In 1940, the British government asked its citizens to accept that from now on, every adult would receive exactly one egg per week. Four ounces of bacon. Two ounces of butter. Eight ounces of sugar. Two ounces of tea. The quantities were precise, printed on ration books, enforced by law. And the public response was not what you might expect.

Mass Observation, the social research organisation that had been recording British opinion since 1937, surveyed attitudes to rationing throughout the war. More than half of those surveyed approved. Only 14 per cent expressed dissatisfaction.1 This was not reluctant compliance with wartime necessity. People actively supported the system. They reported feeling that rationing was fair, that it bound the country together, that the duchess and the dustman were finally eating from the same menu.

Now jump forward seventy years. In 2010, a different government asks the British public to accept a different kind of sacrifice. Spending cuts, benefit reductions, frozen public sector wages, a squeeze on services. The stated reason is similar: the country faces an emergency, everyone must play their part. But the response is profoundly different. Instead of solidarity, there is anger. Instead of compliance, there is a slow-burning resentment that reshapes British politics for the next decade.

The gap between these two moments is not explained by the severity of the sacrifice. Rationing was objectively more restrictive than austerity. People had less food, less fuel, less freedom of choice about basic consumption. The gap is explained by something else entirely: whether the sacrifice felt fair.

This piece and the next are about the deal that exists between citizens and the institutions that govern their financial lives. It is about what happens when that deal holds, what happens when it breaks, and why the feeling of fairness matters more to your financial brain than the actual size of the loss. Along the way, we will pass through a bank run that was entirely rational, an ideology dressed up as arithmetic, an inflation crisis that damaged something deeper than purchasing power, and a government wage scheme that briefly restored a contract most people had forgotten existed.

This connects to what we have already seen. Fear makes you sell at the bottom. The herd pulls you towards whatever everyone else is doing. But underneath both of these, there is a more fundamental question: do you believe the system is fair? Your answer to that question determines how much damage everything else can do. It also shapes the narratives we construct to explain crises, the cognitive toll of financial pressure, the silence around money in British culture, the identity we attach to property, and the financial trauma we inherit from our parents.

We should start, though, with a laboratory experiment about splitting money with strangers.

The fairness instinct

In 1982, three German economists named Werner Guth, Rolf Schmittberger and Bernd Schwarze published a paper that should have settled an argument. The experiment was simple. Two players. One gets a sum of money and proposes how to split it. The other can accept the split (both keep their share) or reject it (both get nothing). Rational self-interest predicts that the second player should accept any offer above zero. A pound is better than nothing. A penny is better than nothing.

That is not what happened. Offers below about 20 per cent of the total were rejected roughly half the time.2 People walked away with nothing rather than accept a split they considered unfair. The experiment has been replicated hundreds of times, in dozens of countries, with stakes ranging from pocket change to several months' salary. In one well-known set of studies, the stakes were equivalent to three months' income for the participants. Even then, the rejection rate for low offers remained high. The basic finding holds: human beings will pay a real financial cost to punish what they perceive as unfairness.

The cross-cultural variation is telling. When Joseph Henrich and his colleagues ran ultimatum games in fifteen small-scale societies around the world, they found striking differences.3 Among the Machiguenga of Peru, offers averaged 26 per cent and were almost always accepted. Among the Lamalera whale hunters of Indonesia, offers regularly exceeded 50 per cent. The differences mapped onto each society's economic structure: communities with more market integration and more cooperative production made more generous offers and rejected low offers more often. Fairness norms are not hardwired at a single setting. They are calibrated by the economic world you grow up in.

In 1999, Ernst Fehr and Klaus Schmidt formalised this into a mathematical model.4 Their paper in the Quarterly Journal of Economics proposed that people experience two kinds of inequality discomfort. Being worse off than others hurts. But being better off than others also produces discomfort, just less of it. In their model, the pain of disadvantageous inequality is roughly twice as strong as the discomfort of advantageous inequality. The exact ratio varies across studies, from about 1.6 to 2.7 depending on the methodology, but the direction is consistent. We hate getting less than our fair share more than we feel guilty about getting more.

George Akerlof and Robert Shiller identified fairness as one of the five "animal spirits" driving economic behaviour.5 Their argument was that mainstream economics had made a catastrophic error: modelling humans as pure self-interest maximisers, when actually the perception of fairness shapes every significant economic decision, from wage negotiations to consumer spending to responses to government policy.

What makes the ultimatum game so useful as a lens for crisis psychology is that it strips away every complicating factor. There is no ideology, no political party, no newspaper editorial telling you how to feel. There are just two people and a sum of money. And even in that bare setting, with real money on the table, people consistently choose to punish unfairness at a cost to themselves. They do it quickly, instinctively, without agonising over the decision. The fairness assessment comes first. The financial calculation comes second.

This matters for crisis psychology because it means people's willingness to accept financial hardship depends heavily on whether the sacrifice feels fairly distributed. When it does, compliance is high and psychological damage is limited. When it does not, the same objective hardship produces rage, political upheaval, and lasting institutional distrust. The size of the loss is not the variable that matters most. The distribution is.

Wartime, the fair sacrifice

The British rationing system that began in January 1940 lasted, in various forms, for fourteen years. Bacon, butter and sugar were rationed first. Meat followed in March 1940, tea and margarine later that year. By mid-1942, practically everything edible was either rationed or subject to price controls. Bread, which had not been rationed during the war itself, was rationed from 1946 to 1948, a detail that still surprises people. The last item to come off rationing was meat, in July 1954. An entire generation grew up never knowing what it meant to walk into a shop and simply buy what you wanted.

The Mass Observation diaries from this period, held at the University of Sussex Special Collections, are full of complaints about specific items. People grumbled about the monotony, about queuing, about the quality of dried egg. But the complaints existed within a framework of broad acceptance. The system was considered fair because it was visibly equal. The ration book did not ask how much you earned. A duke and a docker got the same allocation of cheese.

This was not accidental. The government faced a genuine choice between price controls, which would have let the market allocate scarce goods to whoever could pay the most, and per-capita rationing, which guaranteed equal access regardless of income. They chose rationing. The economists advising the government understood that the system needed to feel fair to function. If wealthy households had been seen stockpiling while ordinary families went without, the entire cooperative structure would have collapsed.

Mark Harrison's research on wartime economic psychology shows that governments can persuade populations to accept reduced consumption, but only through specific mechanisms.6 Appeals to patriotism work. Appeals to fairness work. Appeals to shared sacrifice work. Appeals to abstract economic theory do not. The Rapid Transition Alliance's analysis of WW2 consumption patterns confirms the speed at which behavioural change happened once the public accepted the underlying fairness of the system. People did not just tolerate rationing. Many of them found it clarifying: the anxiety of competitive consumption was temporarily removed.

The enforcement dimension mattered too. Black market prosecutions were widely publicised. When people were caught cheating the system, the punishment was visible, reinforcing the message that the rules applied to everyone. Sentences could be severe: fines, imprisonment, public shaming in the local press. The fairness compact was maintained not just by voluntary compliance but by demonstrated consequences for those who broke it. This is a point that often gets lost in nostalgic accounts of wartime solidarity. The solidarity was real, but it was also policed. The government understood that one visible cheater could do more damage to public compliance than a month of deprivation.

Ina Zweiniger-Bargielowska's history of the period documents how this fairness framework survived even the most severe deprivations.7 When bread was rationed after the war, at a point when people were exhausted and impatient for normality, the system held because the principle of equal sacrifice was already deeply embedded. People did not like it. But they accepted it as legitimate.

There is a telling detail in the Mass Observation data that often gets overlooked. When asked about rationing, people did not simply say they accepted it. Many expressed something closer to relief. The pre-war anxiety of keeping up appearances, of competitive consumption, of judging and being judged by what you could afford to put on the table, was temporarily suspended. Everyone had the same amount. The comparison game was, for a brief period, impossible to play. Several diarists described this as a kind of freedom. It was not freedom from want. It was freedom from the constant, grinding awareness of inequality.

The wartime experience established something that economists would spend decades trying to explain away: that human beings will accept enormous material deprivation, voluntarily and even with a degree of good grace, provided they believe the burden is shared equally. The sacrifice has to feel fair. Not optimal, not efficient, not economically rational. Fair.

The broken deal

On 14 September 2007, customers began queuing outside Northern Rock branches across the north-east of England. The images, broadcast on every news bulletin, looked like something from a different era. A bank run in modern Britain. It seemed irrational, hysterical, a failure of public understanding.

It was none of those things. Hyun Song Shin's analysis in the Journal of Economic Perspectives reframes the Northern Rock episode entirely.8 The UK's deposit insurance scheme at the time guaranteed 100 per cent of the first 2,000 pounds in any account, and 90 per cent of the next 33,000 pounds. A depositor with 35,000 pounds in Northern Rock therefore stood to lose 3,300 pounds if the bank failed. Queuing for hours to withdraw your money was not panic. It was arithmetic. The Bank of England's Governor subsequently confirmed that the depositors' behaviour was entirely rational given the legal framework.

But the Northern Rock story matters for this chapter not because of what the depositors did, but because of what happened next. The government extended a full guarantee on all Northern Rock deposits, stopping the run. Then, in September 2008, the financial system cracked open properly. Royal Bank of Scotland received 45.8 billion pounds in public funds, giving the government an 84 per cent stake. Lloyds Banking Group got 20.3 billion pounds for a 43 per cent stake. The total government commitment to stabilising the banking sector reached approximately 137 billion pounds, though the eventual net cost was substantially lower, around 23 billion.9

The public watched this happen. They watched the institutions that had caused the crisis receive the largest public subsidy in peacetime history. And then, in 2010, they were told that the country could not afford its current level of public spending and that ordinary people would need to accept cuts.

Mark Blyth's Austerity: The History of a Dangerous Idea traces the intellectual sleight of hand that made this possible.10 The debts were originally private. Banks had made bad bets and lost. But through the mechanism of the bailout, private debt was transformed into public debt, and public debt was then used to justify public spending cuts. Blyth traces the austerity argument back through two centuries of economic thought, from Locke and Hume through the Austrian school and German ordoliberalism, and finds the same pattern repeating: austerity is presented as pragmatic necessity when it is actually ideological preference. And it consistently delivers low growth with increased inequality.

The human cost was documented by Vickie Cooper and David Whyte in The Violence of Austerity.11 They compiled testimony from across the UK: disability benefit claimants found fit for work who later died, mental health services stripped to skeleton capacity, food bank use rising from tens of thousands to millions. A letter signed by 442 psychotherapists, counsellors and academics described what they were seeing as "anti-therapeutic practices" causing avoidable suffering and, in documented cases, avoidable deaths.

Research published in Social Forces in 2024 examined what happened to the families who bore the heaviest burden. Single mothers lost between 20 and 30 per cent of their household benefit income.12 The study tracked the consequences: increased financial worry, material hardship, deterioration in mental health, and measurable increases in adolescent socio-emotional difficulties. The children of austerity absorbed the stress of their parents' financial precarity in ways that will shape their own relationship with money for decades to come. Financial trauma transmits across generations. The children who watched their mothers counting coins at the supermarket checkout in 2013 are now entering the workforce with a set of money instincts that pre-date their first payslip.

