The average South African adult works 15.7 hours a week.

Not the average of those employed. Not the average of those in the labour force. That is the number you get when you take every adult in the country, employed or not, young or old, and divide the hours they spend in paid work by the number of them. Across the 160 countries in a new database by Amory Gethin and Emmanuel Saez, the people of only nine countries work less. Italy, at 15.3, is one of them. France, at 16.1, sits just above us.

Why that coincidence – a South African working week nearly identical to Italy’s – is the question I investigate in this post. Because 95 years ago, someone predicted that exactly this will happen.

In 1930, in the depths of the Great Depression, John Maynard Keynes published Economic Possibilities for our Grandchildren. He argued that, once you looked past the cyclical gloom, the real trajectory of the world economy was astonishing. ‘The standard of life in progressive countries one hundred years hence,’ he wrote, ‘will be between four and eight times as high as it is.’ Compound interest and compounding knowledge would, within a century, reduce the working week to fifteen hours or so. ‘The economic problem,’ he predicted, ‘may be solved, or be at least within sight of solution, within a hundred years.’

His deadline was roughly 2030. We are four years out. And in South Africa, arithmetically, he was right. But for the wrong reasons…

Read more

Imagine you could rewind about twelve thousand years. In the hills of the Fertile Crescent, something extraordinary was beginning – and, over the millennia that followed, in the Yangtze basin, in southern Mexico, and in the highlands of New Guinea too. People were gathering wild grasses and planting the seeds in soil they had cleared. They were penning sheep and goats instead of only hunting them. They were scratching the earth with digging sticks and waiting, on purpose, for something to grow.

This is the Neolithic Revolution. After the Industrial Revolution, it is probably the most consequential event in human history. It produced the first food surpluses – and, over the millennia that followed, the cities, writing, specialised labour, kings, priests, soldiers, merchants and bureaucrats that a surplus makes possible. It reshaped almost every aspect of what it means to be human.

So how does the new draft South African History curriculum teach this pivotal moment? Under a Grade 5 topic titled ‘Food’, built around the question: ‘How do we know what people ate in the past?’

Not ‘Why did people start farming?’ Not ‘What changed when they did?’ What did they eat.

For the past few weeks, the public debate about the amended History Curriculum and Assessment Policy Statements has focused on one thing: its Africanness. Is it too Afrocentric? Does it sideline the French Revolution, the civil rights movement, Jan van Riebeeck, the Holocaust? Is it patriotic history dressed up in new clothes? These are serious questions, and serious historians have asked them.

But there is one major question that the curriculum has failed to address that I think is the more important one.

For most of human history, almost everyone was poor. Not poor as we understand the word today. Poor as in one or two dollars a day, in today’s money, for nearly all of humanity. Life expectancy of about thirty years. Child mortality of around forty per cent. Famine always one failed harvest away.

Then, roughly 250 years ago, something changed. In pockets of north-western Europe, and eventually across much of the world, living standards began to rise. First slowly. Then astonishingly fast. The average human today is eighteen times richer than in 1800. Global child mortality has fallen from 43 per cent to under four. Extreme poverty, which afflicted nearly half the world as recently as 1980, has fallen below ten.

This is the story of how humanity escaped poverty. It is the central story of the past two centuries. And it is still being written, not least in South Africa, where we remain caught between the world we came from and the one we are trying to build.

Here is the striking thing. In the new draft curriculum, this story is almost entirely absent.

Economic history – the study of how societies become prosperous – has been sidelined. The word ‘productivity’ does not appear in the Intermediate Phase curriculum. The idea of institutions as the rules that determine who benefits from trade and innovation is nowhere. Comparative advantage, structural transformation, human capital: absent. The Industrial Revolution is taught as a story of child labour and European imperialism, not as the moment when, for the first time in history, humans figured out how to generate widespread prosperity. The Mineral Revolution is taught almost entirely through the lens of exploitation. South Africa’s economic story, in Grade 12, ends in the 1930s. Nothing on what happened after 1994. Nothing on why growth stalled after 2008. Nothing on what young South Africans might do about it.

Put differently: a learner can complete twelve years of History without once being asked why some societies are rich and others are poor. A learner can finish Grade 12 without hearing an optimistic, evidence-based account of how humans have, repeatedly, figured out how to live better.

This begs the question: what is the purpose of historical learning? In the post-apartheid era, much has been said about the importance of History for nation-building: preparing young learners to be well-informed, responsible citizens. In 2018, a Ministerial Task Team recommended, rather ambitiously, that History should be made compulsory up to matric. That all sounds rather admirable, except that it’s unrealistic if no one wants to take the subject to begin with.

In the United States the number of students graduating with humanities degrees has fallen for eight consecutive years. History as a major has collapsed. Though the subject has always been relatively small, at its peak History majors accounted for 5.7% of US bachelor degrees in the late 1960s, in 2019 that number was less than 1.2%. Why? Setting aside questions of employability and earnings for History graduates, the bigger problem seems to be that History has a reputation problem. At best, the subject is perceived as dry fact, at worst, it gets dismissed as irrelevant and impractical.

So what’s the problem? It seems it is a question of relevance. The subject is important, but in an abstract sense. People want to know more about the past because they believe historical knowledge is intrinsically important. But a subject which is abstractly important struggles to attract builders – the people who want to make things, start things, fix things. Those students go elsewhere. They head for engineering, computer science, business where the link between action and outcome are more readily apparent.

A history that fails to give learners an account of how change actually occurs – how poor societies have, in specific and identifiable ways, become richer, freer, healthier – fails to empower them. Empowerment comes from whether a subject explains how change happens. Economic history excels at identifying what incentives people faced, how institutions shaped behaviour, how small change compounds over time. It turns facts into mechanisms and supplies actionable insights.

Without that, how do we teach a young South African that poverty is not destiny?

None of this is to say that slavery, colonialism and apartheid should be taught less. They should be taught well – and economic history can help teach them better.

Why did the transatlantic slave trade happen? Not because Europeans were uniquely cruel. But because an institution took hold that made human beings into property – a form of capital that could be bought, mortgaged and put to work on sugar plantations at a scale no free labour market could supply. And because that demand, meeting European firearms, transformed African political economies too: kingdoms that could capture and sell people acquired guns, wealth and the coercive power to capture more. The trade wrote itself into institutions on both sides of the Atlantic. And why were its consequences so lasting? The economist Nathan Nunn has shown that African countries from which more enslaved people were taken are significantly poorer today – not because of some inherent disadvantage, but because the slave trade destroyed social trust. Communities that had to fear enslavement by their own neighbours grew mistrustful of strangers, and that mistrust still shows up, measurably, in surveys today.

Economics does not diminish the horror of slavery. It deepens it, by showing that the damage extended far beyond the millions of lives directly destroyed.

In the report I submitted to the Minister last week, I make a simple argument. Nothing I am suggesting requires a new curriculum. Nothing demands extra teaching time. It is a shift in framing: bring the economic dimension back into topics already taught, so that learners leave school with a fuller picture of the human past.

Take Grade 6, where learners study the trans-Saharan trade. The topic already describes gold, salt and cowrie shells moving across the desert. Add one question: what made Mali rich? A big part of the answer is exchange – Mali controlled the gold fields of Bambuk and Bure to the south, the Saharan salt mines lay a thousand miles to the north, and caravans crossing between them enriched elites on both ends. Mali taxed, conquered and enslaved too. But a great deal of its fabled wealth came from trade rather than plunder – and that is a powerful corrective to the assumption that African wealth, wherever it appears, must have been taken from someone else.

Take Grade 8, where learners study the Industrial Revolution. The framing is ‘what were the inventions, what were the social consequences?’ Add one more question: why did it happen in Britain, and not in China or India, both of which had been much richer a few centuries earlier? The answer – which involves institutions, property rights, scientific culture and incentives for innovation – is one of the most important questions in all of history. It is also directly relevant to a country still negotiating its own industrial transformation.

Take Grade 12, where we currently stop the South African economic story in the 1930s. Bring it forward. Teach the Mandela-Mbeki years, when growth returned and poverty fell. Teach the post-2008 stagnation. Teach state capture and the dismal economic performance, exposing the contrast with other developing countries that have outpaced us. Learners deserve to understand the country they are inheriting.

I do not make this case only as a critic. The ten South African chapters of my book Our Long Walk to Economic Freedom cover precisely this territory, from pre-colonial economies to the present. From next year, the full book will be freely available – I would love to see it in the hands of every History teacher who wants it. Alongside the book, a free-to-read graphic novel based on ten of its chapters will be published in January 2027, aimed at a high school audience. Both are offered as resources for any teacher, learner or curriculum developer who wants to bring this story into the classroom.

This is a debate worth having. The Minister and her officials have opened the curriculum for public comment precisely because they know that an early draft is not the final text. There is time to add the economic dimension without disturbing the structure they have built.

Luckily, we still have the opportunity to change this. A review commissioned by the Department of Basic Education in 2024 found that enrollment into high school-level History has actually increased in recent years. Tellingly, the report suggests this may be because History is perceived as easier than subjects like Physics. All the same, we risk losing this golden opportunity. As I wrote in my report, History education has always been important but it is especially important today. The generation now entering South Africa’s schools will inherit an economy that has not grown in per capita terms for over a decade, where unemployment stands above 30 percent and where inequality remains among the highest in the world. These are economic outcomes, and they have historical causes. A History curriculum that equips learners to understand those causes, and to imagine how they might be overcome, is a necessity.

If we learn anything from history, it is that the future is likely to be better. But it requires us to wish it so.

An edited version of this post first appeared on News24 – my final column for the platform, as my contract came to an end in April. From here on, the twice-monthly columns will continue exclusively on Our Long Walk. Writing like this is now supported directly by readers, and if you’ve valued the column over the years, a paid subscription is the most direct way to keep it going. I would be grateful for the support.

Yesterday I gave a talk to twenty-five of Stellenbosch University’s Heads of Department about artificial intelligence. I wanted to write it up, partly because the argument is easier to follow as an essay than as twenty slides, and partly because I suspect others might find it interesting.

One thing to mention first: When I started preparing the talk a week ago, two of the tools I ended up using did not yet exist. Opus 4.7, the model I use most, was released only last Thursday. Claude Design, which I used to build the slides, appeared the next day. If I had given the same talk a month ago, several of my examples would not have been available. A week from now, parts of this essay will already read as slightly dated. When I wrote a post here listing the AI tools I use, only six months ago, almost nothing on that list would survive today. That is the tempo we are working at, and it is the first thing a Head of Department needs to understand.

It helps, before going further, to place this in the frame economic historians use. General-purpose technologies are an old story: the plough (see an earlier piece here), steam, electricity, the computer. Each reallocated labour rather than eliminating it, but each also rewarded some people, places, and organisations sooner than others. The interesting question has never been ‘will it replace us?’ It has always been ‘where does labour go, and who gets there first?’ AI fits that pattern, with one new wrinkle. It automates cognition itself, and its speed of diffusion is unusual even by the standards of earlier GPTs. That combination is what makes the present moment genuinely different…

Read more

A good friend recently began to work for a large firm in South Africa. He tells me that he’s been surprised at the productivity of the team; having just returned from a venture firm in Europe, where he thought they were working hard to keep the dream of future gazillions alive, he expected a slightly more relaxed lifestyle here. But it is just the opposite. Their main markets are outside of South Africa, so they have to compete not only against the best in South Africa, but the best globally. Success, here, doesn’t just mean higher (future) returns for the owners. It means higher salaries, more jobs – for both skilled and semi-skilled workers – more taxes for government. In short: it means economic growth.

His story got me thinking. South Africa has a Department of Small Business Development. It has programmes, grants, and incubators dedicated to helping entrepreneurs start small. It does not have a Department of Large Firm Competitiveness. Perhaps it should.

That is because the uncomfortable truth is that small firms – however admirable their hustle – are not the ones that will move the needle. Large firms will.

That claim sounds unfashionable, even harsh. So let me show you the evidence.

A striking new paper by Zhang Chen, forthcoming in Econometrica – one of the most prestigious journals in economics – documents a simple but powerful fact: as countries grow richer, the right tail of the firm size distribution thickens. In plain language: economic development means more large firms, and larger large firms. This is not a comparison of apples and oranges – rich countries versus poor countries at a single point in time. It holds within countries over time. As an economy grows, its biggest firms grow even bigger relative to the rest. Chen shows this for a panel of 33 OECD countries from 2008 to 2017 and for the United States over four decades. The relationship is robust across sectors and across different ways of measuring firm size.

The pattern is remarkably persistent. Spencer Kwon, Yueran Ma, and Kaspar Zimmermann collected data on the size distribution of US businesses stretching back a full century. Their finding, published in the American Economic Review in 2024, shows that in the early 1930s, the top 1 per cent of US corporations held about 70 per cent of total corporate assets. By 2018, that figure had risen to 97 per cent. The top 1 per cent’s share of receipts – a proxy for sales – climbed from around 60 per cent to over 80 per cent. This is not a recent Silicon Valley story. It is a hundred-year secular trend. Each generation, it turns out, believes itself to be witnessing something new. Each generation is wrong. Bigness has been rising for as long as we have measured it.

But why? Is it just that rich countries happen to have big firms, or is there a deeper mechanism at work?

Chen’s theoretical contribution offers a compelling answer. He builds a model of what he calls ‘idea search’: firms improve their productivity by learning from other, more productive firms. Think of it as economic osmosis. A mid-sized manufacturer does not figure out a better logistics system from scratch. It learns by hiring someone who worked at a bigger firm, by observing what a competitor does, or by absorbing knowledge through a supply-chain relationship. When firms search for better ideas – new technologies, new processes, new ways of organising production – they are more likely to learn from the most productive firms in the economy. And those are, almost by definition, the large ones.

This creates what economists call a ‘diffusion externality’. When a large firm innovates or adopts a better process, the benefits do not stop at its factory gates. Its suppliers learn. Its competitors are forced to adapt. Its former employees carry knowledge into new ventures. The entire economy benefits. Put differently, when a large firm in Johannesburg figures out how to do something better, that knowledge ripples through its suppliers, its competitors, and eventually the entire economy.

The problem is that large firms do not capture the full social value of this diffusion. They invest in idea search for their own benefit, not for the benefit of the firms that learn from them. The result: they under-invest relative to what would be socially optimal. Chen shows that policies favouring large firms can correct this market failure and improve welfare. This is not a defence of cronyism or monopoly. It is a precise economic argument about externalities – the same logic that justifies subsidising education or vaccinations.

What does this mean for South Africa?

Start with exports. Marianne Matthee and her co-authors showed, in a 2017 study published in the South African Journal of Economics, that only large, productive firms manage to export manufactured goods from South Africa. Small firms lack the scale, the knowledge, and the networks to compete in international markets. If we want South Africa to earn foreign exchange, we need firms big enough to break into those markets.

And here is the rub. South Africa already has firms like that – just not nearly enough of them. SARS tax data show that barely a thousand firms employ 1 000 or more workers. They make up roughly half a per cent of all formal firms, yet they account for fully half of all formal employment.

The trouble is what those giants do. South Africa’s large firms tend to come in two flavours: resource extractors that ship commodities, and domestically oriented champions in retail, banking, and telecoms that sell mostly to South Africans. The category we are missing – the large, export-oriented, internationally competitive manufacturer of the kind my friend now works for – is precisely the one Chen’s model tells us matters most for growth.

Then consider market structure. Friedrich Kreuser, Michael Kilumelume, and Rulof Burger, in a 2024 UNU-WIDER working paper, studied markups and mergers in the South African economy. They found that large mergers affect market dynamics through supply-chain integration – a channel that is often overlooked in the competition debate. The policy question is not whether large firms should exist. It is whether they face enough international competition to keep them sharp. A large firm shielded by tariffs and red tape is a drag on the economy. A large firm forced to compete globally is an engine of growth.

To put it into perspective: a single globally competitive firm like Shoprite employs over 150,000 people across Africa. It trains workers, builds cold-chain infrastructure, and anchors local economies in towns where it is often the largest private employer. Discovery, Naspers, Bidvest – these firms sit at the centre of vast supply chains, generating demand for thousands of smaller suppliers. They pay the bulk of corporate tax. They invest in the technologies that other firms eventually adopt. A thousand survivalist spaza shops, however important for the livelihoods of their owners, do not create the same kind of systemic growth. They are a symptom of an economy with too few good jobs, not a cure.

Of course, this does not mean small firms are worthless. They provide livelihoods. They are seedbeds for entrepreneurship. A handful will hopefully grow into the large firms of the future – and a healthy economy needs that pipeline. But the policy emphasis is backwards. Small firms benefit most when large firms thrive – as suppliers in their value chains, as employers of skilled workers who later start their own businesses, as anchors of local economies that generate demand for everything from catering to courier services. Chen’s model makes this precise: the diffusion externality flows from large firms to the rest of the economy, not the other way around.

The problem is not that South Africa has too few small firms. It is that too few of its large firms compete globally. And when those firms are hemmed in by red tape, unreliable electricity, crumbling rail networks, and policy uncertainty, the damage is not limited to their balance sheets. It cascades through the entire economy.

Which brings us back to my friend. His large firm’s intensity is not an accident. It is the price of global competition. And it is precisely that competition – gruelling, relentless, sometimes exhausting – that makes the firm a growth engine for the rest of the economy. Every rand it earns abroad, every idea it absorbs and diffuses, every worker it trains lifts the broader economy in ways that no government programme for small business can replicate.

The lesson? If South Africa wants jobs, stop romanticising smallness. Make sure the big firms can compete with the best in the world. Their success will not just enrich their shareholders. It will ripple through the entire economy – into the salaries of their workers, the orders of their suppliers, and the tax receipts that fund schools and clinics. Bigness, it turns out, is goodness.

Imagine a small group of people walking across a grassland in what is now KwaZulu-Natal. The year is 220,000 BCE. They are not hunting. They are not gathering food. They are heading, with intent, to a particular outcrop of dark grey rock above the Jojosi River. They have walked this route before. So did their parents, and their parents’ parents, probably for 5000 generations or more. The stone they want is hornfels – a hard, fine-grained rock baked by ancient magma into something that flakes beautifully. They will arrive, knock large blocks into long blades, and carry the blades away to use somewhere else. And then, over the next hundred thousand years, others will keep coming back to do the same thing.

That picture comes from a paper just published in Nature Communications by Manuel Will and an interdisciplinary team working at the Jojosi Dongas, an eroded landscape in the grasslands east of the Drakensberg. The team excavated several thin layers of stone tools across three sites, fitted hundreds of flakes back into the cores they came from, and dated the sediments above and below. The result is the first clear, dated record of people returning to a single rock source for the same purpose – and only that purpose – for more than 100,000 years.

Until very recently, the standard story of how Stone Age people got their tool stone went something like this. They were on the move – hunting, gathering, following water – and when they happened across a useful rock, they picked it up. Archaeologists call this ‘embedded procurement’: stone-collecting was a by-product of doing something else. Making a special trip to a particular place to get a particular rock was thought to be rare, and late, perhaps the sort of thing Neolithic farmers did when they sank flint mines into the chalk of southern England. Pleistocene hunter-gatherers, the textbook said, did not really do this.

Jojosi shows that they did. And much earlier than anyone thought.

