Now, if you look back on your own life - and I don"t think I"m going out on a limb here - there have probably been times when, if you"d known how things were going to turn out, you might have made a different decision. There may have been a purchase, for example, that was less than optimal, because the product in question fell apart a day after the warranty expired. Or perhaps a better option was available, which you would have gone for if only you"d known about it. Maybe it was on sale at another store, and you missed the advertis.e.m.e.nt. Or maybe you just screwed up and picked up the wrong thing by mistake.

If an economist were to show up at the door and ask you to produce a consumption plan for the rest of your life, you might also have a bit of a problem. Especially if she magnified her request by producing an infinite list of contingencies: what will happen if you get that job you applied for, or if you"re injured, or you have a new baby, or war breaks out, or you win the lottery, etc.?

In fact, making a list of every future contingency is impossible, the same way that making a list of irrational numbers is impossible. In 1968, the American economist Roy Radner managed to weaken some of these conditions, but he still concluded that for the model to work, every partic.i.p.ant in the economy needed to be endowed with infinite computational capacity.8 The Arrow-Debreu model didn"t represent an economy of human beings - it was an economy of G.o.ds.

Truth and beauty.

While the Arrow-Debreu model obviously made some unreasonable a.s.sumptions - economist Mark Blaug described it as "clearly and even scandalously unrepresentative of any recognizable economic system" - it wasn"t easy to modify any of these too much without the whole thing falling apart.9 Economists could adapt or extend it slightly to take into account factors such as asymmetric information (for example, buyers knowing less than sellers about the goods), or imperfect compet.i.tion, or real currencies with fluctuating exchange rates. But these added complications, and it was easier and more elegant to treat the model as a perfect market economy to which real economies aspire. As such, the model soon became the core reference for neocla.s.sical economic theory.

So how much does this matter? An interesting defence of neocla.s.sical economics was supplied by Milton Friedman, who said that the a.s.sumptions of a theory aren"t important, so long as it makes accurate predictions.10 Friedman was probably the most influential economist of the latter half of the 20th century. His interest in mathematics was inspired by a high-school geometry teacher who connected John Keats" poem "Ode on a Grecian Urn" - "Beauty is truth, truth beauty" - with the Pythagorean theorem.11 He went on to become a leader of the Chicago School of Economics, based at the University of Chicago, which was famous for its free-market ideology, opposition to regulation - Friedman even opposed regulation of drugs - and general railing against taxes and big government. As Friedman said in a 1975 interview, "thank G.o.d for government waste. If government is doing bad things, it"s only the waste that prevents the harm from being greater."12 (Somewhat ironic, considering that Friedman and neocla.s.sical economists in general were supported in large part by government grants.) Friedman"s main contribution to economic thought - and his most famous prediction - relates to his work on monetarism. The basic idea of monetarism is that markets are inherently stable, and the government"s role in controlling the economy should be limited to making sure that the supply of money equals the increase in GDP. This contrasted with the views of John Maynard Keynes (probably the most influential economist of the first half of the 20th century), who believed that fiscal policy was essential to moderate the business cycle - for example by raising interest rates during a boom, and lowering them in a recession.

According to Keynes, the economy was strongly affected by psychological factors. He believed that many of our decisions "can only be taken as the result of animal spirits - a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quant.i.tative benefits multiplied by quant.i.tative probabilities."13 Government policy could therefore act as a stabilising influence. Friedman, however, argued that governments couldn"t understand the economy well enough to make such judgements. Instead, he predicted that people would learn to antic.i.p.ate the government"s actions, thus rendering them useless.

For example, if the government attempted to boost the economy by printing extra money during a recession, then this would cause prices to rise and stimulate supply. However, if price inflation persisted too long, then workers, being rational people, would build the expectation of future inflation into wage demands, and firms into planned price increases. This in turn would lead to loss of jobs and higher unemployment. The government cure was therefore worse than the disease.

Friedman"s position appeared to be vindicated in the 1970s by the appearance in industrialised countries of stagflation - an unprecedented combination of high unemployment and high inflation - which defied Keynesian a.n.a.lysis or treatment. In the US, the so-called misery index - the sum of unemployment and inflation rates - reached 21 per cent. In the UK, the 1978-79 "winter of discontent" featured widespread strikes, with union leaders demanding higher pay agreements. Friedman and others blamed this disaster on Keynesian policies.

One result of stagflation was that President Carter in the US and Britain"s prime minister, James Callaghan, were thrown out of power and replaced by Friedman"s future friends and admirers, Ronald Reagan and Margaret Thatcher. Another was that economics came to be dominated by the paradigm of "rational expectations" - the idea that partic.i.p.ants in the economy act to maximise their long-term utility, based on their expectations for the future. As Naomi Klein doc.u.ments in her book The Shock Doctrine, this Chicago School model was rolled out to countries around the world from Chile to South Africa.14 Together with the Pareto optimality of the Arrow-Debreu model, and the efficient market hypothesis, it formed a compelling picture of the economy as intrinsically rational and efficient. One could argue that individual investors weren"t completely rational all the time; but to all effective purposes, the economy behaved as if they were.

The prediction test.

Inspired by all this rationality, inst.i.tutions such as the US Federal Reserve set about building elaborate Computable General Equilibrium (CGE) models to simulate the economy. These models are similar in principle to the Arrow-Debreu model, but are simplified versions in that they aggregate over large groups of consumers and other sectors of the economy. They a.s.sume that the financial system works perfectly, so no need to worry about middlemen such as banks or hedge funds. The purpose of the models is to predict how the economy"s equilibrium will react to changes in government policy, commodity prices, and so on.

Such models are still widely used by policy-makers and regulators. 15 More recent versions take dynamic and stochastic factors such as random shocks into account, but still a.s.sume the existence of an underlying equilibrium. The models crystallise economic theories into a single consistent framework that can be used to go out and make predictions about the real world. They have been around for long enough to have established a track record, so how do they match up with Friedman"s test of making accurate predictions?

The answer, as we"ve already seen, is not so well. As discussed in Chapter 1, and ill.u.s.trated in Figures 3 and 5, the ability of economic models to predict things like GDP growth or oil prices is not much better than random. Nor have they proved successful at predicting the effects of major policy changes.16 As the economist Alan Kirman noted: "almost no one contests the poor predictive performance of economic theory. The justifications given are many, but the conclusion is not even the subject of debate."17 The problem is not just that the models fail to predict, but that, like the faulty risk models used by banks, they give a false illusion of control. In a 2009 lecture, the economist Paul Krugman stated that much of the past three decades of macroeconomics was "spectacularly useless at best, and positively harmful at worst." Robert Solow observed in 2008 that macroeconomics has been "notable for paying very little rigorous attention to data ... there is nothing in the empirical performance of these models that could come close to overcoming a modest scepticism. And more certainly, there is nothing to justify reliance on them for serious policy a.n.a.lysis."18 Willem Buiter, who was a member of the Bank of England"s Monetary Policy Committee (MPC) from 1997 to 2000, stated on his blog that a training in modern macroeconomics was a "severe handicap" when it came to handling the credit crunch.19 The main effect of the models, in all their neat perfection, has been to desensitise policy-makers to the messy realities and lurking risks of the economy. Another former MPC member, David Blanchflower, wrote that although empirical data were signalling a downturn in the UK economy in mid-2007, the Committee "ignored these data, and as late as August 2008 the majority were arguing that there was not going to be a recession."20 Let alone the longest recession on record. One problem is that the models do not properly account for the role of the financial sector (which in a perfect economy isn"t necessary). The Bank of England"s general equilibrium model, for example, omits banks.21 While Friedman was one of the first to see stagflation as a possibility, not all of his predictions were so prescient. His theory that inflation could be controlled through the money supply alone, for example, turned out to be a huge mistake when it led to extreme inflation in the US and UK.22 The claim that neocla.s.sical economics can be defended on the basis of its ability to make predictions is the kind of extraordinarily counter-intuitive thing that people can say only when they are led by an unshakeable, almost religious belief that they are on the right path. Indeed, Friedman"s mentor at the University of Chicago, Frank Knight, believed that professors should teach economic theories as if they were "a sacred feature of the system" as opposed to mere hypotheses.23 Friedman"s claim of predictive accuracy makes more sense if we see it as throwing down a gauntlet to other theories. Science traditionally evolves when a theory is replaced by one that makes better predictions. If no new and better theory comes along, but there are obviously problems with the existing one, then there is no clear rule on how to proceed. Whatever is in vogue, or has the strongest and most inst.i.tutionalised support, tends to dominate.24 "To say something has failed," says Myron Scholes, "you have to have something to replace it, and so far we don"t have a new paradigm to replace efficient markets."25 So is there really no alternative economic model that can make better predictions than the neocla.s.sical one? The answer is not yet clear, but there is at least a new approach, and at its heart is the very idea of what it means to be human.

