For many us, work is not only a vital source of income, but also an important part of our identity. As computers become ever better at doing jobs that used to be the exclusive preserve of humans, the work available to us and the rewards for doing it will change dramatically. As economist Daniel Susskind explains, these developments are going to force us to rethink how society as a whole works at a very fundamental level, changing the role of the state, the way we think about how individuals contribute to society and how they can, or should, be rewarded.
We’re talking about the future of work. Could you start by explaining how, in general, you think about the future of work and what you were looking for in your choice of books?
In my view, to think clearly about the future of work, we have to begin by looking at the past: at inherited ideas about the impact of technology on work and traditional views on how we ought to respond. By reflecting on what we got right and wrong, we can better prepare ourselves for the future. My first two choices reflect this: one book captures a popular view of machine capabilities; the other, a common plan for responding to the challenge of automation. My remaining three choices focus on the different problems that I think we will face in the years to come.
When I started thinking about the impact of technology on work, almost a decade ago, the sort of book that I would have begun with was The New Division of Labor, based on the fascinating work done by Frank Levy and Richard Murnane—that’s my first choice.
This book contains an early articulation of a view of machine capabilities that has been dominant in economics for some time. It is based, in part, on a particular understanding of how machines must operate: that they must somehow copy the way human beings think and reason in order to outperform them. If you want to automate a task, the argument goes, then you have try and uncover the rules that human beings follow when they perform that task, and capture them in a set of explicit instructions for a machine to follow. If you can capture how human beings perform a task in a set of rules, then that task can be automated. But if you cannot, then that task is likely to be out of reach of machines.
“I don’t think we’re taking seriously enough the threat of a world where there’s not enough well-paid work for people to do”
This view is not confined to economics. It was also popular in the field of artificial intelligence at one point. I know this because my dad, with whom I co-authored a book called The Future of the Professions back in 2015, wrote his doctoral thesis on artificial intelligence and the law back in the 1980s at Oxford University. Almost forty years ago, he was part of the vanguard, trying to build systems that could solve legal problems. And importantly, the philosophy of what he was doing back then was very similar to what these economists were elaborating some years later; he thought that if you wanted to build a system to perform a legal task, you had to sit down with a human lawyer, get her to explain to you how it was she performed that task, then capture that explanation in a set of explicit rules for a machine to follow.
In economics, this view of machine capabilities gave rise to a very influential distinction—between so-called ‘routine’ tasks and ‘non-routine’ tasks. A task is ‘routine’ if a human being can articulate how she performs it; a task is ‘non-routine’ if she cannot. And the thought was that we can only automate ‘routine’ tasks, because those are the only ones for which we can readily articulate and write explicit rules for a machine to follow. This distinction is now widespread outside the academic world. Think how often people commenting on the future of work might claim that machines can only perform tasks that are ‘repetitive’ or ‘rules-based’ or ‘well-defined’; those are just different words for ‘routine’. Conversely, they might say that machines struggle with tasks that are ‘difficult to specify’ or ‘complex’; these are other ways of saying ‘non-routine’. The New Division of Labour, then, captures quite nicely an early account of a view of machine capabilities that has been dominant in economics, artificial intelligence, and wider life for some time.
Recently, though, a problem has emerged with this view of machine capabilities. What do the tasks of driving a car, making a medical diagnosis, and identifying a bird at a fleeting glimpse have in common? Well, these are all tasks that, until not too long ago, leading economists thought could not readily be automated. Yet today, all of them can be to an extent. Almost all major car manufacturers have driverless car programmes. There are countless systems that can diagnose medical problems. And there is even an app developed by the Cornell Laboratory of Ornithology that can tell you what bird you are looking at if you take a quick photo of it.
Why did economists think these tasks were out of reach of machines? Because they are all ‘non-routine’ tasks, on their definition that I described before. Ask a doctor, for instance, how she makes a medical diagnosis, and she is going to struggle to tell you exactly what rules she is following. She might be able to give you a couple of rough bits of advice, but in the end she will struggle. Instead, she will appeal to things like gut reaction, instinct, experience, and judgment. It is difficult to articulate how we use these faculties, and so it was thought it is very hard to automate tasks that required them: if a human being cannot explain how she performs a task, the argument goes, where do we begin in writing a set of rules for a machine to follow?