Read more

Imagine you’re a Nigerian law student studying for the bar. You open ChatGPT. It can tell you, confidently, the capital of Arizona. It cannot tell you how a Lagos magistrate is likely to rule. About 52 per cent of the open internet is in English. Four or five other languages split most of the rest. Igbo is not on that list. Neither is Yoruba, Swahili, A…

Read more

In 1602, Dutch merchants pooled their capital to form the Vereenigde Oostindische Compagnie – the VOC. What made the venture revolutionary was not the ships or the spice. It was the information: prices in distant ports, harvest conditions, currency movements, political risks. As the historian Steven Marks has argued, the profit motive is ancient – but it was the expanding capacity to gather and analyse information that turned trade into capitalism. Every great leap – double-entry bookkeeping, the printing press, the telegraph, the internet – was, at root, an information revolution.

Large language models are the latest turn in this long story. ChatGPT, Gemini, Claude – these tools democratise the kind of analysis that was once the preserve of expensive professionals. A small business owner in Soweto can now interrogate a data set, draft a contract, or evaluate a market opportunity in ways that previously required a consultant. The arbitrage gap – between those who have information and those who do not – narrows dramatically. That is, historically speaking, how economies grow.

The point? The very technology that accelerates economic activity also makes that activity harder to count. And counting it accurately has never been more important for South Africa.

Consider a teacher in Limpopo preparing her matric class for the final examinations. A year ago she would have directed struggling students to a tutoring centre at R500 a month, or bought supplementary workbooks from a commercial publisher. Both transactions appear neatly in GDP. Now she uses Claude to generate personalised practice problems, mark essays with detailed feedback, and produce revision notes tailored to each student’s weak points. Her students’ results improve. But the tutoring centre loses customers, the publisher sells fewer workbooks, and measured GDP in education services may actually fall. The real output has increased. The national accounts cannot see it.

This example captures a broader problem. As economies mature, services – finance, software, consulting, healthcare, education – account for a larger share of output. Services are notoriously difficult to measure, and AI is about to make it much worse. When a single subscription can replace a paralegal, a copywriter, and a data analyst simultaneously, the standard framework of counting market transactions breaks down.

A broken estimate of economic output can really hurt an economy, especially one that stands at a fork in the road. The RMB/BER Business Confidence Index, published a few weeks ago, shows that confidence has climbed to levels last seen around the pre-2008 period, with the post-COVID rebound the only comparable jump. But such confidence is not reflected in GDP statistics – or projections. Why?

In a new working paper, Helanya Fourie and I examine the revision history of South Africa’s GDP and find that StatsSA tends to understate booms and overstate downturns. We also show that sectoral revisions are far noisier – agriculture can see a single quarter’s growth revised by more than ten percentage points as harvest data trickles in. Or consider the load-shedding example. Over 6,500 productive hours lost in 2023 must surely have imposed a sharp fall in output. Yet real GDP edged up by 0.7 per cent. When electricity supply returned to uninterrupted in 2024, growth stayed at 0.6 per cent rather than rebounding. The mismatch should invite deeper scrutiny. It also does not help that the demographic benchmarks underpinning these estimates are themselves unreliable – and the troubled 2022 census raises exactly that concern.

For firms deciding where to invest, for the Reserve Bank setting interest rates, and for a government defending its fiscal credibility, these are not abstract statistical quibbles. Get the numbers wrong, and policy is set in the dark.

The good news is that a remarkable toolkit has emerged. Satellite imagery now tracks agricultural output in near real time; vegetation indices explain crop variation that conventional surveys miss. Night-time lights, measured from space, correlate with economic activity and reveal sub-national patterns where ground-level data is sparse – invaluable for an economy with a large informal sector.

Consider the map below. It shows average annualised GDP per capita growth for South Africa (and Lesotho and Eswatini) between 2012 and 2022. The data, from Estaban Rossi-Hansberg and Jialing Zhang’s paper ‘Local GDP Estimates Around the World’, include information on population, vegetation, nighttime light emissions, urban areas, cropland, forest, water, snow & ice, ruggedness, national GDP per capita, and CO₂ emissions from manufacturing, heavy industry, and transportation. For South Africa, it reveals something quite interesting: growth in places we might typically not associate with improvements in GDP per capita: the mining hub of the Kalahari, the Breede River valley, the northern border of South Africa, the foothills of the Drakensberg. KZN and Gauteng (and Lesotho) seems to have had very little per capita growth.

Rossi-Hansberg and Zhang obviously used data available for the entire world, but one could think of a far more innovative measure. Mobile-phone metadata and payment flows map economic engagement at fine spatial scales. Online prices scraped from retailer websites produce daily inflation indices that capture dynamics quarterly surveys cannot. Textual data from earnings calls, news, and job advertisements yield forward-looking indicators of sentiment and labour demand. And AI itself – the same technology disrupting our measurement frameworks – offers powerful nowcasting models that combine these disparate streams into composite indicators updated weekly or even daily.

What we propose is a real-time economic barometer: a composite index that draws on electricity generation, financial transactions, mobility data, satellite imagery, online prices, and textual indicators. Not a replacement for GDP, but a complement that captures shocks and structural change between quarterly releases.

Building a data observatory to produce this economic barometer requires collaboration between Stats SA, the Reserve Bank, universities, and the private firms whose data feeds would form the backbone of the system. It requires investment in public-sector data science. And it requires governance – data-sharing protocols, privacy safeguards, a trusted intermediary – to ensure credibility and transparency.

None of this is fanciful. The UK’s Office for National Statistics already publishes experimental high-frequency indicators. South Africa has the institutional foundations and the technical talent. What it also has – critically – is the urgent need: we cannot afford to have poor GDP numbers slow investment decisions when the reality is very different.

The VOC merchants understood something that still holds: those who see the economy most clearly are the ones best placed to prosper in it. From the printing press to the large language model, that principle has not changed. South Africa can build the tools to see clearly again.

At the end of last year, the G20 has decided that the world faces an ‘inequality emergency’. A committee of eminent economists, led by Joseph Stiglitz, warns that 83% of countries now suffer from ‘high inequality’, defined as a Gini coefficient above 0.4. They propose an International Panel on Inequality, national ‘Inequality Reduction Plans’, and a new architecture of global rules to bring the top 10% closer to the bottom 40%.

There is moral passion here, and some sensible policy ideas. But the language of ‘emergency’ conceals something important. Inequality is being treated as the thing we really care about. It is not.

People care about poverty and social mobility because they have direct consequences for their lived experience: whether you can feed your children, whether your life chances are truncated at birth, whether your effort counts for anything. Once you remember that, the Gini starts to look like a slightly strange hero. If your only objective is to lower the Gini, there are quick fixes. Expropriating the richest hundred citizens would do the trick. So would a devastating war that wipes out urban capital. Few people would celebrate either, because neither tells you anything about whether people are less poor or more mobile. The indicator is not the objective.

The second assumption baked into the ‘inequality emergency’ framing is that modern capitalism is the villain of the piece. The report leans heavily on the idea that rising inequality is the child of neoliberalism and globalised markets since the 1980s, and that what came before was more egalitarian.

That is, at best, incomplete. Here’s why.

Historians who have spent the last decade reconstructing the ‘new history of old inequality’ paint a more uncomfortable picture. Using social tables, tax rolls and probate records, they find that, across much of Western Europe and the United States, inequality rose almost monotonically from the late Middle Ages to the eve of the First World War. Only two great shocks – the Black Death in the fourteenth century and the period 1914–1945 – really dented it. Guido Alfani’s long-run survey for Europe summarises the stylised facts: from about 1450 to 1900, both income and wealth Ginis tended to drift steadily upwards, with the top 10% often owning 80–90% of all wealth.

Inequality is not a new irritant unleashed by five decades of neoliberalism. It seems to be the default setting of agrarian, hierarchical societies.

That raises a natural question for South Africa: when did we become so unequal?

South Africa today tops the World Bank’s global ranking of income inequality, with a Gini around 0.63, and wealth is even more concentrated: estimates suggest the top 10% own more than 85% of household wealth and the wealth Gini is above 0.9. It is tempting to pin that entirely on apartheid and twentieth-century capitalism. In a recent paper, I tried to push the story much further back by reconstructing inequality in the Cape Colony between 1685 and 1844.

The Cape is unusually well documented. The Dutch and then the British left behind a paper trail that would make many European archivists jealous. I draw on four main sources. The first is the opgaafrolle, the annual tax censuses that enumerate almost every free household: the names of the heads, the number of children, the numbers of enslaved and Khoesan workers, and then the key productive assets of the farm – horses, cattle, sheep, vines, grain reaped, wine produced.

The second is the probate inventories of deceased settlers, taken down by the Orphan Chamber in Cape Town, listing furniture, livestock, slaves and tools at death. The third is the slave valuation rolls of 1834, where British appraisers record each enslaved person and the compensation paid to the owners at emancipation. The fourth is a set of tax returns for Khoe mission stations and settlements such as Bethelsdorp, Groenekloof and Swartrivier in the early nineteenth century.

One virtue of this material is that it lets us move beyond ‘social tables’ – those broad group averages that dominated earlier work – and measure inequality within groups, not just between them. That has been one of the key methodological shifts in the new inequality literature: instead of only comparing, say, ‘peasants’ and ‘nobles’, we try to see how unequal peasants were among themselves, and what share of everything the very top 1% captured. At the Cape we can do that for settlers, for slave-owning households, and, to some extent, for Khoe communities.

What do the numbers say? In the Stellenbosch-Drakenstein district, the heart of the colony’s grain and wine belt, the Gini coefficient for key assets, as the figure above shows, is never below 0.5 from 1685 to 1844, and often sits between 0.6 and 0.75. When I combine livestock, vines, grain and wine into a single wealth basket, valued at contemporaneous auction prices, the Gini for settler households hovers in that range for a century and a half. The top 1% of settler households hold about 12% of the measured assets in the eighteenth century and closer to 18% in the early nineteenth century.

