An early Christmas present for Britain's biggest banks: £34bn from taxpayers

We’re still giving big banks special privileges and they’re still too big to fail, writes Lydia Prieg.

British banks are still too big to fail. Not only does that have terrifying implications for UK taxpayers in the event of another financial crisis, it also has a distortionary effect on the economy. Why? Because being so big that the government can’t afford for you to go bust has financial benefits, even for banks that never received a bailout.

For instance, once the government implicitly guarantees the debt of banks, the cost of borrowing goes down, as creditors are taking on less risk that they won't get their loan repaid. This reduction can be measured, and its value is the too-big-to-fail (TBTF) subsidy.

Today the new economics foundation has calculated the benefits of the subsidy for 2011 and found they totalled £34bn for the big four banks combined. Barclays, Lloyds, RBS, and HSBC enjoyed subsidies of £10bn, £9bn, £11bn and £5bn respectively. Their competitors didn't get this advantage, and neither do firms operating outside the banking industry.

There are a number of reasons why we should be concerned about this subsidy:

  • It’s unfair: banks do not pass on this benefit to their customers, it simply inflates their profits.
  • It’s anticompetitive: new and smaller banks do not benefit from the subsidy, and so find it extremely difficult to compete with the big four.
  • It encourages banks to take on more risk: they get to pocket any upside from risky trades, but know that taxpayers will be there to pick up the tab if everything goes wrong.
  • It creates a vicious circle: subsidies incentivise banks to get even bigger, concentrating power within the banking sector and creating even larger TBTF institutions that enjoy even higher subsidies and further weaken competition.

But the key point of the subsidy is that the markets are reflecting what politicians frequently deny: the fact that taxpayers may once again be called upon to bail out the banks – exactly what we were promised wouldn’t happen.

The government’s primary prescription for tackling the TBTF problem is to ring-fence retail banking away from investment banking activities. But ring-fencing will only reduce, not eliminate, the TBTF subsidy.

Let’s not forget that Lehman Brothers was an investment bank that had no retail banking component; yet its collapse sent shockwaves around the globe. In the UK we have individual banks with assets greater than UK GDP. Given this, even outright separation between retail and investment banking – which is not what we are getting under current proposals – would still leave lingering TBTF problems.

The Parliamentary Commission on Banking Standards is releasing its recommendations to the government on Friday and has been looking at the ring-fencing proposals in depth. Let us hope that the Commission acknowledges the short-comings of the current plans, and pushes the government to at least examine more radical proposals, such as capping the size of banks.

2012 has made it clear that for all the hustle and bustle on banking reform, fundamental flaws in the system remain completely unaddressed. The Financial Services Act and the Banking Reform Bill fall far short of producing the safe and useful banking system that British businesses, customers and taxpayers deserve.

HSBC, one of the TBTF banks. Photograph: Getty Images

Lydia Prieg is a researcher at the new economics foundation.

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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism