Unequality: When inequality changes, our strategies must too
This is the third in a series of three essays for a project with the Joseph Rowntree Foundation, Social justice in a digital age. The first essay explored platform capitalism and the second focused on care in a high-technology society.
The more I grapple with big challenges in public policy, the more I think our language holds us back. We use the same words year after year — abstract nouns like inequality, consumer choice, or the Republican Party — even as the subjects of these words change beyond recognition.
It’s a phenomenon I call semantic drag — our words are fixed, their subjects drift — and I think it explains a lot of our public policy woes.
In politics, it means our debates get left behind by reality, like a couple so lost in argument they don’t realise everyone else has gone home.
In policymaking, meanwhile, it leaves us acting like a person staring at a nail as it morphs, ever so slowly, into a screw. Deep down, we sense that something has changed, but we still call the thing in front of us a nail. And since we still have that degree in Nail Studies, and we’re holding a hammer, and our hammering technique is well-practised, we just stand there, year after year, whacking away.
Nowhere is semantic drag more of an issue than with one of the biggest social challenges of all: inequality. Over the last two decades, the way inequality manifests in our society and economy has changed, yet our conception of what inequality is, and of how it works, has stayed essentially the same.
For progressives, this widening gap — between our idea of inequality, and inequality as it is now, out there in the world — should be a major concern. It means we apply statistical constructs and mental models that misrepresent the most pertinent features of inequality; and we brandish the wrong policy tools. We use redistribution to compensate for market dysfunctions that can only be addressed at their source. We design policies that alleviate some aspects of inequality but exacerbate others, prioritising material resources over the relational inequities of status and esteem that now so contort our society. And we misfocus our efforts, sidelining policies like adult skills and housing that should form the core of a contemporary anti-inequality agenda.
It feels timely, then, even urgent, to look at inequality afresh. To take this old idea in our hand and turn it over, examining it with a renewed curiosity — and rethinking the institutional settlement it requires.
But how do we look fresh at something so familiar? How do we escape semantic drag? At root it’s an issue of language and referents, so maybe a new word can help.
The American linguist, Arika Okrent, explains how the words we use can root us in the past — or liberate our thinking. She contrasts the prefixes ‘in’ and ‘un’, observing how “un” words tend to be “freely productive” while ‘in’ words often carry “layers of connotation … from a long history of use”, The prefix ‘un’, she says, “can apply to new words … but “in- remains frozen in the existing vocabulary, a Latin dinosaur bone.”
So in this essay I want to try an experiment. We all know what inequality is, but what about ‘unequality’, defined as the salient forms of economic injustice that arise from capitalism in its early-to-mid 21st century form?
What shape does unequality take? How does it manifest and behave? And, once we can see the problem that’s now in front of us, and put our hammer to one side, what policy tools should we reach for?
Part I: The shape of unequality
Unequality (noun); The salient economic injustices that arise from capitalism in its early-to-mid 21st century form.
Imagine two societies that are identical in the way wages are distributed between people but different in the way wages are distributed between firms.
In the first society, wages vary mostly within firms. There are lots of thriving companies, all moderately unequal, and the ranking between them changes year by year. Within each firm, wages climb a gradient: the company’s cleaners earn less than the administrators, who earn less than the managers, who earn less than their bosses down the hall. People nonetheless share all the rituals of office life: the morning coffee run, the Christmas do, the awkward chat in the lift.
In the second society, wages vary more between firms than they do within them. At the bottom of this society, there are some struggling firms — sluggish and old-fashioned — and there are some profitable but low-paying firms — warehouses, cleaning agencies, fleets of delivery vans. At the top, there is a high-paying elite — design agencies, digital platforms, and law firms — and the membership of this elite changes little year by year. In this world, low paid people still meet high paid people, but they do so mainly when they deliver them food, or cut their hair, which are things they do a lot, since much of their work is in personal services and care.
In the last two decades, the world’s mature capitalist economies have gone from the first society to the second, with wage gaps widening between firms. In one typical study, based on a dataset of 2 billion years of work in 50 million workplaces in 14 high-income countries, the authors found that ‘the share of inequality that is between workplaces is growing in 12 of 14 countries examined, and in no country has it fallen’. Hence an oddity of unequality: in a world of historically unequal wages, the organisations we work for are each getting more homogenous.