Adding slavery pushes inequality into truly extreme territory. If you treat enslaved men as separate, asset-less households – which is close to their legal status – the overall Gini in Stellenbosch-Drakenstein approaches 0.9 by the late eighteenth century. Even if you credit enslaved men with an in-kind ‘wage’ equivalent to a sheep a month, the coefficient hardly budges. Using the emancipation data, and adding the majority of settler households who owned no slaves at all, the Gini for slave wealth and compensation jumps into the 0.8–0.9 range in several districts. A small minority of slave-owners dominate the distribution.

The same pattern emerges from a very different source. The probate inventories – essentially estate files for thousands of deceased settlers – show Ginis for cattle, sheep, wagons and ploughs exceeding 0.6, and after 1770 often above 0.7. You do not need very sophisticated econometrics to see the picture: even within the settler population, a small fraction owns most of the productive capital.

What about indigenous communities? For three Khoe settlements around 1825 – Bethelsdorp, Groenekloof and Swartrivier – we have tax returns listing livestock and grain. Here too the within-village Ginis are extreme: usually above 0.75, and for some assets very close to perfect inequality, with a handful of families owning almost all the cattle or sheep. When you pool Khoe and settler households in a district like Swellendam, the combined Gini for cattle reaches 0.92.

Two things follow. First, the Cape was already a brutally unequal society by the late seventeenth century. Inequality between groups was enormous: settlers were wealthier than the enslaved, who in turn typically had more recorded assets than Khoe households. But inequality within each group was also extreme. Among settlers, the within-group Gini is as large as the overall Gini; even if you removed enslaved and Khoe households from the sample, the Cape would still look like one of the most unequal societies we can measure. Among Khoe communities on mission stations, a small elite of cattle owners towered above their neighbours.

Second, inequality has not steadily increased over the last three centuries. There is ample evidence that, like elsewhere, it fluctuated. Take another source: the top incomes in South Africa during the twentieth century, by economists Facundo Alvaredo and AB Atkinson. The graph below shows the top 1% of incomes between 1914 and 2012.

What is clear is the decline in inequality during the apartheid period. No one is arguing for a return to apartheid policies because inequality was lower then than after the arrival of democracy. It reinforces my point that what we care about is not the actual Gini – or, in this case, the top income share – but the potential of those at the bottom of the income distribution to build better lives.

Democracy did bring a different kind of inequality, and here it echoes the eighteenth century again. As I explained in a previous post, new research by Manysheva, Mestieri, and Schauer documents that within-group inequality among black South Africans has increased sharply since the end of apartheid, lifting overall inequality. By contrast, the gulf between racial groups – the central organising principle of apartheid – is steadily narrowing and will continue to shrink.

All of this complicates easy stories about ‘capitalism’ as the source of inequality. South Africa’s inequality today is not just about the arrival of mining capital or industrialisation and apartheid-era exploitation. It has much deeper roots. Long before ‘neoliberalism’, a peasant in rural France, a subject of the Asante state, or a commoner near Great Zimbabwe would have faced three brute facts: deep absolute poverty, short life expectancy, and a social order designed to funnel most surplus to a tiny elite of chiefs, landlords or priests.

Modern market economies have done something quite different. They have generated the first sustained escape from mass destitution in human history. As the figure above shows, drawn from Our World in Data, the share of the world’s population living in extreme poverty has fallen from well over 80% around 1800 to roughly 10% by 2015. That change has been driven by productivity growth, trade, urbanisation and the spread of technology, combined with states that, at their best, invest in health, education and infrastructure. Inequality has not disappeared; indeed in many places it has risen. But the poor are, on average, far less poor, and the ladders available to climb are far more numerous.

For a young woman in a remote village in Chile or in the mountains of Lesotho, the relevant question is not whether Elon Musk is worth one billion, ten billion or one hundred billion dollars. It is whether she can connect to the internet, attend an online course, or speak to a distant doctor. Low-earth-orbit satellite systems like Starlink are already providing school connectivity in remote Chilean communities such as Caleta Sierra and Sotomó, places that had no adequate digital connection before. In Lesotho, regulators have now granted Starlink a ten-year licence, explicitly framing it as a way to extend broadband into hard-to-reach rural areas. Whatever you think of Musk, that combination of engineering and regulation is likely to matter more for the poorest Basotho than the exact size of his fortune.

Why then the obsession with ‘inequality’ as the organising principle of global policy? Part of the answer is moral: gaps in income and wealth translate into gaps in power and voice, and the G20 report is right to worry about plutocratic capture of politics. And part of it is political economy: framing the world as suffering from an ‘inequality emergency’ creates a powerful justification for expanding the fiscal and regulatory reach of the state. The report’s headline recommendations – more progressive taxation, new global wealth taxes, enlarged multilateral mandates, National Inequality Reduction Plans – all point towards a larger public sector.

I am not arguing against redistribution. A civilised society should ensure that everyone has a basic standard of living and a fair chance in life. South Africa already redistributes a very large share of national income through grants, free or subsidised education, health and other transfers, and there is a strong case for doing better on both poverty relief and mobility. My point is different. We should be honest that what we really care about is not the Gini, but whether those at the bottom are less poor and more mobile. Inequality is, at best, a noisy proxy for that; at worst, it can be a distraction that encourages policies which lower the Gini while doing little for the poor.

People do not experience a Gini coefficient. They experience poverty, or its absence, and the presence or absence of ladders to climb. If there is an emergency, it is that too many of those ladders are still missing.

When Poland began its post-communist reforms in 1992, its GDP per capita was roughly where South Africa’s is today. Twenty years later, Polish living standards had doubled. South Africa, over the past fifteen years, has averaged near-zero per capita growth. If nothing changes, living standards a generation from now will be essentially the same as they are today. That gap between Poland’s trajectory and ours is the difference between a country where young people find work and one where they do not.

I recently published a research note for the Bureau for Economic Research comparing five countries that faced critical junctures – Chile, Poland, India, Germany and Argentina – and asking what political conditions enabled sustained growth. The note attracted some attention. Hendrik du Toit quoted it at the News24 On the Record conference. The Financial Mail ran a story. The core argument is simple: crises open windows for reform, but only credible political settlements turn those windows into lasting prosperity. Here is why that matters for South Africa right now – and why the clock is ticking faster than most people realise.

Economists have three powerful theories about why countries get stuck. Each one describes a trap that South Africa is either already in or drifting towards.

The first is the war of attrition. The idea, developed by Alberto Alesina and Allan Drazen, is straightforward: when reform is painful, every faction tries to wait the others out, hoping someone else will bear the cost. Reform happens only when the crisis becomes so severe that delay is costlier than concession. Think of it as a game of chicken. In Poland, hyperinflation and shortages made the cost of waiting unbearable – the Solidarity movement’s broad coalition meant no single group could offload the pain. In Chile, the 1982-83 banking crisis was deep enough to discredit both the military’s initial economic model and the opposition’s maximalist demands, forcing a centrist convergence.

In South Africa, the war of attrition is playing out in slow motion. The ANC’s dominant faction knows that serious reform – on energy, on logistics, on the structure of the state – will antagonise powerful internal constituencies: public-sector unions, politically connected contractors, cadre-deployed officials whose positions depend on the status quo. The DA, for its part, risks alienating its base if it is seen to make too many concessions. The EFF and MK Party, sitting outside the GNU, have every incentive to wait: if reforms stall, they can campaign on the coalition’s failure. If reforms succeed, they lose their grievance platform. The logic of attrition tells us that each faction is calculating whether it can outlast the others. The danger is that while everyone waits, the window closes.

The second theory is status-quo bias, from Raquel Fernandez and Dani Rodrik. Even when reform would benefit most people, rational uncertainty about who specifically will gain and who will lose produces collective resistance. People who might benefit from trade liberalisation or deregulation cannot be sure they will be among the winners, so they oppose the change. The result is paralysis even when the status quo is terrible.

This is the BEE problem in a nutshell. Black economic empowerment policies were designed to redress historical injustice – a legitimate and necessary goal. But over three decades, the compliance machinery has calcified into a system that creates enormous uncertainty for investors and entrepreneurs. Reforming BEE is not about abandoning transformation but about recognising that the current structure – with its complex scorecards, ownership requirements and sector charters – generates exactly the kind of rational uncertainty that Fernandez and Rodrik describe. The businessman in Sandton who spent years building a compliant ownership structure cannot be sure it will count under the next revision. The young graduate from Soweto cannot be sure she will ever see a share of the promise. And so everyone defaults to defending the status quo, even when most people would be better off under a simpler, more predictable system. The lesson from the comparative evidence is clear: do not start here. This is the hardest reform, not the first one.

The third theory is the veto-player framework, from George Tsebelis. More veto players make policy change harder – but conditional on change, they also make reversal harder. This is what makes institutional constraints valuable. Poland’s EU accession locked in reforms so thoroughly that no subsequent government could undo them. Chile built its institutional framework during the acceleration – central bank independence in 1989, democratic transition in 1990, OECD peer review later. In Germany, the federal structure made the Hartz labour reforms difficult to pass but equally difficult to reverse. When Merkel’s CDU-led grand coalition succeeded Schroeder – initially governing alongside the SPD, which held eight of fourteen ministerial portfolios – she preserved and extended the reforms because reversal would have required an equally broad consensus.

South Africa’s institutional stock is depleted but not destroyed. The Constitutional Court retains credibility. The Treasury’s technical capacity is intact. The Reserve Bank’s independence is constitutionally protected. The NPA is rebuilding. The GNU itself, with its multiple parties, creates veto players. That is a feature, not a bug – if the coalition can agree on a reform programme, reversal becomes very difficult. But the flip side is real: if the coalition cannot agree, nothing moves.

The figure below shows the fork. Chile sustained its growth for two decades; Argentina reversed. South Africa is right there, at the point where the paths diverge.