Irrational humans.

One psychological quirk of human beings is that we like to find specific rational explanations for things. In the early 1970s, one of the most commonly cited causes for stagflation was the failure of the Peruvian anchovy fishery in 1972, which was due in large part to a severe El Nino event. Anchovies were a major source of livestock feed, so the effect was to push up food prices. Another contributor was the success of the Organisation of Petroleum Exporting Countries (OPEC) at constraining oil supplies, which also boosted prices. To the monetarists, the cause of stagflation was the inept government printing too much money.

While the exact causes are debated, though, one thing was for sure: the effect of stagflation was to make people unhappy. There"s nothing like rising prices, and a risk of losing your job, to upset the electorate - which is why politicians keep a close eye on the misery index. They know that money is an emotional business. It may also turn out that the cause of inflation has less to do with things like oil, anchovies, or money supply, than with basic human emotions. In 1971, the Israeli psychologists Daniel Kahneman and Amos Tversky published a paper that explored the difference between intuition, which they called System 1 thinking, and reasoning, or System 2. System 1, according to Kahneman, is "fast, effortless, a.s.sociative, and often emotionally charged."26 It is also governed by habit, which makes it hard to change or control. System 2, in contrast, is "conscious, it"s deliberate; it"s slower, serial, effortful, and deliberately controlled, but it can follow rules."

The paper, ent.i.tled "Belief in the Law of Small Numbers," presented empirical results showing that their experimental subjects, when acting in System 1 mode, could not make accurate estimates of probability. They made extremely basic mistakes, and appeared to have no grasp of the rules of chance. This might not be surprising, except that the people they were studying were experienced statisticians.

The Law of Small Numbers in the t.i.tle was a reference to the Law of Large Numbers. This is the name of a theorem, stated without proof by Girolamo Cardano and finally proved by the mathematician Jacob Bernoulli in 1713, which says that the accuracy of a statistical sample improves with the number of samples - or as Quetelet said: "The number of individuals observed." For example, the quality of an opinion poll will be much better if it is done for a thousand people, than if it is done for ten people.

Statisticians know this very well - or at least the System 2 side of their brain knows it very well. But Kahneman and Tversky found that, in practice, they didn"t need large numbers of samples to jump to a conclusion - instead they "view a sample randomly drawn from a population as highly representative, that is, similar to the population in all essential characteristics." System 1 was leaping in with the answer before System 2 had even fired up its pocket calculator. As a result, people could be easily fooled.

During their long collaboration, Kahneman and Tversky found a number of results that directly question the neocla.s.sical a.s.sumption that we make decisions rationally. If there is such a thing as the average man (or woman), then it turns out he has some distinct psychological quirks. For example, he has an asymmetric att.i.tude towards loss and gain - he is roughly twice as sensitive to losses - so tends to avoid taking risks. He is biased by recent events, so if the market has been going up recently, then he expects that trend to continue. He dislikes change, prefers to keep what he has than trade it for something similar, and hates to give up a long-held belief. He underestimates the likelihood of extreme events, and overestimates his own ability to deal with them.