“Machines are increasingly able to perform tasks in fundamentally different ways to human beings, without having to copy our reasoning or thinking processes”
So, what went wrong? Because of recent advances in processing power, data storage capability and algorithm design, machines are increasingly able to perform tasks in fundamentally different ways to human beings, without having to copy our reasoning or thinking processes. The result is that it now matters far less that we struggle to articulate how we perform particular activities. Take the system recently developed by a team of researchers at Stanford that can tell whether or not a freckle is cancerous as accurately as leading dermatologists. How does it work? It is not trying to copy the judgment or the intuition of a human doctor. Instead it has a database of 129,450 past cases, and it’s running what is essentially a pattern recognition algorithm through those cases, hunting for similarities between them and the particular photo of the troubling freckle in question. It is performing the task in an un-human way, based on the analysis of more possible cases than any human doctor could hope to review in their lifetime.
The deeper consequence of these technological advances is that the ‘routine’ verses ‘non-routine’ distinction, which shapes how many people still think about automation, is actually far less useful than it was in the past. More and more ‘non-routine’ tasks are being taken on by machines. That is what fascinates and troubles me in my writing and research.
When was The New Division of Labour written?
The paperback came out 2005, so 16 years ago. I don’t think the ‘routine’ versus ‘non-routine’ distinction is ever explicitly made in that book—that may have come a little later in the literature—but it is one of the earliest places in economics that we see this emphasis on ‘rules’, and this preoccupation with trying to capture the ones that human beings follow when trying to automate a task. That is why it is such a valuable book.
I wrote A World Without Work, in part, in response to the apparent breakdown of this distinction. I find it remarkable that so many activities, which we had previously thought were out of reach of machines, are increasingly being automated. And I don’t think we’re taking seriously enough the threat of a world where there’s not enough well-paid work for people to do, because of these remarkable technological advances.
Even though in some sense it has been taken over, what conclusions do the authors draw about the future of work, given their assumptions about technological development?
The broader literature that has built up around this book tends to support an optimistic view about the future of work. It encourages us to think that there is some big realm of activity—namely all these ‘non-routine’ tasks—that we cannot automate. My fear is that this is no longer the case. However, rather than try to identify new boundaries to the capabilities of machines, I argue in my work that a more useful way to think about new technologies is that they are relentlessly—gradually, but relentlessly—becoming more capable, encroaching on tasks we once thought only human beings could do.
This book is important because it captures another conventional wisdom with respect to automation: that the best response to the challenge is ‘more’ education. And it does this in a wonderfully clear and engaging way.
The book is a reaction to an empirical puzzle that emerged in labour markets during the second half of the 20th century. During that time, particularly in the US, the number of people graduating from colleges and universities increased. And in economics, if the supply of something increases, you would expect the price of it to fall. But the puzzle is that the wages these people received relative to others—the so-called ‘skill premium’—was rising instead. How could that be? The answer is that new technologies were ‘skill-biased’, that people with more years of formal schooling behind them were more able than others to put them to productive use, and so demand rose for their efforts. As a result, even though the supply of skilled people was increasing, the demand for their work soared so much that their wages were still pushed up, relative to those without all that formal schooling.
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This is why the book argues that the best response to technological change is more education: if technology benefits those with skills, then you want to try and make sure that your workforce is as skilled as can be. There is a metaphorical ‘race’ between workers and machines and, as the latter become more capable, you have to give the former more education to keep up. At the start of the 20th century, ‘more’ meant ‘more people’: mass education was the goal, that everyone, whatever their background, should have access to formal schooling. But as the century went on, more started to mean ‘more advanced’; encouraging people to go college and university, to pursue advanced degrees.
