It may have been a prescient idea, but it didn’t spread very far. ‘That note didn’t really go anywhere unfortunately,’ he said, ‘until the big one came.’ Why didn’t the idea take off? ‘It was hard to spot any examples of such systemic risk at the time. It appeared to be a very flat ocean at that point.’ That would change in autumn 2008. After Lehman Brothers collapsed, people across the banking industry started thinking in terms of epidemics. According to Haldane, it was the only way to explain what had happened. ‘You couldn’t tell a story about why Lehman had brought the financial system down without telling a contagion story.’
If you were to make a list of network features that could amplify contagion, you’d find that the pre-2008 banking system had most of them. Let’s start with the distribution of links between banks. Rather than connections being scattered evenly, a handful of firms dominated the network, creating massive potential for superspreading. In 2006, researchers working with the Federal Reserve Bank of New York picked apart the structure of the US Fedwire payment network. When they looked at the $1.3 trillion of transfers that happened between thousands of US banks on a typical day, they found that 75 per cent of the payments involved just 66 institutions.[83]
Illustration of assortative and disassortative networks
Adapted from Hao et al., 2011
The variability in links wasn’t the only problem. It was also how these big banks fitted into the rest of the network. In 1989, epidemiologist Sunetra Gupta led a study showing that the dynamics of infections could depend on whether a network is what mathematicians call ‘assortative’ or ‘disassortative’. In an assortative network, highly connected individuals are linked mostly to other highly connected people. This results in an outbreak that spreads quickly through these clusters of high-risk individuals, but struggles to reach the other, less connected, parts of the network. In contrast, a disassortative network is when high-risk people are mostly linked to low risk ones. This makes the infection spread slower at first, but leads to a larger overall epidemic.[84]
The banking network, of course, turned out to be disassortative. A major bank like Lehman Brothers could therefore spread contagion widely; when Lehman failed, it had trading relationships with over one million counter-parties.[85] ‘It was entangled in this mesh of exposures – derivatives and cash – and no one had the faintest idea quite who owed what to whom,’ Haldane said. It didn’t help that there were numerous, often hidden, loops in the wider network, creating multiple routes of transmission from Lehman to other companies and markets. What’s more, these routes could be very short. The international financial network had become a smaller world during the 1990s and 2000s. By 2008, each country was only a step or two away from another nation’s crisis.[86]
In February 2009, investor Warren Buffett used his annual letter to shareholders to warn about the ‘frightening web of mutual dependence’ between large banks.[87] ‘Participants seeking to dodge troubles face the same problem as someone seeking to avoid venereal disease,’ he wrote. ‘It’s not just whom you sleep with, but also whom they are sleeping with.’ As well as putting supposedly careful institutions at risk, Buffett suggested that the network structure could also incentivise bad behaviour. If the government needed to step in and help during a crisis, the first companies on the list would be those that were capable of infecting many others. ‘Sleeping around, to continue our metaphor, can actually be useful for large derivatives dealers because it assures them government aid if trouble hits.’
Given the apparent vulnerability of the financial network, central banks and regulators needed to understand the 2008 crisis. What else had been driving transmission? The Bank of England had already been working on models of financial contagion pre-crisis, but 2008 brought a new, real-life urgency to the work. ‘We started using them in practice when the crisis broke,’ Haldane said. ‘Not just for making sense of what was going on, but more importantly for what we might do to stop it happening again.’
When one bank lends money to another, it creates a tangible link between the two: if the borrower goes under, the lender loses their money. In theory, we could trace this network to understand the outbreak risk, just as we can for STIs. But there’s more to it than that. Nim Arinaminpathy has pointed out that networks of loans were just one of several problems in 2008. ‘It’s almost like hiv,’ he said. ‘You can have transmission through sexual contacts, as well as needle exchanges or blood transfusions. There are multiple routes of transmission.’ In finance, contagion can also come from several different sources. ‘It isn’t just lending relationships, it’s also about shared assets and other exposures.’
A long-standing idea in finance is that banks can use diversification to reduce their overall risk. By holding a range of investments, individual risks will balance each other out, improving the bank’s stability. In the lead up to 2008, most banks had adopted this approach to investment. They’d also chosen to do it in the same way, chasing the same types of assets and investment ideas. Although each individual bank had diversified their investments, there was little diversity in the way they had collectively done it.
Why the similarity in behaviour? During the Great Depression that followed the 1929 Wall Street crash, economist John Maynard Keynes observed that there is a strong incentive to follow the crowd. ‘A sound banker, alas, is not one who foresees danger and avoids it,’ he once wrote, ‘but one who, when he is ruined, is ruined in a conventional way along with his fellows, so that no one can really blame him.’[88] The incentive works the other way too. Pre-2008, many companies started investing in trendy financial products like CDOs, which were far outside their area of expertise. Janet Tavakoli has pointed out that banks were happy to indulge them, inflating the bubble further. ‘As