One might think that a group of people would make better decisions, and in some ways they do. But as Kahneman explains, "when everybody in a group is susceptible to similar biases, groups are inferior to individuals, because groups tend to be more extreme ... In many situations you have a risk-taking phenomenon called the risky shift. That is, groups tend to take on more risk than individuals."27 Groups also tend to be more optimistic, suppress doubts, and exhibit group-think. In larger informal groups such as markets, this can translate into herd behaviour, in which investors all rush into the market, or out of it, at the same time.

The work of Kahneman and Tversky helped create the field of behavioural finance. The area has recently been supplemented by the even newer field of neuroeconomics, which uses techniques like brain scans to find out how our brains handle economic decisions. For example, scans have shown that the offer of a reward affects different parts of the brain depending on whether the reward is immediate or delayed. The former triggers a stronger response, which may explain why many people don"t set enough aside for retirement.28 In fact, studies of patients who for neurological reasons are unable to process emotional information show that it is extremely hard to make decisions without some emotional input.29 If we really did have infinite computational capacity but no emotion, as the neocla.s.sical model demands, we would be incapable of buying a pair of socks.

Don"t mention the bubble.

The findings of behavioural economics and neuroeconomics change the way we see the financial system. According to the monetarist argument, inflation becomes established, and is resistant to government control, because the rational expectation of workers is that inflation will continue, so they insist on pay hikes. But another way to look at it is as a System 1 phenomenon. Once inflation has been around for a few years, we tend to see it as an established trend rather than a random event (this is a typical example of the Law of Small Numbers, in which we create patterns based on insufficient data). At the same time our perception of rising prices is driven by anchoring - we compare the prices with those we have grown used to, and are sensitive to any change. Our asymmetric att.i.tude towards loss means that our decrease in purchasing power outweighs any wage gains. The sight of other workers negotiating pay increases makes us afraid we are falling behind. The result is a positive feedback loop in which inflation breeds inflation. Other factors such as money supply and the overall state of the economy clearly play a role, but the problem on the human side isn"t just rational expectations - it"s also irrational expectations.

Similarly, a.s.set price bubbles are driven as much by "irrational exuberance" as by technical factors. General Equilibrium models a.s.sume that the economy reacts pa.s.sively to external shocks, like an inert machine, but the reality is that the economy is capable of generating manic surges and despondent plunges all on its own. One study of the 100 largest daily price changes in the S&P index over four decades found that, rather than being driven by news, most of the large changes happened on days when there was little to report.30 While Kahneman was eventually awarded the economics version of the n.o.bel Prize for his work (Tversky had died), the findings of behavioural economics have long been viewed suspiciously by the mainstream. To efficient market purists, things like bubbles, or irrational behaviour, are inventions of people who don"t understand the wisdom of the market. As Eugene Fama said in 2007, at the height of the US housing bubble, "economists are arrogant people. And because they can"t explain something, it becomes irrational ... The word "bubble" drives me nuts."31 Indeed, according to behavioural economist Robert Shiller, "you won"t find the word "bubble" in most economics treatises or textbooks. Likewise, a search of working papers produced by central banks and economics departments in recent years yields few instances of "bubbles" even being mentioned ... the idea that bubbles exist has become so disreputable in much of the economics and finance profession that bringing them up in an economics seminar is like bringing up astrology to a group of astronomers."32 Steve Keen wrote: "As any non-orthodox economist knows, it is almost impossible to have an article accepted into one of the mainstream academic journals unless it has the full panoply of economic a.s.sumptions: rational behaviour (according to the economic definition of rational!), markets that are always in equilibrium ... and so on."33 After the credit crunch, this scepticism may finally change. Shiller was one of the few economists to have warned of the housing bubble. Another proponent is Richard Thaler, whose book Nudge has been influential in the Obama administration. As he told the Financial Times in 2009, "conventional economics a.s.sumes that people are highly rational - super-rational - and unemotional. They can calculate like a computer, and they have no self-control problems. They never overeat, they never over-drink, they save for retirement, just the right amount - first by calculating how much they need to save, and then religiously putting the money aside. Real people are not like that."34 Governments should therefore consider "nudging" citizens into making healthy financial decisions. For example, enrolment in retirement programmes could be made the default option, so workers would have to choose to opt out if they didn"t want to take part.

Of course, to some this all smacks of Keynesianism and the idea that government knows best. Indeed, I suspect that part of the reason why behavioural economics has won a grudging degree of acceptance among our rational-thinking economists and government planners is because it tends to concentrate on the negative aspect of intuitive System 1 behaviour - of the sort frequently demonstrated by human beings - while downplaying the drawbacks of logical System 2 behaviour - of the sort frequently demonstrated by risk models, or government plans gone wrong (as we will see later, feminist economists and others who have a.s.signed a more positive role to things like human feelings have found the going harder). Logic is not always superior to intuition, and behaviour that appears narrowly rational can turn out to be highly destructive and unreasonable, if divorced from an understanding of the larger context. That"s one reason why nudging may be better than forcing.

The most eager adopters of behavioural psychology, it seems, are marketers and advertisers. As neuropsychologist David Lewis noted, purchase decisions are "more emotional than logical and generated in the oldest part of the brain," though we may rationalise them after the fact.35 Retailers and advertising companies are well ahead of economists at understanding this, because they have been nudging us into buying things since shopping was invented. Credit card firms have found, for example, that they get a better response rate to their mailings if they pay a marketing company to phone up a week or so in advance, and innocently ask the prospective customer if they are planning to make any large purchases in the near future.36

The imperfect economy.

The next step to make economics more realistic, then, is to discard forever the notion of rational economic man, and replace him with something that reflects empirical observations of how people actually behave. Economic agents should make decisions based on the information available to them, instead of a global bird"s-eye view; should employ simple rules of thumb more than abstract reasoning; should be rational at times, but not always; and should be influenced by the context and by other agents. None of this comes easily in the framework of the neocla.s.sical model, which is concerned with elegant mathematical representations of perfect markets; but it is the natural outcome in agent-based models.

The basic ingredients of a typical agent-based model are an inventory of available goods, which can be moved, changed, or traded; and a list of economic agents. These are people or firms who own, exchange, or transform goods, and provide or consume services. Their decisions are guided by a fuzzy and changeable set of needs and preferences that can be influenced by other agents or the pa.s.sing of time (so no fixed utility function). Rather than being blessed with an Apollonian ability to look into the future and maximise utility, agents can make mistakes, and they can learn from them. Trades incur costs, and involve financial intermediaries such as banks (which can go bust). There are also external drivers and constraints such as inputs of energy and outputs of waste. The model parameters are tuned to agree with the wealth of empirical data that is now available for economic transactions of all kinds.

The behaviour of the economy is determined by computer simulations that track the interactions between economic agents as they buy, sell, and trade goods and services. The model economy therefore emerges from the actions of the individual agents, just as it does in real life. The aim is not to make abstract mathematical proofs of stability or other properties, as with the Arrow-Debreu model, but instead to use the model as a kind of experimental laboratory for trying out ideas.

Because agent-based models make no a.s.sumption of equilibrium, they are particularly useful at modelling highly dynamic phenomena such as price fluctuations. For example, models have been developed in which hundreds of agents buy and sell stocks in artificial markets. Each agent has an individual strategy that can change as the agent responds to changing markets and the psychological influence of other agents. As with real markets, the prices don"t settle on a stable equilibrium, but are in a constant state of flux, with the ever-present possibility of extreme changes when investors flock in to or out of the market in unison. The models also replicate statistical features such as volatility cl.u.s.tering and power-law distributions.37 Agent-based models have also provided a new framework for studying everything from inflation to the growth and death of companies. 38 Again, the models can reproduce statistical features of the real economy that are simply unavailable to equilibrium models, such as the power-law distribution of company size. Perhaps the most ambitious model under development is that led by Silvano Cincotti at the University of Genoa, which attempts to simulate the economy of the entire European Union. The model includes about 10 million households, 100,000 companies and 100 banks, as well as government and regulatory agencies. The aim, according to Cincotti, is to "have an outstanding impact on the economic-policy capabilities of the European Union, and help design the best policies on an empirical basis."39

Why predict?

The question still remains of whether agent-based models will answer Friedman"s critique, and make better predictions. It is one thing to be able to reproduce economic behaviour and statistics; another to correctly predict how the economy will react to a change in policy or regulation.