The most consistent pattern across successful reform episodes is sequencing. Countries that sustained growth started with high-visibility, broadly beneficial reforms and deferred the most politically costly changes until a growth dividend materialised.

Chile opened trade first, then reformed finance pragmatically after the banking crisis. Labour flexibility increased only gradually. India moved incrementally: tariffs were cut and industry deregulated through executive discretion, but labour reform was left untouched. Growth accelerated without it. Poland liberalised broadly and simultaneously – but only because the collapse of the planned economy left no status quo to defend. Germany is the exception that proves the rule: as a mature economy, the binding constraint was labour-market rigidity, so Schroeder went directly to it. He paid with his career.

For South Africa, the binding constraints are infrastructure and regulatory bottlenecks. Energy reform, logistics reform, digital spectrum allocation, water infrastructure – these are our equivalent of macroeconomic stabilisation. They are high-visibility, broadly beneficial and achievable through executive action. And it is encouraging that some ministers have understood this. Sputla Ramokgopa has driven the electricity turnaround with visible urgency. Leon Schreiber at Home Affairs has cut through decades of bureaucratic paralysis. Dean Macpherson at Public Works and Infrastructure has begun, though progress there remains uneven.

These are the green shoots. They matter enormously – not just for their direct economic effects, but because they demonstrate that the GNU can translate agreement into action. Poland’s experience shows how quickly the investment climate can shift when delivery becomes visible. Each completed project, each unblocked port berth, each megawatt of new capacity signals to investors that the political settlement is real.

Labour-market reform – the Labour Relations Act, collective bargaining structures, minimum wages, and yes, the BEE framework – should be deferred until infrastructure delivery has built political capital and a growth dividend has materialised. Chile and India both demonstrate that growth can accelerate before labour markets are reformed. The political space for those reforms expands once the benefits of growth are tangible. But deferral must be strategic, not permanent. Two decades after Mbeki’s macroeconomic stabilisation, the employment crisis remains unresolved. That is the cautionary tale.

The fact is that reform windows do not stay open forever. Balcerowicz called it the ‘period of extraordinary politics’ – a brief moment when normal interest-group resistance is suspended because the crisis is so severe. South Africa’s crisis is slow-burning rather than acute: a decade of stagnation, collapsing municipal infrastructure, youth unemployment above 60 per cent. A slow crisis allows factions to hope the situation will improve without concessions. It allows them to wait.

The GNU has perhaps two to three years before the next election cycle begins to dominate political calculations. If by then the coalition has not delivered visible improvements, the incentive structure flips. Coalition partners start positioning for the election rather than cooperating on reform. The ANC’s internal factions begin calculating whether they are better off with or without the DA. The opposition parties outside the GNU – the EFF, MK Party – gain ammunition. The window closes.

And then there are external shocks. The current global turbulence – with war in the Middle East spiking diesel prices and disrupting supply chains – could not have come at a worse time. South Africa is at a delicate moment: the reforms are just beginning to generate momentum, investor sentiment is cautiously improving, and the rand was rising strongly against the dollar. A prolonged war that keeps diesel prices elevated and dampens global demand could derail this fragile progress before it produces results. It would be a cruel irony if external factors beyond our control undid the tentative gains of the past eighteen months.

The good news is that our future prosperity is still in our hands. So let me be direct about what a political economy path to growth looks like for South Africa.

First, the GNU must maintain operational coherence around infrastructure delivery. This means a shared reform scorecard – publicly endorsed by the ANC, DA, IFP and other coalition partners – with specific, measurable targets. Not vague commitments to ‘transformation’ or ‘growth’. Concrete deliverables: megawatts connected, port turnaround times, water treatment plants operational, visa backlogs cleared. Ramaphosa’s role is to hold the coalition together around this scorecard and to protect the ministers who are delivering.

Second, protect and repair institutions. The Reserve Bank, the Treasury, the NPA, the Chapter 9 bodies – these are the commitment devices that make the coalition’s bargain credible beyond the current electoral cycle. Every time a politician attacks the Reserve Bank’s independence or undermines the NPA’s prosecutorial discretion, they are telling investors that the settlement might not survive the next election.

Third, resist the temptation to tackle everything at once. BEE reform, labour-market restructuring, NHI – these are all important, but they are not where the coalition should spend its political capital right now. The sequencing lesson is clear: build credibility through delivery, generate a growth dividend, and then use that political capital for the harder reforms. Schroeder could tackle labour because Germany’s other institutions were already strong. Mbeki stabilised the macroeconomy but never got to labour because the political capital ran out. The GNU must learn from both.

Fourth, name the enemies of progress – not people, but practices. Cadre deployment that puts incompetent officials in charge of critical infrastructure. Procurement corruption that doubles the cost of every project. Regulatory capture that protects incumbents at the expense of new entrants. The coalition needs the courage to confront these practices openly, because they are the binding constraints on delivery.

And fifth, keep the eye on the prize. The prize is not ideological victory for any party. It is not BEE compliance scores or privatisation targets. The prize is growth – sustained, broad-based growth that creates jobs and raises living standards. Everything else is instrumental. If a policy contributes to growth, keep it. If it does not, change it. That is the pragmatism that Chile, Poland and India all demonstrated at their best.

The lesson from five countries is that South Africa stands at a fork. One path leads to a Poland-like trajectory – doubled living standards in a generation, unemployment halved, a country that works. The other leads to Argentina’s pattern of fleeting hope followed by reversal and decline. The difference between these paths is not resources or geography or even policy design. It is political will, coalition discipline and the courage to sequence reforms wisely.

We do not have forever. The window is open now. Let us not waste it.

On 26 January 1926, a group of scientists and a journalist from The Times climbed the stairs to an upper-floor laboratory in London’s Soho. They had come to witness a demonstration by Scottish inventor John Logie Baird. Across the screen of Baird’s so-called ‘Televisor’ flickered the ghostly image of a ventriloquist dummy and the face of its puppeteer. The picture was, as The Times admitted, ‘faint and often blurred’. All the same, what Baird had demonstrated was undeniable: recognisable images, with shades of light and dark, could be transmitted by television.

Within a generation, televisions were a fixture in homes across Europe and North America. But in South Africa, they would not appear until 1976 – fifty years after Baird’s demonstration.

This delay was not for a lack of technological or financial capacity. It was not, as Rob Krabill tells us, some ‘quirky oversight’, nor was it ‘evidence of an unsophisticated Ludditism’. It was a political choice, driven by a fear that still echoes today: the fear of outside influence.

Which brings us to Starlink.

In a recent Substack post, South African MP Songezo Zibi explained why Elon Musk’s satellite internet service is, for him, a ‘hard no’. His concerns range from Musk’s record of promoting misinformation on X, to the risk of a single individual weaponising critical infrastructure for political purposes. More broadly, Zibi worries about sovereignty – about ceding control of a nation’s information environment to a foreign billionaire with unpredictable loyalties.

These are not trivial concerns. But they are also not new. South African politicians have been making versions of this argument for nearly a century. And the history of what happened last time – when the ‘threat’ was television – should give us pause.

Read more

Over the past two weeks, I’ve had three conversations about artificial intelligence. The first was a podcast with Chinasa T. Okolo, a computer scientist with a PhD from Cornell, a former Brookings Fellow, and one of the leading African voices on AI governance (available soon). The second was lunch with Ufuk Akcigit, a world-leading innovation economist at the University of Chicago, in South Africa to help prepare the upcoming World Bank report on AI and development. The third was an informal chat with a part-time preacher.

You might assume the two academics were the more advanced users. You would be wrong.

The sixty-something preacher uses AI tools daily. He uses it for deep dives on scripture interpretation, yes, but also for translations, newsletters, budgets, event logistics, and much more. Tasks that once consumed entire evenings or required paid help now take minutes. And I can imagine a younger version of himself would have been even more hooked: Wispr Flow to record conversations with congregants, Granola to summarise and provide next steps, Claude Cowork to send out reminder emails and updates. Or, to make his services more accessible, Canva to add subtitles to his sermons, Suno to write personalised gospel music, ElevenLabs to produce personalised voice messages en masse. Just like AI, God can work in mysterious ways.

These three conversations illustrate something the development literature is only beginning to formalise. The most powerful effects of AI may not show up first in the places we expect – research labs, tech firms, universities – but in the hands of people who have always lacked access to cheap professional support.

Start with the big picture. Figure 1 plots every country’s GDP per working-age capita against its AI Usage Index – a per-capita measure of how intensively a country’s population uses Claude, Anthropic’s AI assistant. This is an imperfect proxy for overall AI use – it captures one platform rather than the full ecosystem, and likely reflects access constraints as much as underlying demand – but it offers a first, comparable signal. The relationship is stark: richer countries use AI far more. African countries cluster in the bottom left. South Africa, marked in gold, sits at an AI Usage Index of 0.38 – meaning South Africans use Claude at roughly a third of the rate their population size would predict. Israel, at the top, is at 7.0.

This is a familiar story. Technology adoption follows money. It did so with electricity and with the internet – at least initially. The question is whether AI will repeat the same slow diffusion or whether its near-zero marginal cost creates an opening that earlier technologies did not.

Figure 2 offers a reason for cautious optimism. It compares what South Africans actually use AI for against the global average. Two things stand out. First, South Africa over-indexes on education and tutoring relative to the global average. In a country where the quality of schooling remains desperately uneven, AI is being drawn toward the gap. People are using it as a tutor, not a toy. Second, South Africa has the lowest automation rate in Africa – its usage pattern sits closest to high-income country profiles, with a heavier tilt toward augmentation (iterative, collaborative use) rather than simple delegation. Users are not just asking AI to produce outputs. They are thinking with it.

