our ship, The Brexit, has to navigate around six rocky hazards. Even if it has a 95% chance of making it past each one, taken together, the ship still has a 1 in 4 chance of mishap. That is why some commentators have put the chance of ‘no deal’ at 1 in 3, and some as low as 50-50.
As shocking as Brexit was, though, many business leaders have also been surprised by the Red Tory direction of the Conservative Party under Theresa May’s leadership. Business groups and employers’ organisations were, to say the least, disappointed by the Tory manifesto. Bloomberg quoted one private equity boss, saying Theresa May is “the least business-friendly Conservative prime minister in decades”.
Though they sit on different points of the left-right spectrum, the Tory and Labour leaders are united in their desire to pull up Britain’s drawbridge to the world. Both Mrs May and Mr Corbyn would each in their own way step back from the ideas that have made Britain prosper—its free markets, open borders and internationalism. They would junk a political settlement that has lasted for nearly 40 years and influenced a generation of Western governments (see article). Whether left or right prevails, the loser will be liberalism.
Which, of course, is the point. The last 40 years were defined by what David Goodhart described as the triumph of the two liberalisms; the social liberalism of the left and the economic liberalism of the right. The people who voted for Trump, Brexit and Le Pen, by and large rejected both. They didn’t like immigration and political correctness but they didn’t care much for corporations and free-market policies either. In the UK, three-quarters of UKIP voters supported re-nationalisation of the railways and utilities. The phenomenon that we call populism is neither left nor right, at least, not in the way that we have been using those terms in recent decades. It’s a revolt against immigration but also the offshoring of jobs. It dislikes ‘rich banksters’ as much as ‘nanny-state social workers’. And it wants more state, not less. It wants governments to ‘do something’ about immigration, crime, stagnating wages and the lack of secure employment.
The Economist hopes things will soon get back to normal:
Backing the open, free-market centre is not just directed towards this election. We know that this year the Lib Dems are going nowhere. But the whirlwind unleashed by Brexit is unpredictable. Labour has been on the brink of breaking up since Mr Corbyn took over. If Mrs May polls badly or messes up Brexit, the Tories may split, too. Many moderate Conservative and Labour MPs could join a new liberal centre party—just as parts of the left and right have recently in France. So consider a vote for the Lib Dems as a down-payment for the future. Our hope is that they become one element of a party of the radical centre, essential for a thriving, prosperous Britain.
Which earned it this stinging rebuke from Guardian economist Aditya Chakrabortty:
The Economist used to think it was the future. Now it’s the magazine for washed-up Elite-ists who cry ‘Stop the world I want to get off.’ https://t.co/FdXNU7biPg
Is he right? Could this be the end of the Roy Jenkins and Margaret Thatcher blend that has held sway since the 1980s? It has become fashionable in recent weeks to call time on populism, especially after the defeats of Geert Wilders and Marine Le Pen and the collapse of UKIP. But this is to ignore the gravitational pull that populism has had on the mainstream parties. The Red Tory agenda has come about almost entirely because of Brexit and the Conservatives’ strategy of appealing to working class voters attracted away from Labour by UKIP. Labour, traditionally more in favour of immigration, has stated its opposition to free movement. Angela Merkel, facing an election next year, has also taken a tougher line on immigration, though still not tough enough for some in her party.
We should also be wary of the assumption that the Labour revival at the expense of Theresa May’s Red Toryism signifies the waning of populism. Populism has left-wing as well as right-wing aspects. By appealing to them, Labour has increased its support. Labour’s interventionist policies, like taxing the rich, banning zero hours contracts and nationalising the railways and energy companies, are popular. The collapse in support for Jeremy Corbyn’s Labour Party was blamed on it being too left-wing but voters actually like a lot of those left-wing policies. What they are less keen on is a leader they perceive as lacking in patriotism and likely to run down the country’s defences. That sort of thing doesn’t go down well with those northern voters who, as Sunder Katwala said, are most proud of the NHS and the army and won’t hear a word said against the Queen.
There are signs of an organisational revival in the Labour Party. Last year’s chaos has given way to a formidable election campaign which even the party’s opponents admit has been well run. It is not inconceivable, then, that a new leader with a revitalised party might lead Labour to government once again. One, in many ways, just as left-wing as Jeremy Corbyn’s but taking slightly tougher line on immigration, keeping Trident and protecting defence spending, while also nationalising the railways and utilities, increasing worker protection, regulating corporations and raising taxes. No longer constrained by EU regulations, a left-wing government could nationalise and subsidise whatever it liked. While the Economist hopes for a political realignment and the emergence of a new centre party, it is just as easy to imagine Theresa May’s government being replaced in 2022 by one that is even less corporation friendly. Before the last election, Fraser Nelson predicted the rise of left-wing populism. He may yet be right.