Another point made by Friedman, with which I agree, is that if models are to be predictive, they should remain as simple as possible: otherwise the result is an overly complicated structure that can fit past data but fail to predict the future. There is plenty of empirical evidence to show that simple models make better predictions than complicated models.40 This is why hedge funds never use the CGE models beloved of economists, and prefer to rely instead on relatively simple but robust trading strategies.

Agent-based models are clearly not immune to this problem, and are best seen as incomplete pictures that capture aspects of the real economy. However, while they often contain a large number of individual agents, that doesn"t mean they are necessarily more complicated than traditional models, because the agents are usually described by a fairly short set of instructions. A property of complex systems is that the rules describing the system at the local level can be extremely simple, but the emergent behaviour can be rich and frequently surprising. Agent-based models can also help identify pockets of predictability, i.e. features that remain robust to changes in the parameters. One useful approach is to begin with a detailed model, and then use it to derive simpler models that capture aspects of the emergent behaviour.41 The main problem with economic prediction, though, has less to do with the simplicity or otherwise of the models, than with the fact that many of the features of the economy, such as stockmarket crashes, are inherently unpredictable. The aim of models should be not to predict the unpredictable, but to help design the financial system so that it is more robust. Orthodox models ignore effects such as investor irrationality, herding behaviour, destructive feedback loops, and so on, with the result that each failure of the financial system seems to come as a complete surprise. With their insistence on stability, normality, and rationality, the models prevent us from learning from our mistakes, and therefore create their own form of risk.

No model will ever be able to realistically simulate the behaviour of real people with a few lines of code. The human brain is the most complex object in the known universe (at least that"s what our brains say). But despite their drawbacks, even coa.r.s.e models that account for phenomena such as trend-following and incomplete information may be good enough to yield substantial improvements over present methods. Agent-based models can usefully simulate emergent behaviour like the flow of traffic in a city, without simulating what is going on in the head of each driver; and in the same way, they can model some aspects of the flow of money, and help to improve the design of financial markets, without knowing the basis for each individual"s decisions.

Prove it.

In mathematics, the highest kind of prediction is a mathematical proof. It doesn"t just say that something will happen tomorrow, or next week, or in most situations - it says that it will always happen. There is also a kind of entropy law that ranks proofs according to the generality of their results, divided by their complexity. It is the mathematical version of "Beauty is truth, truth beauty." The aim is to explain as much as possible in the shortest and most elegant way. Pythagoras proved that in all right-angled triangles, the sum of the squares of the sides equals the square of the hypotenuse. Cantor showed that no mathematician will ever be able to produce a list of the irrational numbers. The proofs in either case use simple, elegant, but extremely powerful arguments.

The Arrow-Debreu model was motivated by a desire to bring the same kind of beauty and clarity and permanence to economics. Gerard Debreu believed that the "acid test" for economic theories should be that of "removing all their economic interpretations and letting their mathematical infrastructure stand on its own."42 To accomplish this, though, their model had to grant the same kind of rational and prophetic powers to the economy.

Agent-based models have none of this abstract appeal. An agent-based test of the Pythagorean theorem would be to run lots of simulations of different triangles and note that they all happen to satisfy the rule. Like an experimentalist, all the modeller can do is show that the result holds at the time, and in the manner, in which it is tested. Pythagorean harmony of the spheres, this is not. E=mc2, it is not. If there is an advantage, it is that the modeller is less likely to become enraptured with the model, or confuse it with reality.

Such models do, however, provide a kind of negative proof. Because their behaviour is completely different from traditional equilibrium models - for one thing, they don"t have an equilibrium - they prove that the neocla.s.sical picture of rational utility optimisation cannot be right.