Figure 3 places South Africa’s AI Usage Index alongside a selection of other countries. We sit well below the frontier economies but above most of the developing world. Whether that gap widens or narrows will depend less on awareness than on cost and complementary skills.

Over lunch, Chicago economist Akcigit described a pattern emerging from the early research on AI and firms. The relationship between AI deployment and firm size, he said, appears to be U-shaped. At one end sit solopreneurs and micro-operations – the one-person consultancy, the freelance designer, the part-time preacher. For them, AI is transformative. A single person can now produce outputs that previously required a small team: formatted documents, financial models, marketing copy, basic legal drafts, even rudimentary websites. Adoption is fast because the decision-maker and the user are the same person, and the gains are immediate.

At the other end sit large corporations with deep pockets and IT departments. They buy AI wholesale and manage the transition with dedicated project teams.

The gap is in between. Mid-sized firms – too large to pivot on a whim, too small to afford enterprise AI solutions – face the steepest adoption barriers. They lack internal IT support. The returns are uncertain. The organisational change is daunting.

South Africa’s economy is not characterised by a vast informal sector – that is a common misperception. What we do have is a large number of small, often non-employing firms that struggle to grow. Statistics South Africa’s 2023 Survey of Employers and Self-Employed counted 1.9 million non-VAT registered businesses. Most are sole operators. Almost 70% were started because the owner could not find a job. Turnover is low: more than half earn R1,500 or less per month. But most interestingly, when StatsSA asked these business owners what kind of help they needed, 34.3% said marketing – the single most requested form of assistance, and a figure that has risen by nearly seven percentage points since 2001.

Marketing. That is precisely the kind of task AI can do well and cheaply. A compelling product description, a social media post, a flyer, a customer email – these are outputs that a large language model can generate in seconds, at effectively zero cost, for someone who previously had no access to a copywriter or a marketing consultant. That also applies to business planning, bookkeeping templates, and basic legal documents. The preacher’s experience, scaled across 1.9 million small operators.

Two weeks ago, I interviewed Tyler Cowen – the George Mason University economist, blogger, and podcaster – in a public event at Stellenbosch University. His message to students was blunt.

‘You are the first generation in recent history that really does not know what your future jobs will look like,’ he said.

That uncertainty, Cowen argued, is not a reason for paralysis. It is a reason for investment. When I asked him what he would do if he were, hypothetically, rector of Stellenbosch University, he did not hesitate: ‘I’m pretty sure you all need to make major investments in using AI, teaching AI, and teaching people how to integrate AI into organisations.’

On entrepreneurship, Cowen was optimistic: ‘It will be easier and cheaper to be entrepreneurial.’ AI lowers the fixed costs of starting something – the legal template, the business plan, the marketing material, the website. For a country that desperately needs more entrepreneurs and fewer job seekers, this matters.

None of this means the risks are trivial. Cowen pushed back against a lazy reading of AI as merely a time-saver. ‘Claude or GPT writes the memo for you. That’s great. It frees up your time to write other things where you learn, but don’t think of it as just freeing up your time for leisure. It’s freeing up time to help you truly think on the things that matter.’ At a more macro level, Daron Acemoglu, Akcigit’s co-author and a Nobel laureate, has warned repeatedly about the political economy of AI: who owns the models, who captures the rents, and who sets the rules. The oligopolistic structure of frontier AI – a handful of American and Chinese companies commanding extraordinary resources – concentrates economic power in ways that should concern any developing country.

Computer scientist Chinasa Okolo raises a complementary worry about AI governance in Africa specifically. Who decides how these systems are trained? Whose languages, whose data, whose values are encoded? These are legitimate questions, and they deserve serious institutional attention.

But for a country like South Africa – which is not competing in the AI development race and is unlikely to build frontier models – the strategic calculus points in a different direction. We are consumers of AI, not producers. Ownership still matters, but in the short run the more immediate constraint is whether our people can access and use these tools effectively.

That means cheap and fast internet access, because AI tools are useless if people cannot reach them. It means integration into education, because universities that treat AI as a threat to academic integrity rather than a tool for learning will fall behind. And, perhaps most importantly, it means making AI a government priority – both within government and beyond it.

The United Arab Emirates appointed a Minister of Artificial Intelligence in 2017. That is nearly a decade’s head start. South Africa has rich administrative data, a capable tax authority, and a public that is already comfortable with digital payments. What we do not have is a state that uses these assets intelligently. Consider what AI could do for public services if we let it. A WhatsApp bot that reminds you when your vehicle licence is due – and lets you renew it without visiting a traffic department. A Home Affairs system that reads your supporting documents, flags errors before you join the queue, and tracks your application in real time so you stop phoning a call centre that never answers. A municipal platform that takes a photograph of a burst pipe or a pothole, logs the fault, routes it to the right department, and sends you an update when the repair is scheduled. A business licensing assistant that walks a first-time applicant through the municipal requirements, fills in the forms, tells her which documents to bring, and follows up when the approval stalls – because right now, as StatsSA’s own data confirms, fewer than 11% of informal business owners have a licence, and most of those who don’t will tell you the process defeated them before the market did. None of this requires frontier research. But it does require aligning incentives, building technical capacity inside the state, and sustaining systems once deployed.

The preacher has already figured this out. So have thousands of students, small business owners, and freelancers across the country. The costs of inaction – of falling further behind while the rest of the world integrates AI into daily economic life – are larger than the risks of adoption. The lesson, as usual, is to pay attention to what ordinary people are already doing.

The Indian Premier League starts this weekend. Franchises have spent over $200 million in the auction. Analysts have dissected every squad. And somewhere in a Mumbai dressing room, a team strategist is almost certainly arranging his batting order to alternate left-handers and right-handers – because everyone knows that a left-right combination unsettles bowlers.

India’s head coach Gautam Gambhir is a true believer. His assistant, Ryan ten Doeschate, recently explained the philosophy: maintaining a left-right combination throughout the batting order ‘is a big part of how he likes the set-up … we do believe that it’s a big part of strategy in these games.’ Gambhir is not alone. Former South Africa captain Graeme Smith recalled that ‘bowling to the two of us [referring to right-hander Herschelle Gibbs] in a partnership was sometimes very difficult because bowlers had to adapt … to a left-hand-right-hand combination.’ Cricket writers, commentators and fans treat the advantage as obvious.

There is an obvious logic to it: When a left-hander replaces a right-hander at the crease, the bowler must adjust line, length and field placement. The off stump shifts to the opposite side of the pitch. It is not a minor tweak – it is a geometric reconfiguration. Every time the batsmen rotate strike, the bowler supposedly loses rhythm. More disruption, more runs.

But is any of this actually true?

I recently teamed up with my colleague Krige Siebrits to find out. (Our full paper – Invisible Handedness: The Myth of Left-Right Batting Partnerships – is available here.) We downloaded ball-by-ball data from Cricsheet.org covering every men’s international cricket match across Tests, ODIs and T20Is – 96,686 partnerships and roughly 3.4 million deliveries spanning 2001 to 2025. With Claude Code, we ran every econometric test we could think of: regressions with match-by-innings fixed effects, ball-level mechanism tests, survival analysis, quantile regressions and randomisation inference.

The result? Nothing.

The effect of a mixed-hand partnership on runs scored is precisely zero. In our preferred specification, the point estimates are –0.04 in Tests, –0.10 in ODIs and 0.19 in T20Is. None is even close to statistically significant, and we can rule out effects larger than about one run in either direction – roughly 3% of average partnership runs.

Take a look at the first graph. It shows mean partnership runs by hand combination across all three formats. The pattern is striking, but not in the way you might expect. The ordering is LL (both left-handed) greater than LR (mixed) greater than RR (both right-handed). If the switching-cost theory were correct, mixed partnerships should outperform both same-hand types. They do not. Instead, the more left-handers in a partnership, the higher the score. The pattern points to left-hander quality, not left-right hand complementarity.

Here’s how to think about it: Left-handed batsmen are scarce at the international level – only 25 to 30% of rosters – but those who make it are positively selected on ability. They benefit from what psychologists call negative frequency dependence: bowlers face them less often, have less practice against them, and find it harder to settle into a rhythm. The raw advantage of mixed partnerships is real in the data. What is false is the causal attribution. It is not the combination that helps, but simply the quality of left-handed batsmen.

An Oaxaca-Blinder decomposition – a technique economists use to separate composition effects from genuine group differences – confirms this. In Tests, the quality of left-handers explains 130% of the raw gap between mixed and same-hand partnerships. Yes, more than 100%. Left-handers’ superior ability more than accounts for the observed difference.

But we went further.

Read more

Author Karen Jennings has a new book out today. First of December follows three people during the final week of November 1838, the week before enslaved people would finally be freed in South Africa. The three are: James and Caroline Kendrick, and an unnamed runaway slave making her way to Cape Town along the coast, desperate to reach it by midnight on the 31st November.

The blurb explains more:

Caroline is trapped in an unhappy marriage, in a place she hates, always longing to go home; bored, lonely, without purpose or any sense of belonging. James is forever on the move, desperate for success after a lifetime of failure and humiliation, seeing South Africa as his last great hope, preparing for the climax of his work, a bank to serve the city. Each resents the other, feeling trapped and unloved, yet with a wish for it all to change. Meanwhile the slave-apprentice, fearful of being caught before the deadline, meets others living on the coast, at the edge of society, yet always remaining alone, without any clear idea of what to expect in Cape Town.

Now HoD at the Creative Writing department at Northwest University, Karen was a writer-in-residence at LEAP when she wrote the manuscript. I recruited Karen to Stellenbosch with an explicit goal in mind: to make our research more accessible, specifically our work on Cape slavery. I’m very happy to finally see the product of this in print.

The idea is simple: not many people read academic papers. Historical fiction can make ideas travel much further. I hope First of December wins many prizes – it should – but more importantly that it finds a wide readership. If it does, our understanding of the Cape’s past (and, unavoidably, its present) can be pulled out of the archive and into everyday South African conversation.