Why are we being sorted in this way? There are two main drivers at play.
First, wage gaps are rising because productivity gaps are rising. Since around the mid-1990s, the economy’s top performing firms have taken a bigger share of profit, leaving the average firm gasping for air. In one study of the world’s 6,000 biggest firms, the top 10% of firms were found to make 80% of all global profit and the top 1% over a third (36%), while the middle 60% of firms made ‘near-zero’.
Second, wage gaps have risen more than productivity gaps; the link between wages and productivity therefore also now varies more between firms. In other words, it’s not just the performance of firms that’s diverging; wage-setting policies are too. This in turn reflects the separate worlds that workers at the top and bottom now live in. While low-paying firms squeeze every penny from pay, high-paying firms bid for talent as if they’re at Christies. So as well as making more profit, top firms also share more of their profit with workers, in part through pay and in part through conditions; just as Google rolled out a free birthday massage, RyanAir banned its staff from charging their mobile phones. It means our experience of work, and the status it confers on us, differs more than it did before. And it means the firm we work for now matters more than it did, relative to our personal characteristics.
If we track these developments back to their source, the trail takes us deep into the logic of contemporary capitalism. Interfirm wage gaps have risen the most in sectors that are more global and in sectors that are more heavily digitised. And economic analysis tells us why.
Studies show that superstar or winner-takes-all effects are a recurring feature of global digital markets. We also know that this isn’t just a story of stellar performance; the profits made by top firms are higher and more sustained than we’d expect in a competitive market, suggesting market power. Markups between prices and marginal costs have risen — again unevenly — as top firms make wide margins while average firms scrape by. In one major study of two of the world’s biggest superstars, Google and Facebook, the UK competition authority estimated Google’s return on capital at 40% and Facebook’s at 38%, (compared to 6% and 6.5% for old-economy companies like Tesco or BP) — and both figures had stayed above 30% for years. The CMA concluded that Google and Facebook have secured ‘entrenched market power’ on the basis of forces that are ‘wide ranging and self-reinforcing’.
As tempting as it is to focus on big tech, however, this is a wider story. So-called ‘displacement hazards’ — moments when a top firm is knocked off its perch — have become far rarer since 2000. Startup activity has also fallen, cutting off a source of future disruption. This all suggests that we’re not just being sorted into high- and low-paying firms; the strata are calcifying too. And as Jonathan Haskel and Stian Westlake show in their work on the intangible economy, these changes again arise from the way we generate value in today’s economy. It’s hard to dislodge a firm like Google or Starbucks, fortressed as they are by clusters of mutually-reinforcing intangible strengths, from brand, to software, to organisational capability. That’s why intangible investment leads to ‘greater persistence and reduced leapfrogging’ and it’s why technology — contrary to its image as a disruptor — ‘now appears to help suppress disruption.’
Finally, there’s one last force of division at work: firms are themselves changing shape, retrofitting into this new reality. Among old firms that predate the platform economy, low-paying functions have been carved out like tumours. Apple is a case in point; its products are proudly ‘designed in California’ and rather less proudly built under contract in China. For internet-era firms, meanwhile, the low-pay/high-pay distinction runs to a deeper, more conceptual level. For a company like Uber, low-paid workers aren’t employees at all; they’re partners that happen to use the company’s platform. It’s an act of separation or cleaving that one book calls ‘dualisation’ and another ‘the fissured workplace’, sharpening an ‘insider-outsider divide’. The Uber driver won’t meet the Uber coder, unless they happen to book a ride.
All of which gives unequality its first salient feature: people aren’t drifting apart like dust, they’re congealing into clots — some of hope and vitality, others of worry and despair.
It’s a subtle but culturally significant shift and one that doesn’t show up in our main statistical measures of inequality; the Gini coefficient and the 10:50:90 ratios of income distribution have been broadly flat for years. Behind these uneventful statistics lies a story of division and sclerosis. We’re entering a world of higher stakes in which key moments (Do you get the job in the top firm? Do you secure the university place?) decide whether you’re in, or out. And it’s a world in which our divisions aren’t just financial but social and cultural, as the shared rituals of our working lives fracture and we spend more of our time within tribes.