This book, like my first choice, also supports an optimistic story about the future of work. It suggests that, as technological progress accelerates, there is an effective way to respond: more education. I expect that many policymakers, if they were to read this book, would find themselves nodding along in agreement. My worry, though—and it is a worry increasingly shared by others as well—is that limits to education are starting to emerge.
In a sense, I suppose Levy and Murnane’s view of the world at least had the comforting merit that you could identify things that weren’t going to be made redundant by machines and therefore train people to do those kinds of tasks. With those distinctions gone, has it become harder to decide what kind of education we need for work in the future?
Yes, that is a growing issue. Though the two books actually complement each other quite neatly. A natural question to ask of Katz and Goldin is, ‘Well, why is it that technology was skill-biased over the last few decades?’ And the Levy and Murnane book provides one helpful answer: skilled jobs tend to involve tasks that require faculties like creativity, judgment, empathy, and these are hard to capture in a set of explicit instructions for a machine to follow. In short, to use the old distinction, they tend to involve ‘non-routine’ tasks, and that is why they have proven so hard to automate. That is why there has been so much demand for human beings to do those activities.
As an aside, it is worth noting that when I use the word ‘skilled’, I am using it in the economist’s sense of the word: namely, to reflect the amount of formal schooling someone has under their belt. This is a very particular use of the term, and there is clearly a difference between it and a more commonsense use of the word ‘skilled’. That, quite rightly, often has very little to do with education at all. This does not mean either term is wrong, but it is important to be clear exactly what it is that economists have in mind to avoid causing any upset or offence.
Finally, remember that is not only activities that skilled workers do that tend to be relatively ‘non-routine’: it is also the case that many of the tasks done by people in lower-paid service roles are ‘non-routine’ as well. For instance, those who work in nursing or social work, or in restaurants and cafés, tend to draw on faculties like manual dexterity or interpersonal skill, and these have been hard to automate until recently as well.
A lot of those have become frontline jobs during the Covid crisis.
This is a very important point. It is a particularly troubling feature of the pandemic that many of these service roles, which have proven so hard to automate in the past, have been hardest hit by the virus. A corner of the labour market that one would have hoped to be an important source of job creation in the future has been completely decimated over the last few months.
I want to say a little more, though, about an earlier claim—that there are emerging limits to education as a response to automation. In A World Without Work, I describe two different ways that people might find themselves without work because of technological change. This distinction helps to pin down exactly why I worry about education. One way is what I call ‘frictional technological unemployment’; here, there is still lots of work to be done, the problem is that people are not able to do that work for various reasons. The other is what I call ‘structural technological unemployment’; here, there is simply not enough work to go around, full-stop. The problem with education is that it is an imperfect solution to the former, and a relatively impotent response to the latter. Let me explain.
The most familiar reason for frictional technological unemployment is that people don’t have the right skills for the available jobs. Here, more education seems like the appropriate response: we have to give people the skills to keep up in Goldin and Katz’s metaphorical ‘race’ with new technologies. My worry, though, is that policymakers do not always appreciate quite how tough an undertaking this race is for those who want to take part. To begin with, many people in this race are already running as fast as they can: as others have noted, it is very difficult to get more than 90 per cent of people in a country to finish secondary school, or to get more than 50 per cent of people to graduate from university. In turn, the pace of this race is accelerating: basic literacy and numeracy, for instance, might have been enough keep up in this race at the start of the 20th century, as people moved from agriculture into factories, or from factories into offices for the first time, but they are no longer enough. Ever higher qualifications are required to keep up. So that is the first limit to education: that it cannot always solve the skills mismatch that stops some people being able to take up the available work.
“If we find ourselves in a world where there are simply not enough jobs to be done, then we will be forced to consider more radical interventions”
Crucially, though, there are also other mismatches that might cause frictional technological unemployment: and education, at least traditionally understood, does not adequately address them. One is the ‘place mismatch’, that people simply don’t live in the same place that jobs that are being created. Then there is also an ‘identity mismatch’, where people have a particular conception of themselves, rooted in a particular type of work, and are willing to stay unemployed in order to protect that identity. To see this, think of adult men in the US displaced from manufacturing roles by new technologies. There are some that say they would rather stay unemployed than take up—and it’s a really unfortunate term—so-called ‘pink collar work’, a term designed to capture the fact that many of the jobs that are hard to automate are disproportionately held by women. So, 97.7% of kindergarten and primary school teachers, 92.2% of nurses, and 82.5% of social workers in the US are women. Again, as I said before, is not obvious how education addresses either place-mismatch or the identity-mismatch. This is the second limit to education.
The third and final limit to education is that it does not seem to be an effective response to structural technological unemployment. If we find ourselves in a world where there are simply not enough jobs to be done, then we will be forced to consider more radical interventions: a basic income, perhaps, or a job guarantee scheme.
So, why is this Katz and Goldin book so interesting? Because it captures, in a clear and elegant way, the prevailing view of how best to respond to automation: through more education. If new technologies are making high-skilled work more valuable and more important, if there is a ‘race’ on between people and machines, then we need to give people the skills required to keep up. But, as I have said, I think this only right up to a limit. A world with less work presents us with problems that more education alone cannot solve.
Next up on your list of books about the future of work is Essays in Persuasion by John Maynard Keynes and one essay in particular, “The Economic Possibilities of Our Grandchildren”. We’re ten years away from 2030, which is, I think, the year that Keynes had in mind when we would be living in blissful idleness. Tell us why you chose this book. I suspect that, although making the case that Keynes was wrong is easy, you’re going to tell us that, in some ways, he offered some important insights that were right.
That’s exactly right. The next three books are all about the challenges that we will face in a world with less work. This Keynes essay is one of the most famous bits of writing on the future of work. And what I think it captures so wonderfully—and this is the sense in which Keynes was correct—is that, in the 21st century, technological progress is going to solve ‘the traditional economic problem’ that has plagued our ancestors for centuries. If we think of the economy as a pie, as economists like to do, then that traditional problem has been, ‘how do we make the global economic pie large enough for everyone to live on?’
If you were to go back to the turn of the first century AD, take the global economic pie, and divide it up into equal slices for everyone in the world, most people would have got a few hundred of today’s dollars. Roll forward a thousand years, and roughly the same would have been true. Almost everyone lived on or around the poverty line. But over the last few hundred years, economic growth has soared, driven by technological progress. As a result, the global economic pie has exploded in size. Today, global GDP per head, the value of those individual slices of pie, is already approaching $11,000. As Keynes anticipated, we have come very close to solving the traditional economic problem, the struggle for subsistence that preoccupied humankind for so long. This piece of prescience is, in part, what makes his essay so engaging.
“I don’t think it’s a coincidence that worries about inequality are intensifying today at exactly the same time as worries about automation are growing”
But there is also an error of judgement that runs through Keynes’s essay. He was right that, in theory, we would come very close to solving that fundamental economic problem—that the economic pie would be large enough for everyone to live on. But he was wrong to think that, in practice, everyone would automatically get their slice. He assumed that, when our collective prosperity was large enough, all of us would be able to sit back and enjoy a life of leisure. But he never engaged with the distribution question: how is it that we will actually share out that economic pie? How do we make sure that everyone in society gets a fair slice?
Look around today. It is true that some people have big slices. But a great many others have very little, if anything at all. Keynes neglected this issue of distribution and I expect that it will be the great economic challenge of the 21st century. In decades to come, technological progress is going to make us more collectively prosperous than ever before, but we will need to find a way to share out that prosperity when our traditional mechanism for doing so—paying people for work that they do—is far less effective than it might have been in the past. Technological progress is likely to solve one economic problem, that traditional one of how to make the pie large enough, but it will replace it with another—how do we fairly slice it up?
Going back to what you were saying while discussing the first two books, it doesn’t look very hopeful that the labour market, or what’s left of it, will help to solve that distributional issue. Does that mean a much stronger role for the state and how could you get a consensus for the state to take up that role?