Agent-based models are also a useful way to leverage the huge quant.i.ties of economic data that are now available. "Existing economics," says n.o.bel winner Ronald Coase, "is a theoretical system which floats in the air and which bears little relation to what actually happens in the real world."43 According to economist Fischer Black, "a theory is accepted not because it is confirmed by conventional empirical tests but because researchers persuade one another that the theory is correct and relevant."44 Many papers don"t even mention actual economic data, preferring to indulge in abstract arguments that economist Roger Bootle calls "a modern form of medieval scholasticism - of no use or interest to man or beast."45 But because agent-based models are less fixed or idealistic in their a.s.sumptions, they can easily incorporate empirical insights. Their use will help turn economics into a more empirical discipline, based on observations rather than just abstract theorising.

What seems strange is that mainstream economics has held on to its picture of rational economic man for so long, despite all the evidence that people don"t behave like that. Just as there are more ways for numbers to be irrational than rational, and more ways to draw a crooked line than a straight line, so there are more ways to behave in an irrational fashion than a rational one (and we often seem to be bent on exploring them). This tenacity on the part of economists can be explained in part by behavioural psychology, and effects such as ownership bias, loss aversion, and fear of change - we hold on to ideologies as tightly as we hold on to the most treasured possessions. Economists who have been educated to believe that people behave rationally find it hard to accept that, actually, the idea was pretty dumb all along.

As shown in the next chapter, though, the idea of h.o.m.o economicus runs even deeper than that. It is again part of a 2,500-year tradition with its roots in ancient Greece - so letting go of it may be the hardest thing in the world. At least for half of the population.

CHAPTER 6.

THE GENDERED ECONOMY.

My voice was not popular. The financial markets had been expanding, innovation was thriving, and the country was prosperous. The financial services industry argued that markets had proven themselves to be self-regulating and that the role of government in market oversight and regulation should be reduced or eliminated. All of us have now paid a large price for that fallacious argument.

Brooksley Born, former chair of the Commodity Futures Trading Commission (2009).

They [Alan Greenspan, Robert Rubin, and Larry Summers] felt, I think, that they understood finance better than she did.

Jim Leach, co-author of the 1999 Gramm-Leach-Bliley Act (2009).

Independent investors who know their minds and aren"t influenced by the opinions of others; an emphasis on logic and reason over feelings and emotion; a belief in stability rather than flux and change: could it be that orthodox economic theory incorporates a gender bias? Could this be why the field is dominated at the upper levels of academia, business, and government by men? What implications does this have, not just for economic theory, but for the economy itself? How would an economy designed by women differ from the present one? Can we move away from what some call the yang economy? This chapter shows how feminist economists and business leaders are changing the way we think about money.

In August 1984, the economist Milton Friedman travelled to Reykjavik to give a lecture on Chicago School economics at the University of Iceland. While there, he also took part in a television debate with three serious-looking socialists. The moderator began by asking him to define his idea of a utopian society. He replied: "My personal utopia is one which takes the individual - or the family, if you will - as the key element in society. I would like to see a society in which individuals have the maximum freedom to pursue their own objectives in whichever direction they wish, so long as they don"t interfere with the rights of others to do the same thing." The role of government should be minimal, and restricted to areas such as defence, justice, and legislating basic rules.

The debate was three against one, but Friedman - an excellent debater - more than held his own. One of the subjects that came up was the fact that people were being charged to attend, when normally such events were free. Friedman replied that all lectures have costs, so it was only fair that they should be covered by attendees, instead of being subsidised by other people. In a healthy economy individuals look after themselves, and buy their own tickets.

This argument must have rung a bell with some of those watching, because Friedman"s positions were soon endorsed by young intellectuals in the aptly named Independence Party. One of them, Davi Oddsson, went on to become Iceland"s longest-serving prime minister (1991 to 2004) and later the governor of the central bank (2005 until April 2009). His radical reforms over that period turned Iceland from one of the world"s most heavily regulated economies into a cold, volcanic version of Friedman"s utopia.

State companies and banks were privatised, taxes were slashed, capital markets were liberalised, industrial subsidies were cut. In Iceland, if you wanted to see the show, then you had to buy your own ticket, just as Friedman said. This got a big thumbs-up from neocla.s.sical economists, including those at the Fraser Inst.i.tute, a Canadian free-market think-tank. In 1980 they had ranked Iceland 67 (of 105 in the survey) in a list of the world"s freest economies - just one above Sierra Leone.1 By 2006 that had improved to 12 (out of 141).