Still, while the role of the firm is important, it accounts for only part of the change we’ve seen. For a fuller picture, we need to consider another factor: the geography of a digital economy.
It was the promise of the internet: place will matter less. In the future, we’d work from anywhere, see the world from the comfort of our homes. In the last generation, we’ve watched that promise turn into one of the greatest ironies of the knowledge economy. It turns out that, in a digital age, where you live matters more.
Whichever way you cut the digital economy, you see place running through. Investment at the frontier of technology is more spatially concentrated than ever, with the top ten technology clusters in America now accounting for over 90% of Venture Capital (VC) investment, despite being home to a quarter of the US population. San Francisco, with 2.5% of the US population, accounts for nearly half (48%) of VC investment.
We might think it was ever thus, and to an extent that’s true. As economist Arthur C. Pigou once wrote, ‘it’s all in Marshall’, and technological clusters were covered by Marshall too. Pigou was referring, of course, to Alfred Marshall, the towering English economist whose work prefigured so much of 20th century economics. In 1890, Marshall explained that cities foster innovation because, when experts cluster: ‘The mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously.’
Yet while clusters are an old idea, the shift to a digital economy has made dense city living even more valuable. Innovation has become more spatially focused, so that nearly a fifth (18.4%) of US patents are now filed in San Francisco, seven times the city’s share of the US population, while three quarters (77%) of US inventors live in the ten biggest US semiconductor clusters, all thriving cities. This link between cities and innovation isn’t just correlation, it’s causation. Studies show that when an inventor moves to live near other inventors in their field, they go on to secure more and better patents. Economists estimate that a 10% rise in population density equates to a 2% rise in patent intensity. It’s as if the shift to an intangible economy has made the dust of innovation somehow lighter, so that it lingers longer in the air.
Spatial clustering is key to understanding unequality because the geography of innovation soon becomes the geography of opportunity. Data shows that high-skilled jobs became far more spatially concentrated between 1998 and 2019 and this, in turn, meant the elite packed in tighter too. London is now home to 35% of the UK’s top 1%, around three times the city’s share of the UK population. It means today’s thriving cities aren’t a long list as they were in the postwar decades, when urban centres from Detroit to Michigan created millions of jobs. Today’s frontier is concentrated into a short list of intensely wealthy global megacities: places like London, San Francisco, Beijing.
If the story ended there, it might not be so bad. After all, the story of progress has long been the story of the bustling city, evoking that classic tale of social mobility: a person moves to the big smoke with a battered suitcase in their hand, a dollar in their pocket, and a dream in their eyes.
In the last 20 years, however, this dream shimmered into a mirage for one reason above all: urban house prices soared. The clustering of digital tech coincided with the drying up of land in cities — or, rather, of access to that land, via planning — pushing up house prices like walls around a castle.
Since the year 2000, the average price of a home in London has risen nearly 300% from £132,705 to over £500,000. In San Francisco, prices are up 290% from $356,800 to $1.4 million. The soaring price of housing drove the biggest windfall of unearned wealth in history, dramatically increasing the ratio of wealth to income, which on its own is changing the character of our society. But rising house prices in cities did something else too; they kept people away from the frontier, turning tech clusters into elite membership clubs. And now, even after the rise of remote work, urban rents are soaring again.
High house prices in cities accentuate unequality. They make it hard for firms to access talent, explaining sky high salaries. They keep the elite homogenous, limiting good jobs to people whose parents can underwrite their rent or deposit. They even explain the frantic character of the digital economy, which is so starved of workers that today’s elite are in the historically unusual position of working longer hours even as they get richer. It all explains one of the most telling facts of our times: in the midst of history’s biggest ever gold rush, the home state of the gold mine, California, is seeing its population shrink.
So as well as sorting us by organisation, unequality sorts us by place. And this spatiality — this placeyness — explains why unequality has such a pronounced social and cultural flavour.