Let me make a couple of observations. There is sometimes a tendency to dismiss the idea of technological unemployment as a sort of distant threat that’s looming out of sight, that we don’t really need to worry about it now. But I think that’s a big mistake. I don’t think it’s a coincidence that worries about inequality are intensifying today at exactly the same time as worries about automation are growing. The two problems are very closely related. Today, the labour market is the main way that we share out prosperity in society. Most people’s jobs are their only source of income. The sorts of inequalities that we see in the labour market today show that this approach is already creaking; some people get far more for their efforts than others. Technological unemployment, in my view, is just a more extreme version of that same story, but one that ends with some people receiving nothing at all. So, this distributional problem is not new; today’s inequalities already present us with it. Technological unemployment will just create a more extreme version of it.
“Keynes neglected this issue of distribution and I expect that it will be the great economic challenge of the 21st century”
In a world with less work, as I mentioned before, we will have to grapple with the distribution question. And in my view, the only effective way to share out income in society if we cannot rely upon the labour market is, exactly as you say, through the state taking on a larger role. I call this the Big State. But this is not the Big State of the 20th century, with teams of smart people sitting in central government offices, poring over economic blueprints, trying to command and control economic activity from afar. It’s not a big state of production that I have in mind; it’s a big state of distribution. It’s the state being far more involved in sharing prosperity in society if the labour market is unable to do it effectively.
Now the question of consent. I haven’t said anything about the pandemic so far, but in thinking about the future of work, what has been so remarkable about the last few months is that we have found ourselves in a world with less work – not because the robots took all the jobs, but because this virus decimated the demand that so many of these jobs rely upon, and the interventions that have been required to contain the spread of the virus have made economic matters even worse. And we have had to confront exactly the distributional problem I mentioned before, ‘how do we share income in society when, for various reasons, we cannot rely upon the world of work to do it?’
What are we seeing in response to this problem? We’ve seen a Big State. Around the world, states have stepped forward in unprecedented ways to share prosperity in society. We’ve had the furlough scheme here in the UK, with about 10 million employees at one point having up to 80% of their wages paid by the state. And we’ve seen similar interventions elsewhere; sometimes grants and loans, sometimes other types of welfare and support. Ideas in my book which, only a few months ago, some might have dismissed as outlandish or infeasible—something like a basic income, for instance—have become completely commonplace today in thinking about how we respond to the current pandemic-induced world with less work. The last few months demonstrate that, when we face the distributional problem again in the future, a key part of the response to it will have to be through the state. And, given what we have seen over the last few months, I expect that people could be willing to support that.
We’ve distributed the income this time around, but we haven’t collected it yet.
That’s a nice way of putting it. We have borrowed a terrific amount: the UK is on track to set a peacetime borrowing record this year; the US has already borrowed five times what it did at the height of the 2007-08 global financial crisis. But, as you say, we haven’t collected it; we’ve just borrowed it. The question of how we’re actually going to pay for this remarkable expenditure is starting to bubble to the top of contemporary political conversation.
A big part of my new book is about this question. We can’t just continue to borrow indefinitely. We have to think about tax—who and what we ought to tax, and how much.
Again, just thinking very simply, if you have a very highly paid group of people at the top, a hollowed-out middle, with a relatively poorly paid lower section of society, you would expect—at least as an initial assumption—that if the income has got to be redistributed, it has to be redistributed from the top, more disproportionately than it is now.
That’s right, and that’s an important part of the story. One general principle is that we’ve got to tax the valuable capital wherever it is located in the economy, and then distribute the revenue to those who are without it. Part of that valuable capital is human capital, the skills and capabilities that people have. But there is also traditional capital, too; for instance, the systems and machines themselves. I’m not hugely in favour of the idea of a ‘robot tax’, but I think the spirit of the idea, that we need to tax whatever capital is becoming more valuable and important, is right.
Presumably the other thing to do is simply to find a way of distributing capital more equally. If labour incomes are increasingly unequal, then people can earn income from that. Or is that just saying the same thing in a different way?