The fairness rupture was not instant. In the immediate aftermath of 2008, public anger was directed at banks. But the narrative shifted. By 2010, the dominant framing, supported by most of the press, was that the country had been "living beyond its means." The crisis was recast from a banking failure to a public spending problem. The people who had caused the crisis paid bonuses. The people who had not caused it paid with their services, their benefits, and in some cases their health.

There is a useful thought experiment here. Imagine you are explaining the sequence of events to someone who knows nothing about British politics. You would say: the banks made catastrophic bets; the public bailed them out; the public was then told the country had no money; public services were cut; the banks paid bonuses; the people whose services were cut were told this was necessary because the country had been living beyond its means. Your listener would say: that cannot be what happened. But it is.

The language matters. The phrase "living beyond our means" performs a specific rhetorical function. It implies collective responsibility for what was, in reality, a failure of financial regulation and banking judgment. The "our" does an enormous amount of work in that sentence. It distributes blame evenly across a population that was overwhelmingly blameless. And it was repeated so often, by so many political and media voices, that it became the accepted explanation. The greatest trick austerity pulled was convincing ordinary people that the crisis was their fault.

If you wanted to design a policy sequence that would destroy public trust in the fairness of the economic system, you could not improve on what actually happened between 2007 and 2015.

Rationing held for fourteen years because everyone could see the burden was shared, while austerity collapsed that belief in a fraction of the time. In the next part: the three layers of trust that the post-2008 years wore down, and the furlough scheme that briefly made the old contract feel real again. Thanks for reading.

Next in this series: The three layers of trust, and what broke them. If this was useful, subscribing gets you the rest of the series as it lands.

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On the morning of 14 September 2007, a queue formed outside a branch of Northern Rock in Kingston upon Thames. By mid-morning, queues had appeared outside branches across the country. Over the following three days, depositors withdrew approximately £4.6 billion, roughly a quarter of the bank's retail deposits.1 It was Britain's first bank run in 150 years.

The facts of Northern Rock's situation were, at that point, publicly available. The Bank of England had announced an emergency lending facility. Deposits up to £35,000 were covered by the Financial Services Compensation Scheme. The bank was still solvent in the technical sense. None of this mattered to the people standing in the rain outside branches in Kingston, Golders Green, and Cheltenham. What mattered was the queue. The queue was the signal.

Paul Goldsmith-Pinkham and Tanju Yorulmazer analysed the spillover effects in a 2010 paper for the Journal of Financial Services Research.2 They found that both the bank run and the subsequent bailout announcement had statistically significant effects on the wider UK banking system, measured by abnormal returns on bank stocks. Banks that relied heavily on wholesale market funding, as Northern Rock had, were disproportionately affected. The contagion wasn't irrational panic. It was a rational response to new information about the fragility of a particular funding model, amplified and accelerated by the physical visibility of the run. Alliance & Leicester, Bradford & Bingley, and HBOS all saw their share prices fall sharply in the days following the Northern Rock queues. The visible queue had become a signal that triggered wholesale-market herding, as institutional investors reassessed their exposure to banks with similar funding structures.

There is something very British about the Northern Rock story. Not the bank run itself, which followed classical patterns. But the mechanism of contagion: the orderly queue. People joined the queue not because they had analysed Northern Rock's balance sheet, but because other people were queueing. In Britain, a queue is both a social signal and a social obligation. If enough people are standing in line, the assumption is that there's a good reason. The Northern Rock queue was, in Bikhchandani's framework, a perfect information cascade made physical. Each person who joined confirmed the signal for the next.

The same dynamic, though less photogenic, played out during the pandemic panic buying of March 2020. Lorry Taylor, writing in the Journal of Contingencies and Crisis Management in 2021, found that perceived scarcity drove purchasing decisions more than actual scarcity.3 Social media amplified the signal: photographs of empty shelves circulated on WhatsApp and Twitter, creating a feedback loop in which the fear of running out caused the running out. Moinuddin Naeem, in a 2021 study, identified a specific social-media cycle in which images of empty shelves generated anxious posts, which generated more buying, which generated more images of empty shelves.4 The actual supply of toilet paper, pasta, and flour was, in most cases, adequate. The perceived supply, shaped by photographs that spread faster than delivery trucks could restock, was not. Once the herd started buying, not buying felt like negligence.

And then there is property. The British relationship with buy-to-let investment has all the hallmarks of herd behaviour. Research has found that the growth in private landlordism was driven not by professional investors with diversified strategies, but by small portfolio owners with little experience, attracted by paper profits and an almost religious cultural belief that house prices only go in one direction. Over half of landlords surveyed cited interest rates as a deciding factor, and 40 per cent said they were influenced by recommendations from intermediaries, brokers, and the same social networks that amplified every other herd signal described in this post.5 When prices rose, more people bought. When more people bought, prices rose. The circularity was self-evident to anyone standing outside it and invisible to anyone inside it.

The Investment Association's fund flow data tells the same story from a different angle. In 2020 and 2021, when UK equity markets were rallying, retail investors poured £18.4 billion into equity funds. In 2022, when markets fell, they pulled out £18.2 billion.6 The symmetry is almost comic. Money in at the top, money out at the bottom, driven by the same herding impulse that sent Newton back into South Sea Company stock and depositors into Northern Rock queues. The faces change. The pattern doesn't.

What makes the British version of herding distinctive is its social register. In America, herding is loud. The WallStreetBets crowd celebrated their trades publicly, performatively, with rocket emojis and screenshots of six-figure gains. In Britain, herding is quieter but no less powerful. It happens over dinner when a colleague mentions their buy-to-let yields. It happens at the school gate when another parent asks which platform you use for your ISA. It happens in the silent observation of what other people seem to be doing with their money, filtered through a culture that finds talking about money vulgar but finds keeping up with the neighbours essential. The British herd moves discreetly, which makes it harder to see, and harder to resist.

What this means

The tempting conclusion from all of this is that herding is a flaw, a cognitive weakness that education and self-awareness can correct. If we just understood the mechanics of information cascades, recognised the brain's conformity signal, and remembered the lessons of every bubble from the South Sea to GameStop, we could resist.

That's too easy, and it probably isn't true.

Herding is not a character flaw. It is a species-level cognitive pattern with deep evolutionary roots. For most of human history, copying the behaviour of others was an excellent survival strategy. If everyone in your group started running, the smart move was to run first and ask questions later. The people who stopped to conduct independent analysis of whether the threat was real were, on average, the people who got eaten. Our brains are built to follow the crowd because, for the vast majority of our evolutionary history, following the crowd kept us alive.

The problem is that financial markets are an environment our brains did not evolve to navigate. The signals that were reliable on the savanna (if everyone is doing it, there's probably a good reason) are unreliable in a market (if everyone is buying it, the price is probably too high). The error-detection signal that Klucharev identified, the one that fires when you disagree with the group, doesn't distinguish between "the group has spotted a predator you haven't" and "the group is piling into a cryptocurrency they don't understand." The neural machinery is the same. The context is completely different.

And here is the part that makes this a systemic problem rather than a personal one. The system actively produces herding. Fund managers are benchmarked against each other, which rewards following the crowd and punishes deviating from it. Financial regulation assumes that individual investors make independent decisions, an assumption that every part of this series will challenge. Media coverage amplifies consensus and gives less airtime to dissenting views. Social media accelerates the feedback loop to the point where cascades form and collapse in hours rather than months.

The people who pay the highest price for herding are retail investors, the individuals with the least information, the strongest social-proof signals, and the fewest institutional protections. When a fund manager herds and loses money, they lose a performance bonus. When a pension holder herds and sells their equity holdings at the bottom of a crash, they lose years of retirement income that they may never recover. The career incentives that drive professional herding are a problem. The absence of any equivalent protection for retail investors is a bigger one.

Think about what this means in practice. A 58-year-old teacher in Leeds with a defined-contribution pension has no career incentive to herd. She has no career in investment management. She is not benchmarked against other teachers' pension decisions. She has, in theory, every reason to ignore what the crowd is doing and focus on her own time horizon, her own risk tolerance, her own retirement date. But she is also human. She watches the news. She sees colleagues moving their pensions to "safer" options after a market fall. She feels the brain's conformity signal telling her that disagreeing with the crowd is an error. And the pension platform she uses, with its daily valuation updates and its red-and-green performance indicators, is feeding her exactly the kind of real-time social information that makes herding irresistible. The system is designed as though she is a rational, independent decision-maker. She is a social animal watching the herd.

Newton, for what it's worth, never fully recovered from the South Sea Bubble. He forbade anyone from mentioning the company's name in his presence for the rest of his life. He had calculated the motions of the planets. He could not calculate the pull of the crowd. Three hundred years later, neither can we. The question is whether we can build financial systems that account for that fact, instead of pretending it doesn't exist.

The herd is one half of the story of how crises move through a population. The other half is the deal we believe we have with the institutions that hold our money, and what happens when that deal breaks. That is where this series goes next, starting with wartime rationing and the fairness instinct.

Next in this series: Why people accept some sacrifices but not others. If this was useful, subscribing gets you the rest of the series as it lands.

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Further reading:

• Savills (2024) UK Housing Market Research. Over-65s generating GBP 10.1 billion annually from buy-to-let; total housing wealth held by over-65s exceeding GBP 2.6 trillion.

• Shiller, R. (2000, 2005, 2015) Irrational Exuberance. Princeton University Press. Three editions tracking the dot-com bubble, the housing bubble, and the bond market bubble. A masterclass in how herd behaviour creates asset price distortions.

Herding runs from Isaac Newton, who lost a fortune buying back into the South Sea Bubble in 1720, to the fund managers whose careers reward losing money the same way as everyone else. Two ideas matter here: the information cascade, in which rational people suppress their own doubts because the crowd looks better informed, and the neuroscientist Vasily Klucharev's finding that the brain treats disagreement with a group as an error. The herd has since moved onto your phone.

The digital herd

On 11 January 2021, shares in GameStop, a struggling American video game retailer, were trading at around $20. Seventeen days later, on 28 January, the stock hit an intraday high of $483. Then it collapsed. By mid-February it was back below $50.1

What happened in between was the most visible demonstration of digital herding in financial history. Members of the Reddit forum WallStreetBets, a community of roughly two million amateur traders at the time, had identified that several large hedge funds held significant short positions in GameStop. A short position is a bet that a stock will fall. If you can force the price up instead, the short sellers face theoretically unlimited losses and may be forced to buy shares to close their positions, pushing the price higher still. This is a short squeeze, and WallStreetBets decided to engineer one.

What made GameStop different from previous short squeezes was the mechanism of coordination. There was no central organiser. Nobody was issuing instructions. The subreddit functioned as a real-time information cascade, with users posting screenshots of their positions, celebrating gains, mocking hedge funds, and shaming anyone who suggested selling. The social dynamics were as important as the financial logic. Selling wasn't just leaving money on the table. It was betraying the group. The language of the forum, "diamond hands" for holders, "paper hands" for sellers, made conformity a badge of identity.