Place has an absorbent quality; it acts like a wick for injustice, spreading it across domains. That’s why, as economic opportunity has become more geographically clustered, other social outcomes have too. Over the last generation, health economists have watched in horror as gaps in health outcomes have yawned open, so that people in the UK’s most deprived areas can expect to live 19 fewer years of healthy life than people in the richest areas. Meanwhile educational outcomes have diverged between urban and rural areas, so that a map of the best state schools — particularly in Britain, thanks to London’s thriving schools — now looks a lot like a map of the highest-skilled jobs. Two decades into these trends, the resultant feedback loops are in full flow. Better jobs in a local area make for a healthier and better educated population, which draws in better jobs. Elsewhere, of course, we see the opposite: a death spiral of lost jobs and declining education, health, and hope.
All of which speaks to a central question: how does unequality feel? The answer is that it feels like we exist in two distinct countries, side by side.
These cleavages are partly a question of money, but they go deeper than that. In a technological revolution like the one we’re living through now, the distinction between the vanguard economy and the laggard economy isn’t just a distinction of rich versus poor, or of high-paid versus low-paid jobs; it’s a distinction of the new versus the old — a difference not of quantity but of kind.
You can see this if you visit workplaces in technology clusters; they’re populated not by better versions of jobs you find elsewhere, but by wholly novel forms of work. Novel not just because the work is done by new roles — UX designer, product lead, delivery manager — but because of the new norms, practices, and organisational forms within which the work is done. From agile methods to devops culture to the broader aesthetic and ethics of the tech sector, thriving clusters aren’t just different because of their income and wealth; the air feels different too.
This explains why, in the last two decades, we’ve seen that uneven map of economic opportunity become something altogether more emotionally freighted. It’s become a map of esteem and of status; of whether people feel part of the future or not; a map of the extent to which people feel magnified and invigorated each time they engage with the economy, or belittled and bored.
The geography of unequality gives a fuller sense of why our society now feels the way it does. And it shows why, even if we hold the income distribution constant, our society still feels less fair than it did before.
But income distributions aren’t held constant; they’re changing too. So we’ll finish our examination of unequality in the place where a debate about inequality normally starts — with some distributional charts.
It’s one of the most memorable of all political exchanges on inequality: Margaret Thatcher stands at the dispatch box in the House of Commons, in her element as she lambasts the left:
What the honourable member is saying is that he would rather that the poor were poorer, provided that the rich were less rich. … So long as the gap is smaller, they would rather have the poor poorer. You do not create wealth and opportunity that way.
Thatcher acted out her words, using her hands to mark the rich and the poor and the gap between them. It was the rhetorical essence of trickle down economics; the way to make the poor less poor is to allow the rich to be rich, so that money and opportunity can flow.
Whatever we think of the argument, it felt plausible enough at the time for the metaphor to stick — and the stats show why. The 1980s were a period in which Britain saw inequality soar, leaving disfiguring scars from de-industrialisation. Yet this painful story played out against a backdrop in which most people got better off — especially the politically important middle-class, the 50th-90th percentile home of Mondeo Man.
Charts 1 and 2 show how starkly things have changed. And they explain why anyone attempting a Thatcher cosplay in the 2020s comes across not as strong, or as callous-but-tough, but as just plain weird.
The chart on the left shows the UK distribution of income growth in the 1980s, a time of moderately rising living standards in which the richer you were, the better you did. The chart on the right shows income growth in the 2010s , a time when living standards stagnated for most people while a tiny elite did well. Even this downplays the situation because top incomes tend to be underreported. When you bring in more granular data sources, you see that in the US, the top 1% now account for a stunning 22% of total income and the superelite of the 0.01% accounts for an even less proportionate 5%.
Inequality vs. Unequality: The slope and the hockeystick
Some will say I’m simplifying the story, and they’re right. For a full account of changing income distributions we’d need to differentiate families from individuals (thanks to compositional effects, inequality has risen more between families than between individuals). We’d also need to look at incomes before and after housing costs (after housing costs, income inequality looks worse, and the housing boom has also pushed up the wealth to income ratio). Finally, we’d need to consider changes in working hours (in an historical anomaly, today’s elite are working longer hours even as they get richer, as low earners see their hours fall, so annual earnings gaps are now especially unequal).