No, I think that’s a different and critical aspect of the Big State. Its role can’t simply be taxing those who own valuable capital and sharing the revenue raised with those who don’t. It’s also about distributing that capital itself more widely in society. That’s why having a sort of citizens wealth fund for instance—something analogous to a sovereign wealth fund but held on behalf of citizens—is something I explore in the book. That might also be able to give people an actual stake in these valuable types of capital.
In a sense, that’s also the spirit of the Goldin and Katz book. It is about how, through education, we can share out human capital more widely in society. But, in the 21st century, if human capital is going to become less valuable relative to traditional capital, then finding ways to share out the latter is going to become more and more important.
The Keynes book is there, then, because it focuses our attention on how technological unemployment, in a strange way, will be a symptom of success. The success, as Keynes noted, is that technological progress is going to be more materially prosperous than ever before. But the strangeness, which Keynes neglected, is that it is also likely to replace the traditional economic problem with another one – how do we share out that prosperity if we can’t rely on the world of work to do it?
Let’s move on to Future Politics by Susskind—Jamie.
Yes. Not me.
You’re not related…
He’s my brother. I wasn’t sure if I was allowed to include it or not.
That’s completely within the rules. We’ve got no rules against nepotism, only egotism.
In the 20th century, our main worry about large corporations like these tended to be concerns about their economic power, about things like profitability, market concentration, and so on. But, in the 21st century—and this is what Future Politics is all about—our concern is going to be far more with their political power, their impact on issues of liberty, democracy and social justice—and whether they are under threat. Take Facebook. Yes, in part, people are concerned about their profitability and potentially predatory behaviour. But increasingly, they are concerned about their political power—that, for example, the Russian government was said to be able to buy advertisements to influence the US Presidential election in 2016. When I use the word ‘politics’ here, though, I don’t just mean politics in the narrow sense of politicians and great chambers of state, but in a broader sense—the ways in which we all live together in society, and whether the social scaffolding that we have erected to live on together is being dismantled.
“We need an institution analogous to a competition authority, but that’s responsible for policing political power rather than economic power”
Future Politics is all about this threat. In the 21st century, it rightly points out that what matters is who owns and controls these increasingly capable technologies, and the political power that they wield as a result. And I don’t think any conversation about the future of work can leave out those organisations that are responsible for developing many of these technologies in the first place. So, the first challenge is the economic one, the distributional challenge. And the second one is this challenge of the political power of large technology companies, and that’s what this book is focused on.
How, specifically, does the political power, or influence, of these organizations relate to this issue of the future of work we’ve been discussing?
Because it’s the same technologies that displace workers from their work that also have these political consequences. Take driverless cars as an example. Looked at from an economic point of view, people might worry about the consequences for the several million truck drivers in the US. But at the same time, we might also look at it from a political point of view: what are the implications for liberty, for instance, of a car that cannot go above a certain speed, or park on a double-yellow line, under any circumstance at all.
Now, you might think that’s a relatively innocuous example. But what about the automated soap dispenser that is said to be unable to recognize black hands when they are put underneath them because they’ve been trained on data sets of white people’s hands. That raises a vital issue of social justice. And we should worry if future technologies, developed in this way, are used elsewhere in the labour market: determining parole decisions, for instance, or screening job candidates. The future of work isn’t just about the economics of these technologies; it’s also about their politics, too.
Does the book suggest any kind of remedies? I was just wondering, might you develop justifications for anti-trust cases on political grounds rather than just economic ones?
That’s exactly right. And that’s what I propose in my own book. We need an institution analogous to a competition authority, but that’s responsible for policing political power rather than economic power. And I suppose one of my worries—and given I am an economist myself it might sound like I’m shooting myself in the foot—is that these conversations about large technology companies tend to be dominated by economists. Yet the tools that competition authorities, and the economists that populate them, have to think about issues like pricing and profit, however insightful they may be, tell us very little about issues like liberty, democracy and social justice and whether or not those things might be under threat. So, I think we need a very different type of institution, staffed by very different types of people – training in moral philosophy, for instance, or political science – to tackle what are often political, rather than economic, problems.