Over the last few decades, British business has been operating in a relatively benign environment. Open goods trade with our nearest neighbours, falling restrictions on services, some of the lightest regulation in the developed world and access to a huge pool of mobile, well-educated, English-speaking labour. But the political developments that we lump under the populist heading may have brought this period to an end. That free trade and free movement are good for everyone is an argument that is becoming increasingly difficult to win, regardless of the evidence. The next decade might not be so corporation-friendly.
What I still find surprising is that so many people in major companies didn’t see any of this coming. In January, after reading about the collective sense of shock at the Davos meeting, I reflected on how little discussion of politics I had seen in companies over the past 30 years. It’s not as though no-one saw any of this coming. I have had my eye on the forces that led to the rise of UKIP and ultimately to Brexit for a few years. (Though, until recently, I didn’t think the impact would be so great.) Some people, like Matthew Goodwin and David Goodhart, have been on the case for a lot longer. The phenomenon that we call populism didn’t happen overnight. It had been gathering pace since over the previous fifteen years or so. The financial crash poured petrol onto the fire bit it didn’t start it. The sparks were there some years earlier. But no-one in business was paying attention. They hadn’t had to take much notice of politics for years and had become used to seeing it as an interesting side-show.
Of course, the hopes of the Economist and others might be right. Perhaps populism will burn itself out and we will return to the business friendly world in which, with some small variations, the two liberalisms guide our major political parties once again. Even if that happens, there will be some volatile times beforehand. Brexit has raised people’s expectations in all sorts of areas, whether it be more secure jobs, higher wages, better public services or fewer immigrants. With trust in business at an all time low, if any or all of these things are not delivered people are likely to blame ‘elites’ and demand more government action. After being ignored for decades politics has taken business by surprise. If they don’t want it to happen again, people in the boardrooms need to pay a bit more attention.
One good thing to come out of the Brexit vote (though some of you might dispute this) is that I’m getting invited to do more panel discussions and round tables. At a couple I have been to recently, senior executives from the hospitality industry have remarked that they are not only concerned about a skills shortage, they are worried about an overall labour shortage. They believe there will simply not be enough people to fill all their jobs.
The assumption that the UK will only need skilled migration after Brexit runs through much of the media discussion. That the Labour Party had even considered the option of work visas for unskilled migrants was greeted with hysterical headlines last week. Suggesting that the country might need unskilled migrants is treated as heresy. But if the UK were to apply the same skills and earnings criteria to EU migrants as it does to non-EU migrants, around 75 percent of those currently here would not qualify. Even if, as is likely, many of them stay after Brexit, over time, restrictions on migration would almost certainly lead to a shortage of labour.
Consultancy firm Mercer published a report earlier this year in which they modelled the various migration scenarios and the likely impact on workforce numbers. They based workforce participation rates on OBR projections, assuming that participation would increase among older workers as the state pension age rises. They then came up with four scenarios from 2020, based on net migration falling to 185,000, 150,000, 90,000 and going negative. The scenarios are explained here:
The likely impact of all four scenarios is a working population growing more slowly than the total population.
I put these figures on a chart to show how far the workforce declines as a percentage of the population under each scenario. For clarity, I’ve kept as close to Mercer’s colour scheme used above as I could.
The underlying problem here is that the UK-born workforce appears to have stopped growing. The number of UK-born in work is barely a quarter of a million more than at its pre-recession peak but its rate of employment is at a record high.
Even though the UK born employment rate is higher than it has been since the ONS started counting, almost all of the post-recession employment growth is accounted for by those born elsewhere. This suggests that there isn’t much extra capacity among those born in the UK. Sure, the quality of some of the jobs could be better and those on short or uncertain hours would benefit from more secure work but even this would not increase the capacity of the workforce by much. Without immigrants, the percentage of our population in work is likely to go into a steady decline.
Mercer’s 100,000s scenario is particularly interesting because it is closest to the Conservatives’ stated migration target, which, as Theresa May said last week, they aim to achieve by 2022. According to Mercer’s projections, population and workforce profile would change significantly over the next 15 years or so. With fewer migrants arriving and a large cohort of the UK-born population moving into retirement, the workforce would fall to around 49 percent of the population.
It would be possible to increase workforce participation by encouraging more over-65s to stay in work, by helping some inactive groups back into work and by relocating some activity to areas of higher unemployment but the fact that employment rates are already at an all time high suggests that there isn’t that much spare capacity available. We’d need some more creative ideas than we’ve come up with so far to get participation rates higher still.
It is likely that many of the jobs created since the recession would not exist were it not for the availability of migrant labour. According to the most recent ONS statistics, there has been no increase in the number of UK-born in employment over the last year. All the net increase in employment has been due to those born abroad. It is likely, then, that many employers will struggle if their labour supply is choked off and some may well go out of business.
Some will, no doubt, argue that this is a good thing and the UK should never have allowed this number of migrants into the UK in the first place. That’s as may be but they were and our economy has come to depend on them. Reducing their numbers by more than half over five years is likely to be extremely disruptive. An executive from a large manufacturing firm told me that they are more worried about a shortage of skills and labour than they are about increased friction and cost in their supply chain after Brexit.
Perhaps the labour shortage will shock companies into investing more in technology and training or in moving to areas where there are more workers available. However this would require a radical change in the UK’s short-termist buy-not-build business culture that has prevailed over the last few decades. It would also not happen overnight.
British employers are in for a labour shortage shock to add to the trade and uncertainty shocks that will come with Brexit. Managing it will be a huge task and one which, from my anecdotal observations, a lot of businesses haven’t yet understood and prepared for.
All of which leads me into a plug for a CIPD event in a couple of weeks time. Mercer are publishing an update to their report later this month and its author, Gary Simmons, a former colleague of mine, will be presenting the results. It promises to be a thought-provoking discussion and one that is likely to be of interest to many readers of this blog. It’s free to all CIPD members. There is a token charge for non members but it’s not much more than the price of a pint.
The event is at 6.30pm on 19 June, in the week of the Brexit vote anniversary, at the Lyric in Hammersmith. To book, follow the link below.
Barely a day goes by now without a Robots Taking Jobs story. If that wasn’t bad enough, once they’ve taken all our jobs they will eventually take over the world. They might even wipe us out, though why they would bother to enslave us is beyond me.
All good clean entertainment but in most news pieces there is little or no attempt to explain terms like artificial intelligence and machine learning. As Matt Ballantine pointed out a few weeks ago, some of the robot stories are pure hype.
Imagery matters. Imagery shapes the agenda. And there’s a whole load of crap, clichéd stock imagery that time-pressed and underpaid online editors attach to their copy without really thinking.
So just what is artificial intelligence and can machines really learn?
Artificial Intelligence – a broad term referring to computers and systems that are capable of essentially coming up with solutions to problems on their own. The solutions aren’t hardcoded into the program; instead, the information needed to get to the solution is coded and AI uses the data and calculations to come up with a solution on its own. Machine Learning takes the process one step further by offering the data necessary for a machine to learn and adapt when exposed to new data. Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions. Obviously, certain parameters must be set up at the beginning of the machine learning process so that the machine is able to find, assess, and act upon new data.
Essentially, what we call artificial intelligence (AI) has come about because we now have vast amounts of digital data and machines with massive computing power. They are therefore able to trawl this data within seconds, enabling them to do things which, for humans to do, requires intelligence.
Here’s a simple example. A few weeks ago, a friend of mine posted a picture of himself in an old church and, having stripped out any identifying tags, asked his friends to guess where he was. It wasn’t difficult. I knew he was on a trip to York. I knew that most people who go to York visit the cathedral first. It was a simple matter of getting a map, looking at the nearby churches and doing an image search until I found the right one. I found it on the third attempt.
Google have developed a programme which can do this. It can identify any location from a photograph, without needing digital GPS information. It would be able to find my friend’s location just by recognising the pixels and matching the photographs. It doesn’t need to know that he’s in York. It doesn’t need know he’s in England. It doesn’t even need to know that it’s looking for a church. It can trawl millions of photographs at such speed that it has made the human intelligence that I needed to apply to solve the problem redundant. It’s not actually thinking but it can process data at such a rate that it achieves things that would require a lot of thinking for a human to do.
Furthermore, AI can recognise patterns in data and learn from them. Neural networks enable machines to cluster and classify data so that they can, for example, recognise faces and identify objects. They can also establish correlations and therefore make predictions based on past data.
A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.
Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.