Still, the point stands: the distribution of income growth has gone from an upward slope to a hockey stick laid on the ground. And so unequality looks distributionally nothing like the unfair and scarred yet dynamic economy of Thatcher — the economy that made New Labour strike its famous compromise with the rich.
All of which nearly rounds out our view of unequality; as well as being divided and spatial, it’s exclusive, elitist, and stagnant. But unequality also has one other essential feature: it’s stunningly homogenous. And this final characteristic feeds back into and amplifies the others.
In sociological studies of the top 1%, the group’s most striking feature is their sheer sameness. It’s not just that the 1% is made-up disproportionately of white, straight, able-bodied men; it’s also that ethnographic studies suggest the elite shares a common language and worldview, and take very similar paths to the top.
The homogeneity of today’s frontier is perhaps its least novel feature, and its most depressing. But what seems new are some of the forces by which this homogeneity is being reinforced.
For one thing, there’s growing evidence that access to finance has become more exclusive in the digital age. Because it’s harder to secure a business loan against intangible assets, more of today’s founders get their start by borrowing money against their home, or by running a friends and family round. Then, as start-ups scale, financing via Venture Capital worsens the problem. In the UK, for example, around 89% of all VC funding goes to all-male teams and just 1% to all women teams.
Things aren’t much better for people climbing the ladder as employees, either; for them, education plays the same elitist filtering role. Earlier in the digital age, we had hoped that the doors to opportunity would fling open as people learned for free on YouTube. Instead, the educational bridge into top jobs seems to be getting narrower.
In technology roles, it’s common that firms see the best coder or designer as 10 times better than the average — hence those sky-high top salaries. Yet these intangible skills are also difficult to measure, so it’s hard to spot who the superstars are. The result is a world of cultural codes and conversations about ‘team fit’ that are fertile ground for bias. To help solve this issue, elite employers now often turn to postgraduate degrees to differentiate applicants. Yet studies show that many postgrad degrees are valuable only as signals. That MBA, with its $50,000 price tag, doesn’t teach you much that you can’t learn online, but it does something YouTube can never do: it tells your elite prospective employer that you’re one of them.
The homogeneity of the elite is unfair, of course, but what’s more striking is the growing evidence that it’s also an economic disaster. Studies show that cognitive diversity is vital for innovation. Adding perspective to a team is like buying tickets to the innovation lottery; it gives you more chances to win. Diverse institutions are also more resilient, being less prone to error and groupthink. And so as the latest tech bubble deflated in 2022, the homogeneity of the frontier came under scrutiny. From the pipe dream of self-driving cars — $100 billion invested, little delivered — to the grand unveiling of Elon Musk’s humanoid robot which, well, just wasn’t very good, people are starting to wonder if ‘things men remember finding cool when they were kids’ might be a bad way to direct humanity’s most powerful capabilities.
The texture of unequality
By looking at unequality from various angles, I’ve tried to develop a sense of the whole. Divided, spatial, elitist, sclerotic, and homogenous; they’re all characteristics of unequality. But these characteristics also feel to me like more than a coincidence. The whole thing has a kind of aesthetic coherence, a unity of form.
I think this coherence comes partly from the way these characteristics interrelate, accentuating each other. Spatial clusters make it easier to sustain wide wage gaps between firms; high house prices keep the elite homogenous; a lack of diversity starves the frontier of talent, holding back innovation and creating a frantic vibe. Maybe there’s also a sense of coherence because so many of these features share the same root cause; they grow from the logic of digital capitalism, fertilised by policy failings like housing and skills.
Most of all, though, I’m struck by the consistent vibe that unequality gives off; the way it has such a distinctive experiential texture. There’s that eerily dystopian, Ballard-esque glow that emanates from places like Silicon Valley. There’s the weirdly frazzling combination of us all feeling burned out and yet getting nowhere together, as if the doom scroll has become our own mental hamster wheel. And of course there are all those ominous, festering cultural and political divides.
Where does this all leave us? I think it leaves us with a more rounded sense of what unequality is. Unequality is what happens to a society when intangibles become the main way to generate value — whether the value is money, power, or esteem — and yet most people are unable to participate in these forms of value generation.