Long and colleagues, in a 2023 study in The Financial Review, analysed 10.8 million Reddit comments and found that sentiment on WallStreetBets directly affected GameStop's intraday returns at 5-, 10-, and 30-minute frequencies.2 A 2024 study found that positions initiated at peak WallStreetBets attention produced average holding-period returns of negative 8.5 per cent.3 The people who arrived at the peak of the herd's enthusiasm, which is to say the majority, lost money.

The GameStop saga was American, but the dynamics it demonstrated are running constantly in UK markets. The FCA's 2022 cryptoasset consumer research found that 4.97 million UK adults, roughly 9 per cent of the adult population, owned cryptocurrency.4 Of those who held crypto, 34 per cent had first learned about it from friends or family. Fewer than half said they understood the underlying technology. And 45 per cent reported losses on their holdings. Bitcoin, the most widely held crypto asset, had fallen by roughly two-thirds from its November 2021 peak by the time the FCA survey was conducted in mid-2022.

Robert Cialdini described the mechanism in 1984 in his book Influence.5 Social proof, the tendency to look at what other people are doing when deciding what to do yourself, operates most powerfully under two conditions: when you are uncertain what to do, and when the people you observe are similar to you. Both conditions were met perfectly during the UK crypto boom. People who knew little about blockchain watched their friends and colleagues making returns they couldn't ignore. Social media made the gains visible and the losses invisible. The cascade formed fast.

What separates digital herding from its historical predecessors is speed and transparency. In 1720, Newton watched the South Sea Bubble develop through conversations in coffeehouses and letters from friends, over weeks and months. In 2021, retail investors watched GameStop's price move in real time on their phones, with a live commentary thread explaining (or misexplaining) what was happening. The time between seeing the herd move and joining it has compressed from months to minutes. The information cascade that Bikhchandani described as a sequential process, one person watching the next, now happens simultaneously across millions of screens. The dynamics are the same. The speed is different. And speed, in financial markets, kills.

There is also a structural change that deserves attention. In Newton's time, and even in 2007, joining a financial herd required some friction. You had to contact a broker, or at least walk to a bank branch. That friction was, in a quiet way, protective. It gave people time to reconsider. Trading apps have eliminated that friction entirely. A 22-year-old in Birmingham can see a cryptocurrency surging on TikTok and buy it within ninety seconds without speaking to another human being. The time gap between herding impulse and herding action has collapsed to nearly zero. The brain's error-correction mechanisms, the ones Klucharev identified, don't have time to operate. The herd moves, and you move with it, before you've finished thinking about whether you should.

When the herd is wise, and when it isn't

There is a version of the herding story that treats all crowd behaviour as pathological. That version is too simple.

In 2004, the journalist James Surowiecki published The Wisdom of Crowds, arguing that under certain conditions, the collective judgement of a group reliably outperforms the judgement of any individual member.6 His conditions were specific: the group needed diversity of opinion, so that members brought different information and perspectives; independence of decision, so that each person formed their own view without pressure to conform; decentralised knowledge, so that different people had access to different local information; and a mechanism for aggregation, so that individual judgements could be combined into a collective answer.

The jelly-beans-in-a-jar experiment is the classic illustration. Ask a hundred people to estimate the number of jelly beans in a jar. Most individuals will be wildly wrong. The average of all their guesses will be remarkably close to the true number. Prediction markets work on the same principle: when diverse, independent bettors trade on the probability of an event, the market price tends to be more accurate than any individual forecast.

The problem is what happens when any of Surowiecki's conditions breaks down. Herding, by definition, destroys independence. When people observe and mimic each other's choices, the diversity of information in the group collapses. You no longer have a hundred independent estimates. You have a hundred copies of the same few estimates, amplified by repetition.

In 2006, Matthew Salganik, Peter Dodds, and Duncan Watts at Columbia University ran an experiment that demonstrated this with uncomfortable precision.7 They created a website called MusicLab, where 14,341 participants could listen to songs by unknown bands and choose which ones to download. Some participants were assigned to an independent condition: they could see the songs but not how many times each had been downloaded. Others were assigned to social-influence conditions, where download counts were visible.

In the independent condition, quality predicted success. Better songs got more downloads. In the social-influence conditions, the same songs existed, but the results were profoundly different. Popular songs became more popular and unpopular songs became less popular, regardless of quality. Inequality between successful and unsuccessful songs increased. And, most disturbingly, the outcomes became less predictable. A song that topped the charts in one social-influence world might languish in obscurity in another. The initial random advantage of a few early downloads snowballed into permanent dominance, or didn't, depending on what the first few people happened to do.

A 2008 follow-up by the same team went further. They inverted the download counts, telling participants that the least popular songs were the most popular and vice versa. Even the songs with fabricated popularity benefited. Social influence was powerful enough to override quality signals entirely. The herd followed fabricated signals because the signal of "other people chose this" was more compelling than their own ears.

The parallel to financial markets should be obvious. When stock price movements become the primary signal that other investors watch (and they do), price itself becomes social proof. A rising price says "other people are buying this." A falling price says "other people are selling this." And those signals trigger the same conformity mechanisms that Klucharev identified in the brain, the error signal that fires when you disagree with the crowd.

This is where contrarian investing gets interesting, and where the data confounds intuition. If herding pushes prices away from their fundamental values, then going against the herd should, on average, produce excess returns. The evidence, broadly, supports this. Werner De Bondt and Richard Thaler showed in 1985 that stocks with the worst recent performance subsequently outperformed stocks with the best recent performance, a pattern they attributed to overreaction by herding investors.8 Lakonishok, Shleifer, and Vishny found in 1994 that value strategies, which are inherently contrarian, produced higher returns not because they were riskier but because they exploited predictable errors in crowd behaviour.9

But contrarian investing requires a psychological tolerance that most people, including most professionals, do not possess. You are buying what everyone else is selling, or selling what everyone else is buying. Your brain is treating every trade as an error. Your colleagues think you've lost the plot. Your clients are calling. And if the position goes against you before it comes good, which it often does, the career risk identified by Scharfstein and Stein kicks in with full force. Being right eventually is cold comfort if you've been fired in the meantime.

This is the genuine difficulty at the heart of the herding problem. The crowd is sometimes wise and sometimes catastrophically wrong, and the conditions that determine which one you're in, Surowiecki's four criteria, are exactly the conditions that are hardest to assess from inside the crowd. If you could always tell whether you were in a wisdom-of-crowds situation or a bubble, there would be no bubbles.

What Salganik's experiment really showed is that the same group of people, with the same information and the same preferences, can produce wildly different outcomes depending on whether they can see each other's choices. Remove social visibility and quality wins. Add social visibility and randomness wins. Financial markets, by design, are environments of maximum social visibility. Every price is a signal of what other people are doing. Every Bloomberg terminal, every trading app, every fund performance table is a mechanism for transmitting social information. We have built markets that systematically disable the conditions under which crowd wisdom works, and then we express surprise when the crowd turns out to be wrong.

The herd is global and the platforms are American, but a crowd still runs with a national accent. The next part turns to the British herd, and how a nation of queuers behaves when the running starts.

Next in this series: The British herd. If this was useful, subscribing gets you the rest of the series as it lands.

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Anyone who has tried to get something done in a developing country recognises classic bureaucracy: drudging between offices to get various documents signed and stamped, only to be told you must first go somewhere else to get a different document signed and stamped. Weeks go by as you sit and wait in drab offices whilst your inner voice ponders “to bribe or not to bribe”.

Britain is regularly pilloried as a nation in decline, and the charge sheet writes itself. The heavy industries have gone. We no longer invent new technologies; instead, we just implement those pioneered in San Francisco and Hangzhou. Universities are merging and cutting courses to stay solvent. Potholes go unfilled. I bring good news. We have quietly developed a new home-grown form of world-leading economic activity. I call it bureauctivity (bureauc[racy] [product]ivity): bureaucracy so thorough and well executed that it has become a productive industry in its own right.

bureauctivity /bjʊəˌrɒkˈtɪvɪti/ noun [mass noun]

economic output generated by the performance of regulatory compliance to a professional standard; the conversion of administrative obligation into measurable production: the survey, conducted over four nights by five licensed ecologists with thermal-imaging equipment, was a triumph of bureauctivity.

ORIGIN blend of bureaucracy and productivity.

Consider what happens when someone wants to knock down a barn and build houses on the site. In the amateur bureaucracies of the world, this is where the stamping and shuttling begins, at the end of which nothing exists except frustrated developers and richer officials. Not so in Britain. Here a bat survey will be conducted. This is no mere formality or sinecure. Ecologists with degrees, accredited by their own chartered professional institute and operating under a survey licence from Natural England, will attend in teams as the sun goes down.2 They work to the Bat Conservation Trust's published methodology, now in its fourth edition, which specifies how many surveyors a building of given complexity requires and how many dusk emergence surveys must be spread across the May-to-September season.3 Thermal-imaging kit supplements the human eye: the favoured instrument for the job, the Pulsar Telos XP50 monocular, retails at £2,529.4 Echolocation calls are recorded, the bats identified to species and a full report written, with sonogram appendices running into the double digits.

Elsewhere, a bribe disappears into a pocket and produces nothing. Here, there is genuine industrial activity. High Speed 2 is spending more than £100 million on a much-maligned kilometre-long curved structure in Buckinghamshire so that bats never need meet a train.5 Construction is underway, and whilst HS2 is perennially in doubt, that bat tunnel will certainly be completed. And it will be built to last. No other nation on earth could produce a hundred-million-pound bat tunnel. The national accounts record it as output, because it is.

In Yes Minister Sir Humphrey Appleby explains how the nations of Europe would greet a proposed European regulation: "The Germans will love it, the French will ignore it, and the Italians and the Irish will be too chaotic to enforce it. Only the British will resent it."6

But on this rare occasion Sir Humphrey was wrong. The British don’t resent such regulations; we thrive on them. Whitehall even has an official term for implementing European rules more rigorously than Brussels asked: “gold-plating”, a practice the government solemnly promised to abolish in 2010.7 In truth, post-Brexit, this segment has flourished.

Consider our rivers, or rather, the paperwork beside them. Under nutrient neutrality rules, derived from European habitats law we chose to keep and then applied with fresh vigour, housebuilders in affected catchments must demonstrate, calculation by calculation, that their development will add not one gram of nitrogen or phosphorus, depending on the catchment, to the local watercourse. While, at the 2023 peak, roughly 145,000 homes queued behind this arithmetic, an entire secondary market sprang up: mitigation credits, generated by taking farmland out of production and building wetlands, bought and sold so that the spreadsheet balances.8 Soil surveys, water sampling, nutrient modelling, laboratory invoices: real scientists doing real work for real money. The homes may not exist, but the economic activity certainly does.

The closest approximation the statisticians offer to what I mean by bureauctivity is a category called professional, scientific and technical activities, which is not a perfect fit, but it is where the bat reports, the nutrient budgets and the compliance reviews get filed. In 2024, while the economy as a whole managed growth of about 1 per cent, that sector grew 3.2 per cent, the strongest performance of any services sector.9 A sector not far off a tenth of the economy, growing at three times the national rate.