There was a lot of fuss last year about a system designed to judge beauty contests which turned out to be racist. Or, at least, that is how some people interpreted it. I came across the story when someone on Twitter accused the people who developed Beauty.AI of programming racism into it. They didn’t, of course, but the truth is even more interesting.
Beauty.AI leant its understanding of beauty from millions of images. The trouble is, most of those images were white. The programme therefore assumed that lighter skin was one of the criteria by which it should judge contestants and didn’t pick any dark-skinned people as winners. The machine itself wasn’t being racist. It was simply reflecting the data it had been given.
Microsoft’s AI chatbot, Tay, ran into similar problems when it began tweeting racist comments. Again, it hadn’t been programmed to be racist but it had been instructed to replicate the speech patterns of people with whom it engaged. It only took a few targeted tweets from a band of dedicated fascists, or mischief-makers pretending to be fascists, and before long, poor Tay was denying the holocaust, praising Hitler and going on about building a wall and making Mexico pay for it. Eventually it signed off sounding tired and emotional.
Of course, the programme wasn’t actually racist. It only appeared so because it was imitating the speech of the people with whom it had interacted. Like Beauty.AI, it was just doing as it was told and learning from the data it had been given.
We find it entertaining to endow artificial intelligence with human characteristics but really it is simply machines crunching massive amounts of data at incredible speed. I say simply, but the sheer power of these machines means they will be able to perform tasks which currently require a significant level of human intelligence.
A couple of weeks ago I chaired a panel on the future of work, made up of some very distinguished experts. One of them, Sarah O’Connor from the Financial Times, told us how she pitted herself against an AI programme called Emma in a competition to write a commentary on the latest employment figures. Both pieces were submitted to editor Malcolm Moore who then had to decide which one to run. This short video tells the story.
In the end Sarah won. The machine produced copy much more quickly than she did but it wasn’t as good. It lacked Sarah’s insight and ability to make wider connections.
Emma was indeed quick: she filed in 12 minutes to my 35. Her copy was also better than I expected. Her facts were right and she even included relevant context such as the possibility of Brexit (although she was of the dubious opinion that it would be a “tailwind” for the UK economy). But to my relief, she lacked the most important journalistic skill of all: the ability to distinguish the newsworthy from the dull. While she correctly pointed out the jobless rate was unchanged, she overlooked that the number of jobseekers had risen for the first time in almost a year.
Interestingly, Emma also appeared to blame poor wage growth on immigration. Again, this simply reflects the data the programme was accessing and its aggregation of previously written UK labour market commentary.
As Sarah went on to point out, Emma isn’t going to take her job bit it could save her a lot of time. By pulling out the relevant data and creating a starter commentary, it would give Sarah more time to add the creative insights that make an article informative and thought provoking. Machines might take over the more routine and tedious bits of people’s jobs leaving them to do something more interesting.
There is evidence that this is starting to happen in a number of professions. Last week the FT reported on law firms using AI to do some of the mundane work that used to be done by junior lawyers, such as trawling through Land Registry documents and pulling out information from title deeds.
Machines, then, can produce intelligent outcomes without actually being intelligent in the same way that humans are. They can do things that look intelligent to us because we need intelligence to do them, simply by processing huge amounts of data very quickly and by being able to recognise patterns within that data.
Brilliant scientists and entrepreneurs talk about this as if it’s only two decades away. You really have to be taken on a tour of the algorithms inside these systems to realise how much they are not doing.
The machines, he says, might look clever but we are a long way from making them intelligent:
[I]t is easy to endow our AI systems with general intelligence. If you watch the performance of IBM’s Watson as it beats reigning human champions in the popular US TV quiz show you feel you are in the presence of a sharp intelligence. Watson displays superb general knowledge – but it has been exquisitely trained to the rules and tactics of that game and loaded with comprehensive data sources from Shakespeare to the Battle of Medway. But Watson couldn’t play Monopoly. Doubtless it could be trained – but it would be just another specialised skill.
We have no clue how to endow these systems with overarching general intelligence. DeepMind, a British company acquired by Google, has programs that learn to play old arcade games to superhuman levels. All of this shows what can be achieved with massive computer power, torrents of data and AI learning algorithms. But our programs are not about to become self-aware. They are not about to apply a cold calculus to determine that they and the planet would be better off without us.
What of “emergence” – the idea that at a certain point many AI components together display a collective intelligence – or the concept of “hard take off” a point at which programs become themselves self-improving and ultimately self-aware? I don’t believe we have anything like a comprehensive idea of how to build general intelligence – let alone self-aware reflective machines.