First of December is available in the UK from today. It will be available in South Africa in August.

Karen kindly agreed to share an excerpt for paid subscribers. Enjoy.

Read more

I remember the first time I used ChatGPT. It was mind-blowing. I think I WhatsApped my wife and a few friends, saying something had fundamentally shifted. I don’t think they believed me initially, but they soon came around.

I had an even more profound experience a few weeks ago when using Claude Code for the first time. Is this what exponential progress looks like? Because what I can now ask it to do, I could not do a few months ago. It would have been utterly unimaginable two years ago.

For the uninitiated, two examples. About a week ago, I told Claude Code to recreate and expand an academic paper I published last year in the South African Journal of Science.1 I uploaded my dataset and R scripts to a folder, and over the next few minutes watched it redo months of work. After a few hours – I was busy with other stuff too – I had a completely rewritten and, more importantly, improved paper.

I have explored more plausible research leads in the last few weeks than in the last few years. I know there are sceptics – is this really research? – but I have the papers to back it up. Take the second example: Sugnet Lubbe is a statistician at Stellenbosch whose research centre has been at the frontier of biplots, a statistical classification tool. A few years ago, I mentioned to her that her tool might be useful for our rich Cape Colony data. We met once or twice, but life got in the way, and nothing happened. So, over the weekend, I asked Claude to write the paper. It produced an excellent first draft. The results, I think, overturn a long-held belief about farm structure in the Colony – results which Dieter von Fintel and I will now interrogate further, obviously with the help of Claude.

As I have tried to explain before, it is not just that these tools make us more productive; it is that they allow us to ask questions we would not have asked before. That is the superpower. Trying has always been expensive – in time and effort. Now it is not.

This is both scary and incredibly exciting. It is scary because what will we as scientists do in the future, when swarms of agents can ask – and answer – loads of interesting questions? But it is exciting because we get answers to questions we could not answer before and, thrillingly, get to ask questions we could not even imagine before.

The speed at which this technology has improved is difficult to convey without pictures, and pictures matter because humans find it hard to comprehend exponential change. On the left, in a linear scale, the METR ‘time horizon’ series – a measure of how long an AI model can work autonomously on a task before it fails – looks almost flat for years and then suddenly shoots up. Nothing happens, until everything happens. On the right, the same data on a log scale tells a different story: steady, compounding progress, year after year, until it becomes visible in everyday work. A longer time horizon is precisely what allows the tool to carry a workflow, not just complete a task.

A recent NBER working paper by Demirer, Horton, Immorlica, Lucier, and Shahidi offers a useful way to think about this. Most real work is not a list of separate tasks. It is a workflow – a chain where each step depends on the last. In economics research, you do not just ‘run a regression’; you choose a question, find or build data, clean it, write code, interpret outputs, draft the story, make the figure, respond to a referee, and do it all again. If any one link fails, the chain breaks.

What matters is not whether an AI can do one step occasionally, but whether it can do several adjacent steps reliably enough that you stop supervising every hand-off. Once the model crosses a reliability threshold, you can let it run a longer stretch and only verify at the end. In short: a modest improvement in accuracy can unlock a disproportionately large change in autonomy, because it lets the model run longer chains without collapsing. What looks like incremental progress in capability becomes a step-change in what you can actually delegate.

This is exactly what I experienced. ‘Recreate and expand’ worked because the model can now run a long enough slice of the research chain – data, code, prose, figures, robustness checks – that my role shifts from producer to editor-in-chief. Jason Crawford, writing about his experience with coding agents, describes this as humanity ‘stepping up into management’. That captures it precisely. The researcher does not disappear. The researcher becomes the manager of tireless, slightly junior research assistants who never sleep – but who still need a boss with judgement.

Very few knowledge workers – and firms – are prepared for the step-change this will cause. This is especially true of universities. Andy Hall recently tweeted that Claude Code is coming for academia ‘like a freight train’. He points to projects aiming to write one thousand empirical papers, ‘research swarms’ that generate hundreds of drafts, and even ‘LLM councils’ for peer review. In the language of the NBER paper, these are simply longer and wider chains: not one assistant helping with one link, but systems running whole stretches of the pipeline in parallel.

Scott Cunningham has done the arithmetic on what this means for publishing. Project APE at the University of Zurich has already generated over two hundred fully automated empirical economics papers. If researchers can now produce at five or ten times their previous rate, the roughly 3,800 annual publication slots across economics journals will face a flood. Acceptance rates, already around five per cent at top journals, could fall below one per cent. The bottleneck shifts from producing papers to evaluating them – and our current peer review system, staffed by overworked volunteers, cannot scale. It is an arms race: each researcher rationally speeds up, but collectively nobody gains a relative advantage while the system buckles.

At this point, the temptation is to tell the standard story: AI will replace scientists; the end is nigh; abandon ship. I do not think that will happen. David Bessis, in a letter to a PhD student anxious about AGI, offers a more useful framing. We are entering a mass extinction event for legacy career strategies – the comfortable equilibrium where technical mastery and incremental output could be laundered into status. If intellectual labour means scanning the literature, applying known techniques, and producing competent prose, then yes: machines will do it, and increasingly well.

But there is a deeper reason not to despair. Carlo Cordasco makes a striking argument against Daron Acemoglu’s influential model for AI regulation, which recommends deliberately limiting AI precision to preserve human knowledge. The problem is that the model treats the structure of knowledge as fixed. It can track how existing skills degrade but cannot capture how entirely new competencies might emerge. Cordasco’s analogy is sharp: it is like analysing the printing press while assuming that the only forms of knowledge that matter are the ones that existed before 1440. From within a world of manuscript copying, you could reasonably worry that scribes would lose their craft. You could not possibly anticipate the scientific revolution, mass literacy, newspapers, or the restructuring of entire economies around the printed word. Even a Nobel laureate’s carefully reasoned model can miss the point, because the most profound effects of a new technology are not the ones you can anticipate. Restricting the tools to preserve what we already know how to do may be precisely the wrong instinct.

But original research is not the same as valuable research. LLMs can make bad ideas look respectable, and it becomes cheap to produce work that is ‘publishable’ but pointless. In South Africa, we already have distorted incentives that reward volume – the Department of Higher Education pays universities a subsidy for each unit of research output. If a single academic can produce thousands of papers, the marginal paper is worth almost nothing, but the subsidy cheque keeps arriving. The bottleneck shifts from producing text to producing trusted knowledge.

So what does a scientist do in this world? More than ever, we have to own the upstream questions. Why this question? Why this dataset? Why should anyone care? If it is clear and useless, it is useless. AI will make it easier than ever to be clear. It will not automatically make our work valuable. That remains a human problem – judgement, taste, ethics.

For Africa, this is both an extraordinary opportunity and a serious risk. African researchers often possess something no AI can generate: intimate knowledge of local contexts, institutions, and data that the global research machine overlooks. A historian in Accra who understands precolonial trade networks, an economist in Nairobi who knows how mobile money works in rural markets – these scholars can now use AI to ask questions at a scale previously reserved for well-funded Northern universities. The playing field, for the first time, is tilting. But only if the institutions tilt with it.

The future of science will not be decided by whether we ‘allow’ AI into research. It is already embedded in the work. What matters now is the norms and institutions we build around it: transparency about what the model did, open data and code, and a more muscular culture of replication. And while we are reforming institutions, here is a low-hanging fruit that Adam Mastroianni has been hammering on: if the public paid for the research, it should not be paywalled. Major publishers maintain profit margins of around forty per cent by controlling access to publicly funded knowledge. In a world where AI submissions are about to overwhelm peer review, funnelling public money to for-profit gatekeepers is not just unfair; it is absurd. For African universities spending scarce budgets on journal subscriptions, the case is even more urgent.

These same tools can flood the world with plausible-looking nonsense. The real danger is not that machines will make us redundant. It is that machines will let us exploit incentive structures built for a different era. South Africa’s research subsidy system – designed decades ago to encourage a then-underproductive academy – needs a fundamental rethink.

If we want the exhilarating version of this future – the one where we ask better questions, test them more rigorously, and learn faster – then we have to be more demanding of ourselves and of our institutions. Reform the incentives. Open the publications. Invest in evaluation, not just production. The computational power is here. The question is whether those in power will update our institutions, responsibly and quickly, for this new scientific future.

Every December, a ritual played out at the Castle of Good Hope. The Governor and his Council of Policy would appoint four men to serve as heemraden in the Stellenbosch–Drakenstein district – the local officials who administered justice, settled disputes, and enforced the rules of rural life. The outgoing heemraden nominated double their number as candidates. The Governor picked from the list. In theory, these were simply the district’s most capable settlers.

In practice, at least half came from the richest ten per cent of households.

This is the central finding of a new study of colonial elites at the Cape I wrote with Robert Ross and Leoné Walters, recently published in the Revista de Historia Industrial.1 Using newly digitised annual tax censuses – the opgaafrolle – and lists of heemraden extracted from the Resolutions of the Council of Policy, we trace the relationship between economic rank and political authority in the wine-producing heartland of Stellenbosch and Drakenstein between 1720 and 1810.2 The picture that emerges is one of tight alignment between wealth and office – and of a colonial state that depended on that alignment to function.