In the three months to October 2025, that same sector contracted by 1.6 per cent, the largest negative contributor to services output. GDP promptly fell by 0.1 per cent, the first three-monthly decline in nearly two years.10 Industrial production had been shrinking for six consecutive quarters and nobody noticed a thing. The paperwork professions paused for one season and the whole economy went backwards. I am not sure any single statistic better captures modern Britain: we can survive without making cars, but not without writing reports about the implications of making cars.

The OECD has long rated Britain one of the least regulated economies in the developed world on paper.11 That is the exquisite part. Much of this compliance is not strictly required by legislation. Much of the modern British bureauctivity relies on “industry best-practice”, “guidelines”, “standard operating procedures from a Local Authority planning teams”. Health and safety, safeguarding, procurement compliance, planning consultancy: each has its own institute, its own accreditation ladder, its own awards evening and its own conference season at a Midlands hotel. When Parkinson observed in 1955 that "work expands so as to fill the time available for its completion"12 he meant it as satire. The UK made it our principle industrial strategy.

Whatever else you say about these jobs, they are done superbly. Other countries have bureaucracy the way they have potholes, as a symptom of neglect. We have it the way the Swiss have watchmaking: precise, credentialed, admired by connoisseurs and increasingly the thing propping up GDP.

It is a true British success story. Hammond will get his bar in the end, and the survey reports will be immaculate.

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In the spring of 1720, Isaac Newton sold his shares in the South Sea Company for a profit of roughly £7,000. It was a sensible decision. The stock had risen sharply on little more than speculative enthusiasm and a vaguely defined monopoly on trade with South America. Newton, who held the position of Master of the Royal Mint and was by any measure one of the most analytically gifted people alive, had looked at the numbers, assessed the risk, and taken his money off the table.

Then he watched the price keep climbing.

Over the following weeks, Newton's friends, colleagues, and social circle continued to buy. The talk in London coffeehouses was of fortunes being made overnight. People who had never previously traded stocks were mortgaging property to invest. By June, with the share price still rising, Newton bought back in, this time with a much larger position. He invested close to £26,000, a sum that represented the vast majority of his personal wealth.

By December the bubble had burst. South Sea Company shares, which had peaked at over £1,000, fell below £200. Newton's losses came to roughly £20,000, equivalent to several million pounds today. The mathematician Andrew Odlyzko, who reconstructed Newton's trades from surviving records in a 2019 paper for Notes and Records of the Royal Society, calculated a capital loss of around 77 per cent.1 Newton is said to have remarked afterward that he could calculate the motions of heavenly bodies but not the madness of people. (The quote, as Odlyzko notes, cannot be reliably traced to Newton's own writings. It was first attributed to him decades after his death. But its popularity tells us something about how much we want to believe that intelligence protects us from crowd behaviour. It doesn't.)

What happened to Newton is not a historical curiosity. It is a pattern that repeats with remarkable consistency across centuries, currencies, and asset classes. A person makes a rational assessment. The crowd moves in a different direction. The person holds their position for a while, then joins the crowd, often at the worst possible moment. The sequence played out in the South Sea Bubble of 1720, the dot-com mania of 1999, the sub-prime mortgage frenzy of 2006, the cryptocurrency surge of 2021, and the pandemic-era meme stock explosion. The technology changes. The asset class changes. The underlying human behaviour does not.

This post is about why. Why do intelligent, informed, otherwise rational people abandon their own judgement and follow the crowd? The answer involves economics, neuroscience, career incentives, social media algorithms, and a British cultural habit of watching what the neighbours do before making any decision about money. It also involves a harder question, one that most writing about herd behaviour avoids: what if, sometimes, following the crowd is actually the right thing to do?

And it involves Newton, who is often held up as proof that even geniuses are helpless against market manias. That framing is too flattering. Newton didn't fail because he was human. He failed because being intelligent didn't protect him from the specific psychological mechanism, the pull of the herd, that this post will describe. Intelligence is no defence against conformity. If anything, intelligent people are better at constructing post-hoc justifications for following the crowd, which makes the problem worse, not better.

The mechanics of herding

In 1992, three economists published a paper in the Journal of Political Economy that gave herding a formal structure. Sushil Bikhchandani, David Hirshleifer and Ivo Welch described what they called an information cascade: a sequence in which individuals, acting rationally, observe the choices of those ahead of them and conclude that the crowd's collective behaviour reveals information worth following, even when their own private information suggests something different.2

The logic is surprisingly clean. Imagine you are deciding whether to invest in a new company. You have done your own research and you're mildly sceptical. Then you watch ten people ahead of you, each with their own analysis, all invest. You don't know what their individual analyses said. But the collective signal, ten out of ten choosing to invest, is hard to ignore. At some point, a rational person concludes that whatever the crowd has seen probably outweighs what they've seen alone. So they invest too. And the eleventh person, watching, reaches the same conclusion. The cascade has started.

This is what makes information cascades so different from simple peer pressure. The person joining the cascade is not being irrational. Given what they can observe, following the crowd is a reasonable inference. If ten informed people have all reached the same conclusion, the probability that they're all wrong is, on the face of it, low. The difficulty is that those ten people may each have been following the same logic: watching the person before them and suppressing their own doubts. The collective signal looks strong. The information underneath it may be thin.

Bikhchandani, Hirshleifer, and Welch made two observations about information cascades that are worth holding onto. The first is that cascades are self-reinforcing. Each new person who follows the crowd adds to the signal that future observers will use. The crowd gets bigger, the signal gets stronger, and the cost of dissenting increases. The second observation, which receives far less attention, is that cascades are fragile. Because each person in the chain is suppressing their own information, the whole structure can collapse the moment a single credible contrary signal appears. One person with convincing evidence that the crowd is wrong can shatter the cascade entirely. The speed of both formation and collapse is part of what makes financial bubbles so violent in both directions.

A 2024 review confirmed that the core dynamics Bikhchandani, Hirshleifer, and Welch identified have replicated consistently across thirty years of laboratory experiments and real-world market data. The model is one of the most robust in behavioural economics.3

But information cascades describe a process. They don't explain why it feels so uncomfortable to stand apart from a group, even when your own reasoning tells you to. For that, you need a brain scanner.

In 2009, Vasily Klucharev and his colleagues at the Donders Institute in the Netherlands published a study in the journal Neuron that opened a window onto the neural machinery of conformity.4 The study design was simple. Participants rated the attractiveness of a series of faces while lying in an fMRI scanner. After each rating, they were shown the average rating from a group of peers. Sometimes the group agreed with them. Sometimes it didn't.

When the participant's rating diverged from the group's, two things happened inside their brain. Activity spiked in the rostral cingulate zone, a region of the posterior medial frontal cortex that is associated with detecting errors. It's the same area that lights up when you make a mistake on a cognitive task and realise you've got the wrong answer. Simultaneously, activity changed in the ventral striatum, a reward-processing region. The brain was, in effect, treating disagreement with the group as a mistake and agreement as a reward. The amplitude of these signals predicted whether the participant would change their rating to match the group's in a follow-up session thirty minutes later. The stronger the error signal, the more likely they were to conform.

What Klucharev had found was that the brain has, in effect, a built-in conformity enforcer. It doesn't just notice social disagreement. It codes it as an error, the same way it codes a wrong answer on a maths test. And the strength of the coding predicted behaviour. People whose brains generated stronger error signals were more likely to change their minds to match the group. Conformity wasn't a choice. It was a neural reflex, operating below conscious awareness.

This was correlation. To establish causation, Klucharev and colleagues ran a follow-up experiment in 2011, published in the Journal of Neuroscience.5 This time they used transcranial magnetic stimulation to temporarily suppress activity in the posterior medial frontal cortex, the region that had generated the error signal. When they did, conformity dropped. Participants whose error-detection region had been dampened were less likely to shift their ratings toward the group's. The relationship was causal: reduce the brain's error signal for social disagreement, and people become more willing to stick with their own judgement.

The implications for financial markets are uncomfortable. Herding is not just a product of rational inference, as the cascade model suggests. It is also wired into neural architecture. The brain treats going against the group as a mistake, at a level below conscious deliberation. A fund manager who holds a contrarian position isn't just taking a financial risk. Their brain is generating a steady, low-grade error signal, telling them something is wrong. Holding that position requires overriding that signal, continuously, for as long as they remain the outlier. Most people can't do it for very long.

Career herding

There is a joke among fund managers that nobody ever got fired for buying IBM. The equivalent in investment management is that nobody ever got fired for losing money the same way everyone else did.

In 1990, David Scharfstein and Jeremy Stein published a paper in the American Economic Review that formalised this intuition.6 They showed that when investment managers are evaluated against their peers, a powerful asymmetry emerges. If you follow the crowd and the crowd turns out to be wrong, you're one of many. The explanation is easy: "Nobody saw it coming." The reputational damage is shared across the entire industry, and individual careers survive. But if you break from the crowd and you turn out to be wrong, you're alone. You made a bet nobody else made, and you lost. The explanation required is personal. You got it wrong. Your career may not survive.

Scharfstein and Stein's model predicted that professionals with the best information, the ones most likely to know the crowd is wrong, would also face the strongest incentive to follow it. The better your private information, the more conspicuous your deviation from consensus, and the greater the career risk if the deviation doesn't pay off. The result is an industry in which the people best equipped to challenge herd behaviour are the least likely to do so.

Consider how this works in practice. A fund manager at a large UK asset management firm in early 2007 might have had private intelligence suggesting that mortgage-backed securities were mispriced. She might have been right. But if she reduced her fund's exposure while every competitor maintained theirs, and the market continued rising for another six months (which it did), her fund would underperform. Her quarterly review would be uncomfortable. Her clients might withdraw money. Her boss, who was being benchmarked against the same competitors, would ask difficult questions. The rational career move was to stay with the herd, even if her analysis said the herd was heading for a cliff. When the crash came, she would lose money, but so would everyone else, and "everyone got it wrong" is a survivable narrative in a way that "I got it wrong while everyone else made money" is not.

John Maynard Keynes saw this clearly in 1936, though he expressed it differently. In The General Theory of Employment, Interest and Money, he compared the stock market to a newspaper beauty contest in which readers were asked to pick the prettiest faces from a set of photographs.7 The prize went not to whoever picked the objectively prettiest face, but to whoever picked the face that received the most votes. The winning strategy, Keynes argued, was not to assess beauty at all but to assess what other people would find beautiful. And then to go a level deeper: what other people thought other people would find beautiful. The result was that prices reflected not value, but expectations about expectations.

A 2020 study in the International Review of Financial Analysis found that UK mutual fund managers exhibit herding behaviour driven by investor sentiment.8 When clients are optimistic, managers pile into the same trades. When clients turn fearful, they exit together. The study measured herding intensity across different market conditions and found it strongest during periods of high uncertainty, precisely when independent analysis would be most valuable. Fund managers are not stupid. They are responding rationally to career incentives that punish independent thought and reward consensus.

The cost of this dynamic falls disproportionately on the people furthest from the decision. When UK equity fund managers herd into the same positions during a boom, they push prices higher than fundamentals justify. When they herd out during a crash, they push prices lower. Investment Association data shows the pattern with painful clarity: £18.4 billion of inflows to UK equity funds during the 2020-21 rally, followed by £18.2 billion of outflows in 2022.9 Money arriving at the top and leaving at the bottom. The fund managers are following their career incentives. The cost is borne by the pension holders, ISA savers, and retail investors whose money moves in and out at exactly the wrong times.