Let’s move on to the last book, Marienthal. Marienthal is a place where there was prolonged unemployment and there was a very detailed study done on the attitudes of the people who were long-term unemployed. This book is the fruit of that study. Is that right?
Yes, that’s right. Marienthal is a small village outside Vienna, founded in the 1830s to provide homes for people employed in a flax mill that had been built nearby. And it grew and grew in the decades that followed. But then, in 1929, the Great Depression hit. The next year the factory closed down. In 1932 three-quarters of the 478 families in the village had nobody in work and were relying just upon unemployment payments for an income. And Marie Jahoda, the lead researcher on this project, wanted to know what the impact of such widespread worklessness would be.
It’s a fascinating book in part because their methods were remarkably unconventional by today’s standards. They wanted to collect data on residents without the residents realizing that they were being watched. So, they just embedded themselves in everyday life there. They opened a clothes-cleaning and repair service, various sorts of parental support classes, a free medical clinic, courses in gymnastics and pattern design and so on, just to immerse themselves, in as innocuous a way as they could, into everyday life in the town. So it’s an intriguing read, in part because of the peculiar methodology.
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What they found was also very striking: a growing apathy and loss of direction in life, increasing ill-will among the unemployed towards others. People borrowed fewer library books, dropped out of political parties, stopped turning up to cultural events. There were even some more granular things: researchers who stood at street corners supposedly noted physical changes in the way that unemployed people carried themselves. Men without work supposedly walked more slowly in the street and stopped more frequently.
The reason these observations matter is because it’s so often said that work is not simply a source of income, but also a source of meaning and purpose in life. And, if that is right, then the threat of technological change isn’t simply that it’s going to hollow out the world of work, but it might also hollow out the sense of direction, purpose, and fulfilment that people have in their lives, too. And this book is an idiosyncratic but insightful account of exactly that problem.
It’s interesting that the things you say they stopped doing when they became unemployed were exactly the sorts of thing Keynes hoped we would all start doing once we no longer had to work and could live a life of leisure. Clearly work can give a huge amount of meaning and dignity to people’s lives that has nothing to do with meeting their material needs.
This book is, for me, a starting point. I treat it as a provocation for thinking about the relationship between work and meaning. What I argue in my own work is that, actually, as you’re suggesting, that relationship is far murkier than we might commonly suppose. If you look at the data today in the US, almost 70% of workers are either not engaged or actively disengaged from their work; only 50% say they get a sense of identity from their job. In the UK almost 40% of people think their work does not make a meaningful contribution to the world. So, there’s a lot of heterogeneity today; some people get meaning and purpose from their work, but others do not. And it’s also the case that, over time, this relationship between work and meaning has changed tremendously. I’m quite interested in how, in the ancient world, work was often thought of very differently. In Thebes, the ancient Egyptian city, you were banned from seeking office if you had been engaged in trade over the previous ten years. In Sparta, the warrior-city state of Greece, citizens were banned from productive work by law. Both Aristotle and Plato thought that work was a prohibitively grubby affair, and argued that meaning and purpose could only come through certain types of leisure.
I think the French aristocracy before the Revolution were banned from getting involved in any retail trade. Wholesale was OK, but retail tainted their nobility.
Again, it goes to this point that the relationship between work and meaning is a murky one. But in a world with less work, the nature of this relationship becomes particularly important, and we need to think more carefully about it.
I think we’ve seen evidence of this during the pandemic. Over the last few months, there has been a fairly unfamiliar public conversation about how we ought to best spend our time in the enforced idleness we have found ourselves in under lockdown. The point I am making in my book, and which I think has been borne out by the inconclusiveness of much of this new public conversation, is that while many of us have a sense of what gainful employment looks like, we do not have an equally good sense of what gainful unemployment looks like.
I suppose, if the distributional question could be settled, you’re then divorcing activity from income, in a way. And, so, perhaps we can divorce work from income, in the sense that there will be a list of things that need to be done, that might give meaning to one’s life, but which might not provide an economic return. But people could do those things because a redistributive big state will give them a return.