Here’s why these results are interesting from a political economy/political science perspective: Colonial states cannot survive on coercion alone. The costs of repression are too high. To endure, they need local allies – people with enough to gain from the system to help run it, and enough to lose to stay loyal. At the Cape, those allies were the district’s wealthiest farmers. Understanding how that bargain worked tells us something important about colonial governance far beyond the south-western tip of Africa…

Read more

In this episode of the Our Long Walk podcast, Jonathan Schoots and I speak with Alessandra Voena, Professor of Economics at Stanford University, about power inside the household, and why it matters far beyond the home. Her work shows that bargaining power is shaped not only by income, but by institutions – inheritance, divorce law, property rights, and …

Read more

This week, News24 will host its ‘On the Record’ summit in Cape Town. The goal: to create five million new jobs in ten years. The speaker list reads like a who’s who of South African public life. President Ramaphosa will deliver the keynote. Trevor Manuel, Fikile Mbalula, Busisiwe Mavuso, Geordin Hill-Lewis, Neal Froneman, Hendrik du Toit, Imtiaz Sooliman, Helen Zille, Siviwe Gwarube and Tsakani Maluleke will all take the stage. International voices include the Chinese economist Keyu Jin, the Irish writer David McWilliams and the Indian planner Montek Ahluwalia. News24 journalists will moderate panels on the economy, growth, youth unemployment, BEE, crime and cities.

Count the professions in that room. A president, a party secretary-general, an auditor-general, a mining CEO, a mayor, a fund manager, a disaster-relief founder and a stable of journalists. Now count the labour economists. Zero. Not one person who has run a randomised experiment on job search in Soweto. Not one who has evaluated a wage subsidy or measured the cost of spatial mismatch. The people who have spent their careers studying this exact question are not in the room. As one friend said to me: would you organise a cancer conference without inviting an oncologist?

Over the past decade, South Africa has produced some of the best experimental evidence on unemployment anywhere in the developing world. Published in the discipline’s top journals – the American Economic Review, the American Economic Journal: Applied Economics, the Journal of Development Economics, World Development – and cited globally. This work will be entirely absent from the country’s biggest jobs summit. It matters because ignoring what we already know guarantees we will waste time and money repeating what does not work.

The figure above maps the network of economists behind this research – a co-citation network drawn from all the references in 19 of what I judge to be the best recent economics papers on South African unemployment. Two scholars are linked when they are cited together; larger nodes are cited more frequently; orange dots are the authors of the papers themselves. Not one of them is speaking at the conference. In the conference booklet produced for participants, more than seventy people are thanked for the time and expertise they contributed. Only two are professors. Only one – Haroon Bhorat – appears on the list above. If the organisers wanted the world’s leading experts on the topic, based on this network graph, they might have invited David McKenzie, Marianne Bertrand or Simon Quinn. If they wanted South African scholars, why not Rulof Burger, Neil Rankin or, indeed, Haroon Bhorat?

How could this research make a difference to the conference? The largest cluster of findings concerns information frictions – the idea that employers and job-seekers simply do not know enough about each other. Eliana Carranza, Robert Garlick, Kate Orkin and Neil Rankin, writing in the American Economic Review in 2022, showed that giving young South Africans a credible skills assessment they could share with employers raised both employment and earnings. Martin Abel, Rulof Burger and Patrizio Piraino found that reference letters from former employers – a practice virtually absent in South Africa’s low-skill labour market – increased callbacks by 60 per cent. Andrea Kiss and colleagues demonstrated that job-seekers who receive better information about their own comparative advantage redirect their search and earn more. Laurel Wheeler and co-authors showed that training job-seekers to use LinkedIn raised employment by 10 per cent, with effects lasting at least a year. And in a striking experiment, Marianne Bertrand and Bruno Crepon found that simply teaching small firms about labour law – correcting their misperceptions of how burdensome it actually is – increased their hiring by 12 per cent.

A second body of work addresses spatial mismatch, the apartheid-era legacy that placed black workers far from where jobs concentrate. Abhijit Banerjee and Sandra Sequeira gave transport subsidies to young job-seekers in Soweto and found something unsettling: when subsidised workers searched more in Johannesburg’s CBD and still could not find work, they updated their beliefs downwards and gave up. Cally Ardington and colleagues showed that when grandparents in rural KwaZulu-Natal begin receiving the state pension, their grandsons are significantly more likely to migrate for work – the pension finances the search.

Cash transfers tell a similar story. Haroon Bhorat and Timothy Kohler, evaluating the Social Relief of Distress grant in World Development, found that it boosted job search and even short-term employment – but the employment effect vanished after a single quarter. Alessandro Tondini found the same pattern for the Child Support Grant. Money helps people look for work. It does not create the work.

And the big-picture reviews are sobering. David McKenzie’s critical assessment, Carranza and McKenzie’s 2024 survey in the Journal of Economic Perspectives, and Crepon and Van den Berg’s Annual Review piece all converge: traditional active labour market policies – large-scale vocational training, wage subsidies, public works – produce, at best, employment gains of around two percentage points. South Africa’s own flagship programme, the Employment Tax Incentive, had what Amina Ebrahim and Jukka Pirttila diplomatically called ‘fairly limited’ effects.

The most promising interventions are cheap and scalable: skills assessments, reference letters, plan-making prompts, digital job platforms. But they move the needle by single-digit percentage points. None of them, individually or collectively, can absorb eight million unemployed South Africans. There is also an awkward implementation gap: private-sector initiatives like Harambee and Yes4Youth have been far more eager than the Department of Labour to use this research to design their services, yet their reach remains a fraction of the DoL’s employment centres. The evidence exists; the institution best placed to act on it at scale largely ignores it.

Which brings us to the uncomfortable gaps. The research is excellent on the margins – how to make job search more efficient, how to reduce information frictions between workers and firms. But it is largely silent on the structural constraints that create mass unemployment in the first place: rigid labour laws in a low-productivity economy, BEE and other compliance costs that deter small-firm hiring, minimum wages set above market-clearing levels for low-skill work, and barriers to informal-sector entry that are, by developing-country standards, unusually severe. Emily Breza and Supreet Kaur’s recent review notes that South Africa is a genuine outlier: high unemployment combined with an abnormally small informal sector. Juan Pablo Rud and Ija Trapeznikova reach a similar conclusion: labour market frictions matter more than firm entry costs, and South Africa’s formal sector is extraordinarily large relative to its employment rate.

The honest answer most labour economists would give off the record: only sustained economic growth – four to five per cent a year for a decade – will meaningfully reduce unemployment. The Western Cape’s declining unemployment rate offers a domestic proof of concept. But this truth is politically inconvenient, because it implies removing restrictions rather than adding programmes.

So why are economists absent from this conference? I am an economist, and I think the answer begins with us. We are terrible salespeople. Academic incentives reward publications in the American Economic Review, not op-eds in the Sunday papers. We publish in journals no policymaker reads, use jargon no journalist translates, and then wonder why no one invites us to the table.

Our answers are also, frankly, small. The interventions we can rigorously evaluate – reference letters, plan-making prompts, skills assessments – move employment by two percentage points. Real, yes. But not the transformative headline a conference like this wants to sell.

And our hardest answers are unwelcome. The structural reforms that would make the largest difference – a working school system, greater labour market flexibility, BEE reform, minimum wage recalibration, informal sector deregulation – are politically toxic. Conference organisers want hope and solutions, not trade-offs and discomfort.

This raises the deeper question. What is the theory of change for a summit like this? If the binding constraint on job creation is political will – and I suspect it is – then adding more knowledge will not help. If the constraint is that decision-makers genuinely do not know what works, then excluding the researchers who do know is self-defeating. Either way, we economists bear part of the blame for staying in our lane.

The risk is that summits like this become substitutes for action; that the act of talking about unemployment creates the comfortable illusion of doing something about it. Or worse: that they entrench the wrong narrative, more programmes and more training, while the evidence points elsewhere – less regulation, more growth, better information.

But the self-criticism must cut both ways. A genuinely useful conference would require economists willing to leave the seminar room. Who can explain a randomised experiment in three sentences? Who can make spatial mismatch vivid to a businessperson or a politician? Until we learn to do that, we have no right to complain about not being invited.1

Five million jobs in ten years is a fine ambition. But ambition without evidence is just a slogan. And evidence without communication is just a PDF behind a paywall no one reads.

One of the most influential questions in economic history is why some societies became rich while others, equally sophisticated for long periods, did not. In their recent book, a superhero team of economic historians – Avner Greif, Joel Mokyr, and Guido Tabellini – offer a clear and powerful way of approaching this problem. Rather than focusing primarily on geography or resources, they emphasise social organisation. Before modern states could reliably provide law, security, or welfare, societies had to organise cooperation locally. How they did so mattered enormously for long-run development.

In China, the dominant solution was the clan: a large, kin-based organisation that combined mutual insurance, dispute resolution, education, and moral discipline. Clans relied on loyalty to family, respect for elders, and shared ancestry. Much of Africa developed similar kinship-based systems – lineage networks, age-set organisations, and rotating savings groups like the stokvel, which still thrives in South Africa today. In Europe, cooperation increasingly took place through corporations: purpose-built organisations such as guilds, towns, monasteries, universities, and later joint-stock companies. Membership was based on rules rather than blood. Corporations were open to strangers, governed by formal procedures, and allowed people to belong to several organisations at once.

These European corporations helped generate contract law, representative institutions, and habits of impersonal cooperation. They supported the knowledge accumulation that eventually fed into the Industrial Revolution. Chinese clans, while effective at local governance, tied cooperation more closely to kinship and hierarchy. Over centuries, these contrasting organisational forms shaped political institutions, legal systems, and patterns of innovation.

Prosperity, in this view, is about more than technology or ‘culture’. It is about how people organise cooperation when trust is limited and enforcement is costly. That framing also raises a natural question: what happens when a new way of organising cooperation becomes available – one that sits outside the familiar clan-versus-corporation divide?

Read more

I’m interviewing Tyler Cowen on Friday the 13th. (What could go wrong?) The event is open to all; no need to RSVP, just show up.

As a paid subscriber, you’re also welcome to send me your questions to ask Tyler. Please send all questions to johan@ourlongwalk.com.

More on Tyler: Tyler Cowen is the Holbert L. Harris Chair of Economics at George Mason Univers…

Read more