Keynes's beauty contest, written nearly ninety years ago, remains the most accurate description of how professional investment actually works. The question is not "what is this asset worth?" The question is "what does everyone else think this asset is worth, and will they still think it tomorrow?" When enough people are asking that question instead of the first one, markets stop being mechanisms for price discovery and become mechanisms for the amplification of consensus.

Newton followed the herd on the strength of coffeehouse talk and letters from friends, and it took months to ruin him. The modern herd moves in minutes, on screens, with the whole crowd visible at once. In the next part: GameStop, the UK crypto boom, the Northern Rock queue, and the awkward question of when the crowd is actually right.

Next in this series: The herd goes digital. If this was useful, subscribing gets you the rest of the series as it lands.

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Further reading: Mackay, C. (1841) Extraordinary Popular Delusions and the Madness of Crowds. The original study of financial manias, still readable and still relevant nearly two centuries later.

Fear in the body

The same hormonal machinery exists in every person with a pension, an ISA, or a savings account. The cortisol response is triggered by a letter from your pension provider showing a 25 per cent decline or an email from your bank about your mortgage rate or a conversation with a colleague who mentions they’ve “moved everything to cash”. Whilst the trader has a risk manager, a compliance team and colleagues who have survived crashes before, the retail investor has an echo-chamber on social media and a sense that something terrible is happening to their future.

The FCA's Financial Lives 2024 survey is the most comprehensive picture we have of financial wellbeing in the UK. It surveyed 17,950 people and found that 13.1 million UK adults, 24 per cent of the population, had what the regulator classifies as "low financial resilience."1 These are people who have missed a payment, are struggling to meet their financial commitments, or lack sufficient savings to weather a difficulty. One in ten had no savings at all. A further 21 per cent had less than £1,000. In the twelve months to January 2024, 43 per cent of adults, roughly 22.7 million people, reported anxiety or stress caused by the rising cost of living. A year earlier, at the peak of the energy price shock, that figure had been 54 per cent.

Pair that with the Mental Health Foundation's 2023 polling of 6,000 UK adults, which found that one in three said worries about paying bills had made them anxious in the preceding two weeks.2 Or with research from St James's Place in 2024, which found that 47 per cent of UK adults said financial worries had affected their mental health. Among 18-to-34-year-olds, the figure was 66 per cent.3

These are not small effects in a marginal group. They describe a country in which financial anxiety is a mass experience, touching nearly half the adult population and two thirds of its youngest working adults. Between 2022 and 2024, tens of millions of people experienced a level of financial stress that would be considered a mental health risk factor. The cost-of-living crisis was not a sudden market crash of the kind that makes newspaper front pages. It was a slow squeeze, playing out over utility bills and supermarket receipts. It produced anxiety levels comparable to those seen during the 2008 crash itself.

The clinical literature confirms what the surveys suggest. The Money and Mental Health Policy Institute found that approximately 46 per cent of people in problem debt have a mental health condition.4 Causation runs in both directions, making the relationship harder to untangle but no less devastating in practice. A 2024 population-based cohort study found that people experiencing financial hardship had significantly elevated risks of insomnia, poor sleep quality, and failure to meet sleep duration guidelines.5 Psychosocial factors, worry and rumination in particular, explained about 40 per cent of the link between financial difficulty and insomnia. A separate UK study found that students with high financial stress experienced poor sleep quality that then degraded their dietary choices, their physical health, and their academic performance.

Sleep matters more than it might seem in a piece about money. Working memory, emotional regulation, and decision-making quality all decline with sleep loss. A person who lies awake at 3am worrying about their overdraft is, the following morning, measurably less equipped to make good decisions about that same overdraft. It is a feedback loop running downhill: anxiety disrupts sleep, poor sleep impairs judgment, impaired judgment leads to worse financial choices, and worse financial outcomes generate more anxiety. The cycle is self-reinforcing, and it operates below the level of conscious control.

The cardiovascular evidence is grimmer still. A 2023 study found that economic uncertainty is associated with increased mortality from circulatory diseases, ischaemic heart disease, and cerebrovascular disease.6 Research using the UK Household Longitudinal Study found that increased economic insecurity was linked to adverse levels of HDL cholesterol, triglycerides, and C-reactive protein, all biomarkers for cardiovascular risk. The pathway from financial shock to cardiac event runs through chronic stress, inflammation, and the wear of sustained cortisol elevation on the vascular system.

There is something unsettling about laying all of this out in sequence: the hormonal loop on the trading floor, the cortisol-driven paralysis, the insomnia cascade, the cardiovascular damage. Each mechanism has been studied independently, published in a specialist journal, and discussed within its own academic silo. What no one seems to have done is stand back and look at the full picture. The financial fear response is not a single phenomenon. It is a system, running from the endocrine glands through the prefrontal cortex to the cardiovascular system, activated by the same stimulus: a threat to your financial security. And every part of the system makes every other part worse.

Selling at the bottom

In 1985, Hersh Shefrin and Meir Statman identified a pattern they called the disposition effect.7 Investors hold on to losing investments too long, hoping to avoid the pain of crystallising a loss, and sell winning investments too early, to lock in the pleasure of a gain. Loss aversion and the disposition effect are not the same thing, but they're related: the disposition effect is what happens when loss aversion meets a brokerage account.

In 1998, Terrance Odean tested the theory against real trading records.8 He analysed seven years of data from 10,000 individual accounts at a large US discount brokerage. The disposition effect was large and consistent. The stocks people sold for a gain went on to outperform the stocks they stubbornly held at a loss by 3.4 per cent over the following twelve months. In December, the pattern reversed slightly, as investors sold losers for tax purposes. The rest of the year, it held firm. People were paying a measurable cost for their inability to face up to a loss.

In normal market conditions, the disposition effect is a drag on returns. It's expensive but not catastrophic. People hold their losers too long, sell their winners too soon, and underperform the market by a few percentage points a year. It's the financial equivalent of a persistent low-grade fever: uncomfortable, costly over time, but survivable.

During a full-blown crisis, something different happens. The disposition effect breaks.

Nicholas Barberis, a Yale finance professor, documented the psychological sequence of the 2008 crash in a paper published in 2012.9 In the early months, from summer 2007 through the first half of 2008, loss aversion actually prevented selling. The FTSE 100 was down, pension statements were bad, but people couldn't bring themselves to crystallise a 20 per cent loss. They held. The disposition effect was doing what the disposition effect does: making people cling to losing positions.

Then Lehman Brothers collapsed on 15 September 2008. The losses steepened. Through October and November, markets fell further and faster. By early 2009, portfolios that had been down 20 per cent were down 40 per cent. And somewhere in that decline, a threshold was crossed. The pain of watching the number fall further exceeded the pain of realising the loss. The psychological dam broke. Paralysis gave way to capitulation. Investors sold in volume, at the worst possible prices, because the emotional cost of continuing to hold had become unbearable.

The FTSE 100 hit its crisis low of 3,512 on 3 March 2009. It had peaked at 6,731 in October 2007, a fall of almost 48 per cent.10 To put that in personal terms: a pension pot worth £200,000 in October 2007 (if invested in the FTSE 100) would have been showing roughly £104,000 by March 2009. For someone approaching retirement, looking at that number on a screen, the emotional response is not academic.

An investor who sold at that moment and moved to cash would have missed the recovery that took the index back above 5,000 by the end of 2009 and above 7,000 by 2015. The same £200,000 pot, left untouched, would have recovered its full value within two years and doubled within six. But "left untouched" requires a level of emotional composure that the cortisol research tells us is biologically difficult for most people to maintain during a sustained market downturn.

The Investment Association's fund flow data captures the pattern in aggregate. UK retail investors were net sellers of equity funds through the worst of the crisis, pulling money out at precisely the moment prices were cheapest.11 They then began re-investing months later, after the recovery was already well under way. They sold low and bought high. Every piece of financial guidance in existence tells you to do the opposite. Loss aversion made them do it anyway.

The 2022 cost-of-living crisis produced a related pattern, though the mechanism was different. The Investment Association recorded a record annual net outflow from UK retail funds of £25.7 billion, the first full-year net outflow in its data history.12 Funds saw outflows in ten out of twelve months. This was not panic selling driven by market fear. It was forced selling: people liquidating their ISAs and investment accounts because they needed cash to cover rent, energy bills, and food. The contrast with the previous two years was stark. In 2020 and 2021, UK retail investors had put in near-record inflows of £30.8 billion and £43.6 billion respectively. The reversal was total, and it was driven not by fear of the market but by the material pressure of rising prices on household budgets.

In both cases, the selling happened at the wrong time. In both cases, it fell hardest on people without financial buffers, without access to professional advice, and without the luxury of simply waiting.

There is a bitter irony in the Investment Association data. The same system that encourages people to invest for the long term, through workplace pensions, ISAs, and platform advertising that shows thirty-year performance charts, provides no mechanism to protect them from their own biology when a crisis arrives. The auto-enrolment system gets people into the market. Nothing in the system's design helps them stay there when their cortisol is elevated, their sleep is broken, and every instinct in their body is telling them to run.

The gender question

Chris Dawson's 2023 study in the British Journal of Psychology approached the gender gap in financial risk-taking from an angle that most financial research avoids.13 Instead of asking people hypothetical questions about gambles, he used data from 13,575 respondents to the UK British Household Panel Survey and measured loss aversion through real-world income changes: how much does a £1 drop in household income affect your psychological wellbeing, compared with a £1 gain?

His findings were clear on the surface. Women reported a lower willingness to take financial risks than men. Fifty-three per cent of that gap was explained by higher levels of loss aversion among women. A further 3 per cent was attributable to lower levels of financial optimism. Income losses were more psychologically painful for women than for men. Income gains felt roughly the same to both sexes.

Set alongside the Coates research, a picture forms. The testosterone-driven feedback loop that produces bull market overconfidence is predominantly a male phenomenon. Women's risk preferences are more stable across market conditions. Women may take less risk during booms, missing some upside, but they also tend to avoid the worst excesses of the bust. Coates argued that a more gender-diverse financial system would be a less volatile financial system. Dawson's data suggests he might be right, at least about the loss aversion half of the equation.

But the picture has complications that deserve honesty rather than a neat conclusion.

A 2024 meta-analysis on gender and loss aversion found that the answer depends entirely on how you define and measure it.14 Under one common experimental definition, women are more loss-averse. Under another, there's no gender difference at all. Under two further definitions, women are actually less loss-averse than men. The finding that women are "more risk-averse" may be partly an artefact of how questions are framed, which samples are recruited, and which definition of loss aversion the researchers happen to use. A 2022 replication study found no significant gender difference in financial risk aversion, contradicting earlier work.15

I don't think the research is mature enough to draw clean conclusions from. What it does tell us, usefully, is that loss aversion is not a universal constant. It varies between individuals, between genders, between age groups, and between economic contexts. The 2.5x ratio from Kahneman and Tversky is a population average. Some people experience losses at 1.5x intensity. Some at 4x. The variation matters more than the average, because the average obscures precisely the people who are most at risk during a crisis.