And that, in turn, tees up the question we need to answer with public policy: what would it take to open up access to the digital economy, so that more people can participate in — and share in — its potential?
Part II: Policy implications
As we turn to policy implications, there’s a risk we’ll snap back onto old tramlines of thought. So to keep us focused, I want to frame this section with a question: what kind of problem is unequality?
Here’s the thing I find most striking about unequality: it’s not just unfair, it’s also inefficient. By which I mean unequality isn’t just bad because it’s inequitable; it’s also bad because it chokes off productivity and growth.
An economy with high unequality is far less productive than it has the potential to be. And it’s not just superficially unproductive, in a way that can be corrected later. It’s unproductive down to its bones.
More than this, a society with high unequality is unproductive largely because it’s unfair. It hands the vanguard of productive practice to a tiny and homogenous elite made up mainly of men who think they’re smarter and more rounded than they are. As a result, it is sclerotic; it’s disfigured by allocative inefficiencies; it’s fragile; and it wastes human potential in a way that is as uneconomic as it is inequitable.
I might go so far as to say that this ‘both/and-ness’ — that unequality is both inequitable and inefficient — is its distinguishing feature, making it different — at first by degree, and later in kind — to inequality.
Why does this matter for policy? It matters because our whole policy response to inequality was built on the premise that it wasn’t ‘both-and’, it was ‘either/or’. That is to say, with inequality, we pursued efficiency and equity as if they were separable policy goals, defined by the tensions between them, and this set the terms of our approach.
In fact, we went further than this. We treated efficiency and equity as if they were not just separable, but sequential, in that our approach was essentially compensatory. First we regulated markets, making them efficient but unequal. Then we smoothed things out with the redistributive tools of a social democratic state.
This framing mattered because it shaped the work we did. It meant the core of our anti-inequality agenda was redistributive social policy. And it determined a division of labour. Anti-poverty charities and some parts of the state (in Britain, DWP and parts of HMT) cared about redistribution, while other parts of the state (economic regulators, BEIS, and other parts of HMT) and economic think tanks cared about productivity and growth. Each side had an internally coherent language, framings, and policy tools, and they struggled to talk to each other.
With unequality, this two-pronged approach won’t work at all well. Its both/and-ness makes possible — indeed necessitates — a more integrated response. We need a unified account of unequality that combines its unfairnesses and its inefficiencies. And we need a unified policy agenda, delivered with one language, conceptual repertoire, and set of policy tools.
What could this look like? Needless to say, I don’t have space for a full manifesto here. In fact, my point really is that this project — constructing an institutional settlement to fight unequality — will prove to be a defining task for 21st century progressives, absorbing the work of many people over many years.
Still, in the spirit of this project, let me throw down some ideas for people to critique. So here’s a starter for ten — a unified definition of the problem of unequality and five policy implications.
A unified problem, a unified response
We’re now a quarter century into the internet age, and we understand better how a digital economy works. It turns out that if you govern digital capitalism using outmoded pre-digital institutions, a small and homogenous elite — of people, cities, and firms — comes to monopolise the vanguard of practice, choking off growth and making society sharply divided. Soon a tiny minority of superstar firms and cities pull away from the rest, as a long tail lags years, in fact decades, behind the frontier of what’s possible. Then aggregate growth peters out because, however frantically it whirs, the elite new economy can’t pull the old economy along. And, before long, this split — the new vs the old — becomes the main schism of politics.
This is a pickle for sure. But it’s a very different pickle to the one we were in after the 1980s, the last time concern about inequality powered a wave of progressive reform. Back then, our challenge was to redistribute the gains of a fairly vibrant but deeply unjust and scarred economy. Today, we need to resuscitate a stagnant economy by broadening participation and accelerating the diffusion of practice. We need to make our economy more dynamic by making it more inclusive.
Public policy can help us do this in lots of ways, but here are five policies that must surely sit at the heart of our response.