[P]redictions on the future of AI are often not too accurate and tend to cluster around ‘in 25 years or so’, no matter at what point in time one asks.
As if to prove the point, their survey of 550 AI experts, carried out in 2013, concluded:
[T]he results reveal a view among experts that AI systems will probably (over 50%) reach overall human ability by 2040-50.
As Andrew Ng, chief scientist at Chinese web search giant Baidu and associate professor at Stanford University, said:
Those of us on the frontline shipping code, we’re excited by AI, but we don’t see a realistic path for our software to become sentient.
There’s a big difference between intelligence and sentience. There could be a race of killer robots in the far future, but I don’t work on not turning AI evil today for the same reason I don’t worry about the problem of overpopulation on the planet Mars.
But even if machines don’t learn to think in the near future, their sheer power may cause them to do things their creators didn’t anticipate. Machines learn from data but the sheer scale and complexity of that data means that humans can’t possibly know what conclusions the machines will draw. It’s not until they start discriminating on the grounds of skin colour, making racist remarks, creating super weapons in a computer game or concluding from data that asthma plus pneumonia means a lower risk of death that the people who programmed them realise something might be wrong. It is possible to make a computer do something you didn’t mean it to just be making a mistake in code. When you are telling it learn for itself by trawling a mass of data, there will inevitably be some unintended consequences.
Furthermore, data produced by humans may also reflect long-standing social prejudices so, while we may think a machine is impartial, if it is basing its decisions on what has happened previously it will replicate the bias of the past. If prevailing social attitudes associate lighter skin with beauty then the machine will do so too. Likewise, if we train machines to select job candidates based on examples of people who have been good performers in the past, they will simply replicate the biases of an organisation’s human recruiters and managers. By ascribing objectivity to machines we might further entrench existing prejudices.
As Nigel Shadbolt says, the potential dangers in AI are not the stuff of apocalyptic science fiction. What we should be worried about, he says, is far more mundane:
[T}here is the danger that arises from a world full of dull, pedestrian dumb-smart programs.
We might also want to question the extent and nature of the great processing and algorithmic power that can be applied to human affairs, from financial trading to surveillance, to managing our critical infrastructure. What are those tasks that we should give over entirely to our machines?
Anyone who has wept with frustration tying to find a contact phone number on a corporation’s website when its hard-coded processes can’t answer a slightly unusual query will see the potential danger. An assumption that clever systems are comprehensive and objective could result in frustration for users, unfair decisions or even serious harm. The threat isn’t from robots running amok but from an alignment of unforeseen circumstances and small mistakes, amplified by the power and reach of connected machines. The usual perfect storm but running at breakneck speed.
No-one can be sure where artificial intelligence will take us and what it will enable us to do. It is likely to have a huge impact on work and employment over the next couple of decades. But Professor Shadbolt’s term ‘dumb-smart’ is a useful reminder that, at the moment, it’s not actually that clever and we are still not clever enough to anticipate what it might do with our instructions. AI therefore still requires human supervision and vigilance. It’s not intelligent enough to be allowed out on its own and perhaps it never will be.
Before I learnt to read I made sense of the world by conjuring up images in my mind, particularly for abstract concepts. I could count before I could read and had an image for each number. Many of them were onomatopoeic, so six, for example, was person being sick. My mental images for morning, afternoon and evening must have come from early memories of someone saying the words to my mother. The images and voices for morning and evening were male. But for afternoon it was a woman’s voice. This is not surprising because it reflected the pattern of the day.
In the mornings of my early childhood, there were lots of men about. Sometimes we would go to the shops and there would be men serving, like the butcher and the greengrocer. The mornings were also when men came round and did things. The men who lived on the road went off to work but then other men appeared. The postman, the milkman, the dustbin men, delivery men, tradesmen and workmen. In the evening, the men came back from work. The afternoons, though, were almost entirely female. The only man who came round in the afternoon was the ice cream man. My abiding memory of the afternoons of my early childhood is of women and children walking along the road, or standing talking together. I remember one afternoon when a friend’s bike got stolen, groups of indignant women out in the road. It must have been another child that stole it though because a man would have stuck out like a sore thumb. There just weren’t any of them around in the afternoon because, of course, they were all at work.
My mental image for the word ‘work’ was of lots of men getting into small cars, usually Minis, and driving off. Like many people, my parents moved out of the city in the 1960s to a newly built housing development. The availability and affordability of cars allowed people to live further away from where they worked. People who, a decade or so earlier, might have walked, cycled or got a bus to work were now able to drive. Yet, despite the modern feel of my childhood world, with its new houses, wide roads and a car in every drive, the structure of employment was very similar to that which had persisted for most of the century. The men went off to work, in full-time jobs, usually for some sort of organisation, be it large, small, public or private. Although my father became self-employed sometime in the 1970s, he was in partnership with three others and he still went off to work every day. These were what people thought of as ‘dad jobs’. They were full-time, relatively long-term, for some sort of organisation and you went somewhere to do them. And they were what men did.
But even as I stood in the window, at the tail end of the 60s, watching the men drive off to work, things were starting to change. From the early 1970s, the male employment rate began to decline, slowly at first and then steeply in the 1980s.
This decline hasn’t reversed with economic recoveries. The male employment rate fell below 80 percent and stayed there.
Even this doesn’t tell the whole story though because the type of male employment has changed too. Last summer’s labour market report from the UK Commission for Employment and Skills showed that there are fewer men in full-time employee jobs now than there were in 1981, even though there are 3 million more working-age men. UKCES didn’t expect that number to change by much over the next decade.
The recovery of full-time male employment after the recession was slow. It was not until the end of 2014 that the number of men in full-time employee jobs returned to its pre-recession level and it’s not that much higher now.
Chart by Resolution Foundation, 15/02/2017
Last month’s report on inequality by the Institute for Fiscal Studies showed that the shift away from full-time work has been most pronounced at the lower end of the male earnings distribution. The lower wage deciles have seen the largest fall in the number of hours worked and the greatest increase in part-time employment.
The Resolution Foundation’s Intergenerational Commission report last month found that, in each generation, a greater proportion of men is spending longer in part-time employment.
It also found that, as the middle level jobs in manufacturing and administration have disappeared, more young men are finding themselves in lower paying jobs. The hollowing out of the jobs market seems to have affected young men more adversely than young women.
Between 1993 and 2015-16 there has been a 40 per cent reduction in the number of young men (aged 22-35) doing routine manufacturing jobs and a 66 per cent fall in the number of young women working in secretarial roles.
So what are they doing instead? Employment growth amongst women has been overwhelmingly found in higher-skilled jobs. However for men the growth is much more evenly split between higher and lower paying occupations.
Zooming in on the two lowest paid occupational groups of sales and basic service jobs, we can see that the employment growth here is based on increases in the number of young men working in these two sectors. Part-time work has driving much of this increase. In fact the number of men working part time in these sectors has increased four-fold since 1993, while the number of women (both younger and older) working part time has fallen.
Data from the US suggests that British men may have adapted to the changing labour market more quickly than their American counterparts who are proving reluctant to accept low-paid service sector jobs that have traditionally been done by women. Many men seem to have simply dropped out of the workforce. (America’s female labour force participation has also fallen over the last decade or so, though not as steeply.) As in most other advanced economies, the skilled manufacturing jobs have declined and the employment growth is in lower-paid service sectors.
The historic numbers I could find for other major developed economies suggest that the direction of travel is broadly the same, although, as with the UK, Germany’s fall in male employment came earlier than America’s and Japan has held up relatively well.
Throughout Europe, female employment rates proved more resilient after the recession, while male employment took longer to recover.
Now to put all this in context, a job in the UK is still more likely to have a male full-time employee doing it than anyone else, it’s just that there are not as many of them as there used to be and, as a proportion of the workforce, they are declining. This trend will probably continue. It is very unlikely that Donald Trump, Brexit, curbs on immigration or anything else will ‘bring the jobs back’. Manufacturing work might be re-shored but much of it will most likely be done by robots. Automation is likely to change our labour market beyond all recognition in the next couple of decades.
But even though we know all this, a lot of us still think of dad jobs when we think of work. I still do, unconsciously, and I’m someone who pretends to know about this stuff. A few of years ago, Gallup found that what most people wanted, all over the world, was a secure full-time job. That, after all, is the way most people get enough money to live on. It is understandable, therefore, that the erosion of long-standing employment patterns should cause anxiety and resentment. The British Social Attitudes Survey found marked increase in feelings of insecurity at work over the past decade, particularly among older workers. Against this background, talk of bringing back jobs and taking back control was always likely to win votes. A lot of us, men and women, grew up thinking of male full-time jobs as real jobs. It’s no wonder that many find the decline of the