If you are a 28-year-old woman with a workplace pension she rarely checks, loss aversion during a downturn may barely register. If you are a 62-year-old man three years from retirement, checking his SIPP daily on his phone while the market drops, the same biological machinery is running at full intensity. Same brain architecture. Wildly different exposure to the fear response. Any system that treats these two people identically, which is to say, the system we currently have, is designed around an abstraction rather than a reality.

What this means

Financial fear is not a character flaw. It is not a sign of financial illiteracy or emotional weakness. It is a physiological response, operating at every level of the human system: hormonal, with cortisol flooding the bloodstream and testosterone amplifying your worst instincts; cognitive, with loss aversion distorting judgment in direct proportion to the stakes; physical, with disrupted sleep, elevated inflammatory markers and measurable cardiovascular damage; and behavioural, with the reliable pattern of selling at the worst moment and hoarding cash that should be deployed.

This response has been documented in every significant financial event in modern British history. The people who queued outside Northern Rock in September 2007 were not irrational. They were making a calculation under extreme uncertainty, and for those who had deposits above the guaranteed limit, they were right to queue. But millions of other people, across the months and years that followed, made decisions driven by a fear response that their conscious, rational minds could not override. They sold their equities at the bottom. They pulled their pensions into cash. They stopped contributing to investments that would have recovered. The cost, measured in retirement income lost, runs into the billions.

Understanding that the fear response is biological, not moral, is the first step toward managing it. The second step, as Benartzi and Thaler demonstrated three decades ago, is absurdly simple: stop checking your portfolio so often. Every time you open that app and see a red number, you pull the trigger on the loss aversion machinery. The machinery does not care about your thirty-year time horizon. It does not care about average annual returns. It cares about the threat directly in front of it, right now.

And the financial system, far from helping, actively makes this worse. Pension dashboards show daily fluctuations. Investment platforms send push notifications when markets move. Fund factsheets report monthly returns to three decimal places. Workplace pension statements arrive with graphs that make a temporary decline look like the edge of a cliff. The entire architecture is built as if the humans using it are rational agents who process information calmly and act in their long-term interest. They are not. They have amygdalas. They have cortisol receptors. They have the same fight-or-flight wiring as every other mammal on the planet.

The cost of pretending otherwise falls hardest on those with the smallest financial buffers and the least access to the professional advice that might, in the moment of panic, talk them back from the edge. A wealthy investor with a financial adviser will get a phone call during a crash telling them to hold steady. A care worker with an auto-enrolled workplace pension worth £8,000 will get an app notification showing her pot is down 22 per cent, and no one to explain why that number, frightening as it looks, is a temporary feature of a long-term investment. The biology is the same. The support is not and the system is designed as though neither exists.

John Coates left Wall Street to find out why his body overruled his brain. What he found was that the same hormonal machinery runs in every person with a financial stake in an uncertain world. The trader on the Deutsche Bank floor in 1998, hands steady during the boom and shaking during the crash, was not an outlier. He was a preview. His body was doing what all our bodies do when the numbers on the screen start falling. The question he asked in that laboratory in Cambridge, whether we are, in the end, frightened animals making financial decisions, has only one honest answer. We are. The financial system just hasn't caught up with that fact.

The fear this piece describes plays out inside a single body, one nervous system at a time. Crises spread between bodies too, through watching what everyone else does and doing the same, and that is where this series goes next. Even Isaac Newton, the most rational man of his age, could not resist following the crowd into the South Sea Bubble.

Next in this series: Why we follow everyone else off the cliff. If this was useful, subscribing gets you the rest of the series as it lands.

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In 1998, a derivatives trader at Deutsche Bank in New York noticed something odd about his own body.

During a bull run his hands were steady, his thinking quick, his appetite for risk almost physical. He slept well, ate well and felt “invincible”. Then the Russian government defaulted on its debt and the mood on the trading floor changed in a way that no pricing model could have predicted. His fingers hesitated over the keyboard. His sleep broke apart. His stomach clenched each morning before the opening bell. His colleagues, among the most analytically trained people on Wall Street, were making decisions that looked like the behaviour of frightened animals.

The trader's name was John Coates. He had spent thirteen years on Wall Street, working derivatives desks at Goldman Sachs, Merrill Lynch and finally Deutsche Bank, where he ran a trading operation specialising in the tails of probability distributions (extreme outlier events). With a PhD from Cambridge, he was not short of analytical training, yet the question he couldn't shake was disturbing: what if they actually were frightened animals? What if the decisions being made on trading floors during a financial crisis had less to do with spreadsheets and pricing models and more to do with cortisol, testosterone and the ancient mammalian fear response?

In 2004, Coates left Wall Street and returned to Cambridge to find out. He started collecting saliva samples from City of London traders, twice a day, measuring their hormone levels while they worked. He would arrive on the trading floor early and then return in the afternoon. He did this for weeks at a time, in conditions ranging from quiet, profitable markets to the kind of volatility that made grown men swear at screens.

What he discovered over the following decade would reframe how we understand financial risk. His findings should unsettle anyone who believes that markets are driven by rational calculation. They certainly unsettled the traders and the academic economists who reviewed his papers. And they should unsettle you, because the same biological machinery that Coates measured in City traders is running, right now, in the body of every person in Britain who has a pension, a mortgage or a savings account.

Before looking at Coates findings, we need to start with a more fundamental question: why do human beings experience financial losses so much more intensely than financial gains? The answer takes us to a psychology laboratory in Jerusalem, in 1979, and to a pair of researchers whose work would change how we understand decisions about money, risk, and everything that connects them.

The asymmetry

Daniel Kahneman and Amos Tversky spent years asking people to make simple choices between gambles. Would you rather have a guaranteed £500, or a 50/50 chance of winning £1,000? Most people took the sure thing. Would you rather lose £500 for certain, or take a 50/50 chance of losing £1,000? Most people gambled. The expected value is identical in each case, yet the choices people made were not. The pattern was consistent across thousands of subjects and it violated everything that classical economics assumed about how rational agents weigh outcomes.

In 1979, Kahneman and Tversky published Prospect Theory: An Analysis of Decision Under Risk.1 The paper would eventually help win Kahneman the Nobel Prize in Economics. Its central insight was deceptively simple: people don't evaluate outcomes in absolute terms. Instead, they evaluate them relative to a reference point, usually whatever they currently have. The function is not symmetrical: the pain of losing £100 is more intense, psychologically, than the pleasure of gaining £100.

How much more intense? Kahneman and Tversky's original experiments suggested a ratio of about 2 to 2.5 times. A loss feels roughly twice as bad as an equivalent gain feels good. They called this asymmetry loss aversion and it offered a tidy explanation for a cascade of financial behaviour that economists had been labelling "irrational" for decades.

During the financial crisis in 2008, UK pension holders watched their retirement pots drop by 30 or 40 per cent over a period of months. If losses are felt at twice the intensity of gains, a 30 per cent fall doesn't feel like the mirror image of a 30 per cent rise. It feels closer to a 60 per cent hit. Brain imaging studies have since shown that financial losses activate the anterior insula and other neural circuits associated with physical pain and disgust.2 The 2008 pension losses weren't just disappointing. For the people experiencing them, they were physically painful in a way that neuroscientists can now measure on a scan.

In 1995, two economists at the University of Chicago, Shlomo Benartzi and Richard Thaler, built on Kahneman and Tversky's work with an observation that still hasn't properly filtered through to the people who design pension systems. They called it myopic loss aversion.3 Their insight was straightforward: people check their portfolios too often. When you evaluate your pension fund's performance monthly rather than annually, you encounter more periods showing a loss, because stock markets are volatile in the short term even when they trend upward over decades. Each encounter with a negative number triggers the loss aversion response: a small chemical jolt of distress; tightening in the chest; a thought, conscious or not, that maybe you should move to something safer.

Benartzi and Thaler ran simulations to quantify the effect. The probability of seeing a loss on a diversified equity portfolio on any given trading day is roughly 46 per cent. Check monthly, and it drops to about 38 per cent. Check once a year, and it's around 27 per cent. Check every five years and losses become rare. The underlying asset is the same in every case. Only the frequency of emotional exposure changes. Their simulations showed that investors behave as if they're operating with a time horizon of about one year, even when their actual investment horizon is twenty or thirty years. The result is that frequent checkers end up holding far less in equities than infrequent checkers and they earn lower returns over their lifetimes as a consequence.

Rather than a flaw in either the market or the investor, this is a flaw in the interface between the two. Every push notification from a trading app, every monthly statement email from an investment platform, every red number on a portfolio dashboard is a trigger. The information is accurate. The emotional response it provokes is, from an investment perspective, counterproductive. The more accessible investing has become, the worse the problem. In 1995, when Benartzi and Thaler published their paper, checking a portfolio meant calling your broker or waiting for a quarterly statement. In 2025, it means opening an app on the device in your pocket, which most people do dozens of times a day. The frequency of emotional exposure has increased by an order of magnitude. The loss aversion machinery hasn't changed at all.

Now here's where the picture gets more complicated, and more interesting.

In 2024, Lukasz Walasek, Matthew Mullett, and Neil Stewart published a meta-analysis in the Journal of Economic Psychology that re-examined the loss aversion evidence from scratch.4 They collected every study that had fitted prospect theory's loss aversion parameter, known as lambda, to individual choices between risky gambles. The sample turned out to be surprisingly small for such a famous finding: just 17 published studies comprising 19 data sets. And the mean loss aversion coefficient they calculated was 1.31, with a 95 per cent confidence interval from 1.10 to 1.53. That is a long way from Kahneman and Tversky's original estimate of 2.0 to 2.5. Much of the available data, the authors noted, was of poor quality, making precise estimates difficult.

Separately, a 2023 study in Judgment and Decision Making found that loss aversion doesn't reliably appear for small stakes at all. It emerges only weakly and only for losses above roughly $40.5

I want to be careful with this, because the headline version of the meta-analysis, "loss aversion is weaker than we thought" misses the point that matters for this series. What the evidence actually shows is that loss aversion scales with stakes. Small, everyday financial decisions may not trigger it. Lose £5 on a bet and you'll barely notice the asymmetry. But the kinds of losses people experience during a genuine financial crisis, where pension pots drop by tens of thousands of pounds over weeks, where house prices fall by 15 per cent in a year, where a lifetime of savings shrinks visibly on a screen, these are precisely the conditions under which loss aversion hits hardest. A 2020 global replication study, spanning 19 countries and 13 languages, confirmed that prospect theory's core predictions hold, particularly in the domain of large losses under uncertainty.6

So the picture that emerges is more nuanced than the textbook version, and more useful. Loss aversion is not a fixed constant baked into every human brain at the same intensity. It is a response that scales with the magnitude of what you stand to lose. Financial crises, by definition, are the moments when the stakes are highest. Which means they are the moments when loss aversion dominates most completely.