This gets at something very important. Take a basic income as an example of a mechanism that disentangles work from income, in the sense that you get an income independent of your status in the labour market. That, as you say, solves the distribution problem, by providing us with a way of sharing prosperity in society. But what it fails to solve is what I call the contribution problem, which is a need to create a shared feeling that everybody is pulling their individual weight and paying into the collective pot. In a world with work, that sense of social solidarity often comes from a feeling that everyone is making an economic contribution to that pot, that everyone is paying in through the work that they do and the taxes that they pay. If they’re not working, but they are able to work, then there is an expectation that they actively look for work or retrain and re-skill and try and find work.
My worry about a basic income is that while it solves the distribution problem, it doesn’t engage with the contribution problem. This is particularly true for a ‘universal’ basic income, which is a basic income given to everyone with no strings attached. And that is why I’m interested in the idea of a conditional basic income. It is still a basic income, but it comes with certain conditions attached. What sorts of conditions? Well, as a society, if we take seriously the threat of a world of less work, we need to think about what sorts of non-economic contributions we might ask people to make to the collective pot. What activities do people think are socially valuable and important, but might not necessarily receive a wage in the labour market? In Britain, about 15 million people volunteer regularly, half as many people as there are in paid work. Indeed, the Bank of England estimates that this volunteering is worth £50 billion a year, making it as valuable as the energy industry. We recognise that this work is often hugely socially valuable, even though it doesn’t receive a wage in return. In a world with a basic income, why not recognise it more formally?
“My worry about a basic income is that while it solves the distribution problem, it doesn’t engage with the contribution problem”
So, when we are thinking about how people might spend their spare time in a world with less work, I imagine that part of it will be spent doing as they see fit. But we will also—just as we do today—expect that people spend some of their time contributing to the collective pot, even if it is in a non-economic way.
Work as a public good.
Yes, or work for the public good. There is an opportunity here. During the pandemic, many people have noted the striking mismatch between the great social value of the work that so many key workers do and the wage that these workers receive. And so, there is a chance here to think together about what activities we want to recognize as being valuable contributions to the collective pot, to hold up some of these activities that, while they might not receive a big return in the labour market, we nevertheless think of as socially vital.
So, everyone will have to make some sort of contribution.
I expect so, though what is interesting is that, during the pandemic, this has not really been the case. There has been a strong sense of social solidarity in facing down the virus together. And that is partly why in the UK—quite rightly—there does not seem to be a sense of grievance from those working for a wage towards furloughed colleagues who are receiving a wage without working. The question, though, is whether this sort of arrangement could continue indefinitely in response to the threat of automation instead. My fear is that it is only feasible in the short run. In the longer run, I worry that it would offend some people’s sense of social solidarity, if some people are receiving a wage and not working. And so again, that’s why I think engaging with the contribution problem is so important.
So those are the three problems: the problem of inequality, of how to share income in society when we cannot rely on the labour market to do it; the problem of power, of what to do about the growing influence of large technology companies; and the problem of meaning, of how to provide purpose and direction to people in a world where work may no longer sit at the centre of their lives. These three final books are so important because they provide us with a glimpse of these problems, and how they will trouble and test us in decades to come.
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Daniel Susskind is a Fellow in Economics at Balliol College, Oxford University. He is the co-author of the best-selling book, The Future of the Professions, and the author of A World Without Work(January 2020). Previously he worked in the British Government as a policy adviser in the Prime Minister’s Strategy Unit, as a policy analyst in the Policy Unit in 10 Downing Street, and as a senior policy adviser in the Cabinet Office.
Daniel Susskind is a Fellow in Economics at Balliol College, Oxford University. He is the co-author of the best-selling book, The Future of the Professions, and the author of A World Without Work(January 2020). Previously he worked in the British Government as a policy adviser in the Prime Minister’s Strategy Unit, as a policy analyst in the Policy Unit in 10 Downing Street, and as a senior policy adviser in the Cabinet Office.
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