First, we can tackle anti-competitive dynamics in digital markets, stopping firms from accumulating abusable market power and minimising winner-takes-all effects. These dynamics arise from deepset characteristics of the digital economy, from the network effects of software, to the self-amplifying power of big data, to the spill-overs of intangibles. This means our policy response must also run deep. It’s not enough to break up individual firms, or to crack down on individual behaviours. We need to change the institutional environment in which digital markets sit, in order to change how these markets function. Thankfully, there’s an energetic debate underway about how to do this. It includes ideas like requiring that major platforms be interoperable; forcing big platforms to open up their data for researchers and entrepreneurs to use; and reclassifying the biggest platforms to recognise them as natural monopolies of unprecedented power.
Second, we can make it easier for people to try their hand in the digital economy. We can make business finance easier to come by so that entrepreneurs don’t need to rely on the elitist institution of the friends and family round. More broadly, we can do more to help diffuse new practices across the laggard economy, supporting the long tail of small businesses to apply internet-era technologies well.
Third, we need cheaper housing so that people can live in thriving clusters. The digital economy will remain elite, and will continue to suffer from chronic skills shortages and eye-watering wage premiums, all the time people can’t get near the goldmine. As others have written, housing is so central to our contemporary predicament that it comes close to being a theory of everything. So this is an area where policy should be bold and tear up the rules (in planning, sometimes literally). We need to build game-changing amounts of very cheap housing near technology clusters and open up access to this housing for people who would not otherwise be able to afford it.
Fourth, we need to take adult skills policy out of the boring cupboard to make it the keystone of a radical progressive agenda. If anything speaks to the economic madness of our present situation, it’s the way millions of smart, committed people work every hour they can get in low-paid, low-productivity jobs, at the same time as the digital economy is so starved of talent that it burns through its people until they collapse, and struggles to hire talent no matter how much companies pay.
Adult skills is one of those policy areas where all our old paradigms have failed. It doesn’t work to expect individuals to fund their own skills; try telling a single parent to quit their low-paid job and take a loan for a course because academics find positive wage returns to level three skills. It doesn’t work to ask yesterday’s legacy employers to fund the skills of tomorrow, as evidenced in long-running declines in on-the-job training. And it doesn’t work when the state tells people what to learn through a bums-on-seats approach. So as I argue in my book, End State, we should emulate a policy from the past: we should make a generation-defining investment in adult skills on the model of America’s postwar GI Bill. In Britain, this would mean fully-funding four million adults — today’s equivalent to the scale of the GI Bill — to learn the skills of the future, filling the halls of our most elite educational institutions with people who would otherwise be shut out.
Fifth, welfare. You might think, from what I’ve written above, that I’m going to argue for a smaller role for redistributive welfare in the future. That we should fix markets upfront and lean less on redistribution after the fact. Actually, I think history pulls us in the other direction. In any plausible and palatable future, we’ll need to do more to redistribute incomes than we do today.
There’s no escaping the fact that, even if we succeed in making the digital economy less winner-takes-all, it will still be winner-takes-more. There’s no reason to think this reflects changes to the distribution of talent, or effort, or of any other measure of intrinsic worth. This change is happening because the economic environment in which we work is changing; specifically because digital technologies and intangibles are massively scalable, and the gains flow back to the small group of people who own and run them. So unless we’re happy for ever-rising wage gaps to become ever-rising gaps in living standards and in people’s experiences of life, redistributive welfare must do more heavy lifting.
My sense, though, is that we don’t just need a welfare system that’s bigger; we need a system that works differently too. Welfare must be fundamentally enabling, and simple enough to give people the security they need in order to engage in the new economy with confidence.
This is partly about money for sure; if you’re worried about how you’ll feed your kids tonight, or pay your energy bill tomorrow, you’re not going to take out that loan to retrain, or risk moving home to access good jobs, or take the leap of starting a business. So in Britain at least we must address below-subsistence levels of welfare and heed the calls — from the right as well as the left — for an adequate minimum income.
Security, though, isn’t just about money. It’s also about how people experience the welfare state. It’s time progressives and economists saw that the experiential aspects of welfare matter as much as the income distributions they analyse in their spreadsheets. A welfare system won’t enable people if it’s so damn complicated that it leaves people living on the edge of their seats, permanently anxious at how any change to their circumstances will affect their income. And it won’t enable people if financial support comes wrapped in so much stigma that it leaves them better off financially but shot of all dignity and self-esteem.