The hour between dog and wolf

The title comes from the French phrase l'heure entre chien et loup, the twilight hour when you can no longer tell the dog from the wolf. John Coates uses it as a metaphor for the moment when a rising market tips into mania, or a falling market tips into panic: the moment when your body crosses a hormonal threshold, and confidence becomes recklessness, or caution becomes paralysis. It is also the title of his 2012 book which laid out a single uncomfortable argument that the financial industry has mostly preferred to ignore.7

To understand why the argument mattered, we need to understand what a trading floor feels like during a crash. Coates described it in interviews with the vividness of someone who had lived through it repeatedly. During the good times, a floor has a particular energy: loud, competitive, almost athletic. Traders shout, joke, take on positions they wouldn't normally consider. They eat more. They sleep well. They feel sharp. During a downturn, the same room becomes a different place. People go quiet. They stare at screens without acting. Lunch goes uneaten. Conversations turn short and defensive. The confidence that seemed like competence six months earlier has evaporated, and what replaces it is something that looks and feels a lot like clinical anxiety. Coates wanted to know whether these observations were just anecdote or whether there was a measurable biological process driving them.

In 2008, Coates and Joe Herbert published their answer in the Proceedings of the National Academy of Sciences.8 They had measured testosterone and cortisol levels in the saliva of 17 male traders on a City of London trading floor, sampling twice daily over eight consecutive business days.

First: a trader's morning testosterone level predicted his day's profitability. On high-testosterone mornings, traders made significantly more money. Fourteen out of seventeen subjects showed higher profits and losses on days when their testosterone was elevated. Testosterone increases confidence and appetite for risk. In a rising market, this creates something insidious: success breeds testosterone, which breeds more risk-taking, which breeds more success, which breeds more testosterone. A biochemical positive feedback loop, invisible to risk management systems, inflating the bubble from inside the bodies of the people running it.

Second: cortisol rose with both the volatility of the market and the variance of the trader's own results. During calm, profitable periods, cortisol stayed low. When volatility spiked, as it did violently in the autumn of 2008, cortisol surged. A brief cortisol spike is useful. It sharpens focus and quickens reactions. But sustained elevated cortisol, the kind that builds over days and weeks of market turmoil, does something different.

Narayanan Kandasamy, working with Coates and others, published the follow-up in PNAS in 2014.9 Using a double-blind, placebo-controlled, cross-over protocol, they raised cortisol levels in volunteers over eight days, to levels comparable to those observed in traders during a market crash. They then tested financial risk preferences. The result: participants' certainty equivalent (the guaranteed sum they'd accept in place of a risky gamble) fell from £25 to £14. A 44 per cent drop. They became profoundly risk-averse. They wouldn't take bets that were obviously favourable. Their bodies had overridden their arithmetic.

This is the mechanism behind the question that puzzles every market commentator during a downturn. Prices are cheap and yet money sits in cash, in gilts, in anything that feels safe. The Kandasamy study offers a biological explanation. The investors who should be buying at the bottom are running cortisol levels that make them physically incapable of bearing risk. Their rational minds may know the market is cheap. Their endocrine systems are vetoing the trade.

Coates also observed that these hormonal feedback loops are not distributed equally. The testosterone-driven cycle of escalating confidence was significantly stronger in younger men. Women's risk preferences were more stable across market conditions. The cortisol-driven risk aversion hit both sexes, but the bull-market overconfidence cycle was predominantly male. Coates argued, provocatively but with data to support him, that a more gender-balanced trading floor might produce a more stable market: fewer euphoric highs during the boom, fewer paralytic lows during the crash.

His conclusion was uncomfortable for an industry that had spent decades building risk management infrastructure on the assumption that the people operating it were rational agents making calculated bets. The financial system, Coates argued, is not just psychologically unstable. It is endocrinologically unstable. The models assume the trader is a computer. The trader is a human. And the human’s hormone levels are moving in sync with the very market the models are trying to predict.

Coates found this chemistry in professional traders, people who are paid to live with risk. The machinery he describes is standard issue, and that is where this series goes next: what cortisol does to ordinary savers, and why so many of us sell at the exact bottom.

Next in this series: Selling at the bottom. If this was useful, subscribing gets you the rest of the series as it lands.

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Every financial crisis in modern British history has triggered the same set of psychological responses in how people handle their money. War, financial crash, pandemic, inflation: each crisis arrives wearing different clothes, but the underlying mental machinery does not change. The brain that made a wartime housewife hoard tinned food is the same brain that made a millennial panic-sell their ISA in March 2020. The same patterns repeat because they are features of human cognition, not products of any particular economic era:

1. Fear response

2. Herding instinct

3. Narrative contagion

4. Cognitive narrowing

Carmen Reinhart and Kenneth Rogoff studied financial crises across 66 countries over eight centuries. Their central finding, published as This Time Is Different in 2009, is that every generation believes its crisis is unprecedented.1 Policymakers and investors convince themselves that the old rules no longer apply, that new financial instruments or regulatory frameworks have made the system safe, that the lessons of the past are irrelevant to the present. They are always wrong. The crises recur because the human psychology driving them has not changed.

What makes this a British series, rather than a behavioural economics textbook, is that the UK occupies a peculiar position. It has experienced every major category of financial crisis in the past century: world wars, depression, devaluation, stagflation, a housing crash, a banking collapse, a pandemic, a cost-of-living crisis and now the impact of the war in Iran. It has also produced some of the most influential research on how people actually behave with money, as opposed to how economic models assume they behave. For example, the UK Behavioural Insights Team, created in 2010 was the world's first government institution dedicated to applying behavioural science to policy. The UK has pioneered the pension auto-enrolment system that nudged over ten million people into retirement saving by changing a single default setting.

Britain is also a country with a particular and sometimes peculiar relationship to money. The class system, the importance on property ownership, the culture of financial silence, the specific shape of British shame about debt and poverty (particularly with older generations): all of these filter the universal psychological responses through a distinctly national lens. Any account of financial crisis psychology that ignores the cultural context is only telling half the story.

Scattered insights existed for decades before the discipline of behavioural economics coalesced. Charles Mackay wrote Extraordinary Popular Delusions and the Madness of Crowds back in 1841. Daniel Kahneman and Amos Tversky published Prospect Theory in 1979. However, behavioural economics did not become mainstream until after 2008. The financial crisis made it impossible to ignore the gap between how economic models assumed people would behave and how they actually did, the crisis made the old models look foolish. Richard Thaler won the Nobel Prize in Economics in 2017 for his work on how cognitive biases affect economic decisions. The discipline went from academic curiosity to policy tool in less than a decade.

There is a reason the discipline accelerated in Britain specifically. The UK financial sector is enormous relative to the size of the economy. The City of London processes trillions of pounds in transactions daily. British household wealth is more concentrated in housing than in almost any other developed country, which means that property market fluctuations hit personal psychology with unusual force. And the UK welfare state, while more generous than America's, is considerably less protective than those in Scandinavia or continental Europe. British citizens are more exposed to financial shocks than their French or German counterparts.

The psychological toolkit

This series maps eight psychological forces that shape how we handle money during crises. Some are individual: the fear response in your body, the bandwidth tax that poverty imposes on your thinking, the money scripts your parents passed to you by osmosis. Others are collective: the herd instinct that makes us follow everyone else off the cliff, the narratives that spread through populations like viruses, the fairness perception that determines whether we accept sacrifice or revolt against it. And some are distinctly British: the silence around money, the identity politics of property ownership.

Over the coming weeks, this series will work through each force in turn. Fear and the body, starting with loss aversion and the hormonal cascade that financial dread triggers in your physiology. Herding, from career herding in the City to the digital herd on social media. The deal: fairness, trust, and why people accept some sacrifices but not others, from wartime rationing to the austerity response. Financial narratives, how they form, how they spread, and why the story you tell yourself about a crisis matters more than the data. The bandwidth tax: how scarcity itself degrades your cognitive function, making financial decisions worse at precisely the moment they matter most. The British silence around money, the class-inflected shame that prevents people from talking about their finances even when talking would help. Property and identity, why the British treat their houses as extensions of themselves and what this does to financial decision-making. And the intergenerational transmission of financial trauma, from Depression babies to the children of austerity.

The cumulative argument is that financial crises are not primarily economic events that happen to have psychological side effects; instead they are psychological events with economic triggers. The trigger might be a fall in asset prices, a bank failure, a spike in inflation or a pandemic…but the crisis itself (the part that actually damages people's lives, their sleep, their relationships, their health, their capacity to make good decisions) is psychological. And because it is psychological, it follows psychological rules that we can understand and (hopefully!) counteract.

Why this series now

There is much talk about the whether the AI Tech Bubble is about to burst - reproducing (but perhaps on a bigger scale) the 2000 Dot Com Crash. If this were the case, the UK would already be battle-weary from the unusually compressed sequence of financial crises we’ve weathered in the past two decades. The 2008 banking collapse, a decade of austerity, Brexit uncertainty, the pandemic, the 2022-23 cost-of-living crisis triggered by the Ukraine War and now the 2026 oil price fluctuations due to the Iran War. Each of these following in quick succession has not had the recovery gap that previous generations could rely on. The FCA's 2022 Financial Lives Survey found that 12.9 million UK adults, one in four, had low financial resilience. That figure was up from 10.7 million just two years earlier. In the north-east of England, 31 per cent of adults were classified as having low financial resilience. For Black adults, the figure was twice the national average.

These are not just economic statistics. They describe a population whose financial psychology has been shaped by crisis after crisis, each one layering new anxieties on top of old ones. The Bank of England has described consumers as "scarred" by the sequence of shocks. The generational wealth data tells a similar story from a different angle: research from the Institute for Fiscal Studies shows that the difference in typical wealth between someone in their early 60s and someone in their early 30s has more than doubled in real terms since the mid-2000s. Only 36 per cent of people born in the 1980s were homeowners by age 30, compared with over 60 per cent for those born in the 1950s and 1960s. Mike Brewer's 2025 Gresham College analysis puts the wealth gap between the top end and middle Britain at £1.27 million - 17 per cent more than in 2006.

The compressed nature of these crises matters psychologically. Previous generations had recovery periods between shocks. For example the post-war generation had two decades of growing prosperity before the 1970s stagflation hit. The current generation has had no such breathing room. The Bank of England's observation about "scarred" consumers is more than metaphor. Each crisis leaves psychological damage: lower trust, higher anxiety, changed spending habits…and that damage has not healed from one crisis before the next one arrives.

Ulrike Malmendier's research at UC Berkeley puts hard numbers on this effect. People who live through a major economic downturn remain more risk-averse in their financial decisions for decades afterwards. The effect is strongest for those who experienced the crisis during their formative years, roughly ages 18 to 25. A generation that came of age during the 2008 crash, then entered the workforce during austerity, then lived through a pandemic, carries a psychological profile that no previous British generation has matched. They are not just financially poorer than their parents at the same age. They are psychologically different in their relationship with money, more cautious, less trusting of institutions, more anxious about the future.

There is a large and growing portion of the British population for whom the financial system feels less like a structure that supports them and more like a set of forces that happen to them. Understanding the psychology of that experience is not an academic exercise. It is, for millions of people, a daily reality.

What this series is not

My intention in this Crisis Money series is not to provide a personal finance guide on where to invest or how to budget. Although I’m intending to draw on history extensively, I’m not seeking to provide a complete history of British financial crises. The research in behavioural economics has sometimes been used to justify paternalism, but I won’t be an argument that people are irrational and need to be protected from themselves.

My aim is to provide something closer to “a field guide”. If we understand the psychological forces that act on us during a financial crisis, then we are better equipped to recognise them when they arrive. We cannot eliminate loss aversion or the herding instinct, but we can learn to notice when they are operating. Optimistically, that awareness, by itself, changes the decisions we make. Perhaps not always and not perfectly, but hopefully in ways that the research can quantify.

In this series we will be discussing some arguments about system design. If the people using the financial system are predictably affected by fear, herding, cognitive overload, shame and fairness perception, then surely the system itself should be designed with those realities in mind, rather than assuming they do not exist. The auto-enrolment pension success showed what is possible when system designers take human psychology seriously. The Northern Rock deposit insurance failure showed what happens when they do not. The gap between those two examples is where most of British financial policy still sits.

And the speed at which that gap matters is accelerating. In 2007, Northern Rock's run played out over about a week. In March 2023, Silicon Valley Bank in the United States lost $42 billion in deposits in a single day. Cookson and colleagues at the FDIC called it the first Twitter-fuelled bank run.2 The herding instinct, the narrative contagion, the fear response: they all still work the same way. They just work faster now. It will not be the orderly queue outside Northern Rock in September 2007. The next version of that queue will not be visible. It will happen in seconds, across millions of screens.

Next time: what financial dread actually does to your body. A derivatives trader noticed his own hands shaking during the 1998 Russian default, left Wall Street, and spent the next decade measuring what fear does to the people who move markets. His findings apply to anyone who has ever stared at a falling balance.

Next in this series: Fear and the body. If this was useful, subscribing gets you the rest of the series as it lands.

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On the morning of Friday 14 September 2007, a queue formed outside the Northern Rock branch in Kingston upon Thames. It was the first run on a British bank in a hundred and fifty years. By the time BBC camera crews arrived, the line stretched along the pavement and around the corner. Similar scenes were playing out at Northern Rock branches across the north-east of England and beyond: in Golders Green, in central Newcastle, in small towns where the building society had been a fixture of the high street for decades.

The people in those queues were mostly older. They had saved carefully, in many cases for decades. They were not speculators or traders, instead they were retired teachers, former factory workers and couples who had put their life savings into an institution they trusted because of its building society heritage and physical branch network. Some brought folding chairs. Some brought flasks of tea.

That sentence contains, in miniature, almost everything this series is about. The herd instinct. The role of media narratives. The contagion of financial fear. The gap between rational calculation and emotional response.

The images from that Friday went around the world. They looked like something from the 1930s. But they were misleading in an important way. Hyun Song Shin's analysis in the Journal of Economic Perspectives showed that the branch queues were partly an artefact of Northern Rock's business model.1 The bank had only 72 branches, most with just two counter positions. The physical queue was dramatic precisely because the infrastructure was small. The majority of Northern Rock withdrawals were actually made online and by telephone. Approximately one billion pounds left the bank on Friday and Saturday alone, and most of it vanished electronically, without anyone standing in line.

The depositors were not irrational. The UK's deposit insurance scheme at the time guaranteed 100 per cent of the first £2,000 and only 90 per cent of the next £33,000. Someone with £35,000 in Northern Rock stood to lose £3,300 if the bank failed. The Bank of England's Governor later confirmed that the depositors' behaviour was entirely rational given the legal framework. The queue was not a failure of public understanding. It was a perfectly reasonable response to a badly designed insurance system.

And the Northern Rock run was only the beginning. Over the next fifteen years, the British public would be subjected to one financial shock after another, each one activating the same psychological machinery in slightly different configurations:

1. The 2008 crash.

2. Austerity.

3. Brexit.

4. The pandemic.

5. The cost-of-living crisis.

6. Closure Strait of Hormuz.

Each time, for individuals it created the same fear responses, the same herding patterns, the same narrative battles and the same cognitive overload. Whilst each crisis was happening contemporary discussion focussed on the practical ramifications, and predicting the next progression in the crisis. The economics and politics were debated and analysed endlessly, but the psychology of what was happening was largely ignored.

For personal, selfish, reasons I began reading research on how we react in the UK to financial crisis. As more of the financial and mainstream press are discussing the potential for the AI Tech Bubble to burst on a bigger scale even than the 2000 Dot Com Crash, I’ve been thinking “how would I react to that”. I decided to better structure my own thoughts by writing this Crisis Money series.

Crisis Money is about what happens inside (particularly British) people's heads during financial crisis. Specifically, not the macroeconomics or policy debates, but the cognitive and emotional experience of watching your financial security come under threat, and the predictable ways our brains responds. I use the word predictable deliberately. The responses feel chaotic from the inside, intensely personal, as if your particular anxiety about your particular mortgage is yours alone. But the research, drawn from neuroscience, psychology, behavioural economics, and decades of crisis data, tells a different story. Our anxiety follows patterns. Our decisions during a crisis are shaped by forces that have been documented, measured, and in some cases exploited. Understanding those forces does not make the anxiety disappear, but it can change our relationship with it.

Next in this series: The through-line. If this was useful, subscribing gets you the rest of the series as it lands.

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A Money Outsider investigation, based on Freedom of Information responses from 102 UK universities, reveals a significant and widening difference in hardship fund approval rates for UK versus international students.

The headline numbers

In 2020/21, international students who applied for hardship support were approved 94.9% of the time, a higher rate than UK students (86.3%). By 2024/25, those positions had reversed: UK students were approved at 80.6%, while international students had fallen to 70.0%.

The crossover happened between 2021/22 and 2022/23. Before that point, international students were more likely to be approved. After it, they were less likely, and the gap has continued to widen.

There are possible explanations. The COVID year saw specific emergency funding streams, some of which may have been particularly accessible to international students. As those wound down, universities may have reverted to hardship criteria that favour home students, whether by design or effect. Some government-funded hardship pots are explicitly restricted to UK students.

Fee income versus hardship spending

International students pay significantly higher tuition fees than home students, and at many universities they contribute the majority of total fee revenue. As those fees have to be funded without the aid of UK Government student loans, the assumption is that most international students have more substantial personal or family wealth; therefore, a lower need for hardship funds.

We compared each university’s international share of tuition fee income with its international share of hardship fund spending for 2023/24.

At the vast majority of universities, the international share of hardship spending is lower than the international share of fee income. This does not necessarily indicate unfairness: fee income and hardship eligibility are separate things, and many hardship funds are drawn from sources ring-fenced for home students. But the scale of the gap at some institutions is notable.

Six universities (Exeter, Royal Holloway, Bangor, Royal Academy of Music, Cranfield, and Keele) spend proportionally as much or more on international hardship as international students contribute in fees.

Hardship spending per student

Another way to look at the data is to calculate how much each university spends on hardship per UK student versus per international student. The variation is large.

At De Montfort, the university spends £41 on hardship per UK student and 17 pence per international student. At the other end, Exeter spends roughly twice as much per international student as per UK student. The range is enormous.

Some universities awarded zero international grants

At a small number of universities, every single international hardship application was refused, presumably on the grounds of policy. Leeds Beckett rejected all 67 international applications in 2022/23, and all 53 in 2023/24. The Royal Veterinary College has awarded zero international grants in three consecutive years. These may reflect eligibility rules rather than discretionary decisions, but the pattern is striking.

Context and caveats

This data does not tell us why these gaps exist, and there are several legitimate explanations.

Many university hardship funds are drawn from sources that are restricted to home students by regulation, not by university choice. The Office for Students’ hardship funding, for example, is ring-fenced for UK students. Several universities told us this explicitly in their FOI responses.

International students also have different financial profiles. They must demonstrate funds before receiving a visa, which may make them less likely to meet hardship criteria even when they are struggling. Conversely, international students do not have access to the UK welfare system, maintenance loans, or many of the other safety nets available to home students, which could mean their need is underreported rather than lower.

It is also worth noting that fee income and hardship spending are fundamentally different things. Tuition fees go into the university’s general revenue. Hardship funds are typically a separate, much smaller pot. There is no established principle that hardship spending should be proportional to fee contribution.

What the data does show clearly is that the gap is large, it is widening, and it varies enormously by institution. Whether that reflects appropriate policy or a structural problem is a question the sector has not yet publicly addressed.

The full dataset behind this investigation is available here, including charts and raw data tables.

This dataset is compiled from responses to Freedom of Information requests made to UK Universities. Money Outsider have not independently verified the accuracy or completeness of the disclosed information. Money Outsider have attempted to reprocess data from different formats to allow comparison. No warranty is given as to fitness for purpose. Use of this dataset is at your own risk. Attribution to MoneyOutsider.com is appreciated.

Where applicable, re-use of information is intended to be consistent with the Open Government Licence.

Money Outsider sent Freedom of Information requests to every university in the UK. The 101 that responded with comparable data gave us a detailed picture of how hardship funding has changed since the pandemic.

The five-year arc

In the COVID year of 2020/21, fuelled by emergency government funding, universities paid out £79 million. By 2024/25, that had dropped to £46 million, a fall of 42%.

But it is the 2022/23 cost-of-living crisis year that deserves the most attention. That year saw the highest number of applications in the entire dataset: 116,373. More students asked for help than at any point during the pandemic. Yet total spending (£66.7 million) was 16% below the COVID peak. The money was being spread thinner: more students receiving smaller amounts.

The shrinking grant

The average hardship grant has fallen consistently. In 2020/21 a typical grant was worth £958. By 2023/24 it had dropped to £702, a 27% decline in nominal terms. The 2024/25 figures show a partial recovery in average grant size to £785, but total spending continues to fall because fewer applications are being received. Adjusted for inflation over the same period, the real-terms cut would be significantly larger.

Number of hardship applications is falling

In 2024/25 3.4% of students applied to their university’s hardship fund. The drop in hardship applications from their 2020/21 peak of 5.4% is one of the few pieces of encouraging news in an otherwise bleak landscape for student finances in the UK.

The broader student finance picture

The maintenance loan system, for all its faults, does appear to be doing the basic job of keeping students afloat while they study. The fact that hardship applications have come down from their cost-of-living crisis peak suggests that the worst of the acute funding emergency may have passed, at least for home students who can access the full loan package.

But the real sting comes after graduation. The student loan repayment system has come under intense criticism in recent months, and the numbers are stark. The total outstanding student loan book now stands at £267 billion. In the last tax year, £15.2 billion in interest was added to loans, but only £5 billion was repaid. The average graduate who finished in 2024 left with roughly £53,000 of debt.

The full dataset behind this investigation is available here, including charts and raw data tables.

This dataset is compiled from responses to Freedom of Information requests made to UK Universities. Money Outsider have not independently verified the accuracy or completeness of the disclosed information. Money Outsider have attempted to reprocess data from different formats to allow comparison. No warranty is given as to fitness for purpose. Use of this dataset is at your own risk. Attribution to MoneyOutsider.com is appreciated.

Where applicable, re-use of information is intended to be consistent with the Open Government Licence.