So my personal view is that this all points in one direction: a welfare system that is simpler and more universal, moving over time to a clear and emphatic offer, more on the model of a universal service than a labyrinth of means-tests: the security of a living income for all.
Conclusion
In this essay I’ve tried to take a fresh look at inequality. To tilt our heads to the nail of a problem we thought was familiar, hoping that a new angle, oblique to the light, might show up the contours of a screw.
Some people will say I’ve gone too far; that I’ve overstated the extent to which inequality has changed — and in a sense I agree. Of course inequality hasn’t changed entirely. Of course there are still injustices in the world today for which our old tools work well, and on which we just need to hammer harder. But the hill I will die on is that the digital revolution changes inequality more than progressives have yet come to accept. And it seems to me there’s zero chance that we’ll end up being too quick, or too adept, in the way we formulate our policy response.
That’s why in this essay — in fact, throughout this whole project — I’ve emphasised — okay, maybe at times exaggerated — the profound changes we’re living through. It seems to me that as progressives there’s little risk that we’ll give too much thought to the future. The real risk is that we’ll spend all our time squabbling over tweaks to old policy settlements, and not nearly enough time building new ones.
With a topic as big as inequality, the idea of building a new policy settlement can feel daunting. But my hunch is that, in the long-run, there’s far more opportunity here than risk.
An awkward fact for people on the left is that most people don’t really care about inequality. Or, to be more precise, most people don’t share the left’s gut aversion to unequal distributions of material resources.
That’s not to say people don’t care about fairness; they do, but in a host of wider ways. People get riled up by place-based inequities, like when their local area has no good jobs and no decent schools compared to the posh part of town. People get angry when privilege transfers across domains, like when rich people push their kids to the front of the educational queue. And people are acutely sensitive to the daily indignities of relational injustice: the rage of being looked down upon, or the simmering fury of feeling patronised.
Inequality had all these features, of course it did. But unequality has them in spades. If anything, the main currency of unequality isn’t money or material resources; it’s status, agency, and pride.
All of this forces on progressives — in the manner of force-feeding a medicine that will do them some good — a broader, more humane language of fairness. And so unequality demands a response that isn’t just more integrated but that could be more politically inclusive too, speaking to people’s intuitions beyond the traditional left.
Maybe it’s because I’m a political philosopher at heart, but this is the aspect of unequality that I find most promising. It doesn’t just demand a new policy agenda, it also opens the door to a new politics, even a new philosophy, of justice. It means it’s time for the left to emerge from its 1990s turn, during which it narrowed its conception of justice to a technical utilitarianism and a superficial reading of Rawls. And what comes next is a broader conversation, drawing partly on contemporary material, like Elizabeth Andersen’s notion of relational equality, but also on the rich conceptions of equality that gave progressive politics so much of its life and moral clarity and vigour in the past.
As always, those are just some reflections — strongly stated, loosely held — shared to provoke debate. I’d love to hear responses and critiques. (Here’s an open version of the essay if you want to add comments.)
By way of a wrap up, this is the last of three essays from a project I’ve been leading for the Joseph Rowntree Foundation, Social justice in a digital age. The project has explored the nature of digital capitalism, trying to make sense of a world that’s changing so fast around us, and reflecting on the implications for the pursuit of social justice.
In the first essay in the series, The Invidious Hand, I looked at how platform capitalism changes markets, requiring a new relationship between the market and the state. In the second essay, The Care Paradox, I explored what happens to care in a high-technology future. We also had fascinating and thought-provoking contributions from Anna Dent on worker-driven futures, Rachel Coldicutt on a feminist future of digital care, Toby Nangle on the deep future of public services, and Stian Westlake on the theme of this essay: inequality in an intangible age. Thanks to everyone who attended discussions on these topics and to our co-hosts: the Institute for Government, the Tony Blair Institute, the Resolution Foundation, and Newspeak House.
To stay in touch with what‘s coming next you can follow me on Medium or support my writing on Substack. And, as always, for the big optimistic take on how we build a fairer future, there’s my book, End State.