Many of the reforms were indeed very successful, at first. Inflation was tamed through strict monetary control. The over-subsidised fishing industry was reined in. Businessmen like the flamboyant Jon Asgeir Johannesson, CEO of Baugur, led an Icelandic invasion of the British high street, buying stakes in retailers including French Connection, Debenhams, House of Fraser, and the toy store Hamleys. The lobby of Baugur"s office in London featured a sculpture of a Viking carrying, for some reason, a guitar and a ma.s.sive fishtank.

Icelandic banks were even more adventurous. Adopting free-market reforms and the latest ideas in financial engineering, they expanded into foreign markets and became a major lender to savers in the UK, the Netherlands, and elsewhere. Not bad for a tiny country of only 320,000 people, most of whom are related by blood.

That was when Iceland experienced a different kind of volcano.

Financial terror.

One of the great but little advertised advantages of internet banking is that if you want to partake in a run on the bank, you don"t need to physically line up like in the old days - you can do it all from the convenience of your own home. You might not get your money back, but at least you don"t waste as much time, or get rained on.

This is the thought that simultaneously occurred to a large fraction of the British population in October 2008. The online bank Icesave, run by Landsbanki, had attracted hundreds of thousands of savers with its slick marketing and generous interest rates - 6.3 per cent for instant access and higher for fixed-term accounts. And it wasn"t just individuals who were stashing away their savings. Kent County Council had deposited some 50 million in Icelandic banks, and other local authorities had followed suit. But then word got out that the Icelandic economy was in trouble. The krona was losing value, inflation was spiking, banks were looking fragile, and the stockmarket was melting into the ocean. Articles appeared in the UK media saying that Iceland was on the verge of collapse. Savers got spooked and rushed online to get their money out.

Unfortunately, the Icesave website quickly iced up, which shows that even in the internet age, you have to be fast. The last people to catch on were, apparently, the UK county councils, who were still depositing tens of millions of pounds right up until the last moment (this fact was determined by the UK Audit Commission, which had itself deposited 10 million in the banks).

Things got worse when Oddsson, who was then governor of the central bank, told an Icelandic TV interviewer that: "We have decided that we are not going to pay the foreign debts of reckless people ... of the banks that have been a little heedless."2 The UK treasury immediately invoked an anti-terror act to freeze billions of pounds" worth of Icelandic a.s.sets. This sealed the fate of the Icelandic economy. Only ten days after the start of the crisis, not one of its three main banks was still standing, and the country"s brief reign as a financial power was over.

Foreign savers got their money back in the end, but in Iceland the consequences were devastating - the credit crunch bit harder there than in any other industrialised country. Interest rates, inflation, and unemployment all soared, and the krona collapsed against other currencies. The result was stagflation squared. Home construction and car sales came to an abrupt halt. Widespread anti-government protests forced Oddsson and the prime minister Geir Haarde to step down. And people started to ask what had gone wrong.

One obvious problem was that Iceland"s Financial Supervisory Authority had allowed the newly privatised banks to lend out too much money - about ten times the country"s GDP, or half a million dollars for every man, woman and child. The central bank could not therefore act as a plausible lender of last resort. When the banks got into trouble, they were on their own.

During the boom years, many people and companies had also taken out large loans in foreign currencies (an example of the carry trade discussed in Chapter 3). As the krona soared, the loans became easier to pay off; but when the economy turned, they found themselves saddled with unpayable debts.

The UK government also played a part with its decision to use anti-terror legislation. Banking depends on trust, and it is considered poor form to have your central bank and ministry of finance appear on the UK"s official list of terrorist organisations, right up there with al Qaeda and the Taliban.

In Iceland, though, many believed that the roots of the problem lay in a particular culture and a particular group of people who had held power for far too long. As Halla Tomasdottir from Audur Capital told Der Spiegel: "The crisis is man-made. It"s always the same guys. Ninety-nine per cent went to the same school, they drive the same cars, they wear the same suits and they have the same att.i.tudes." She described their focus on short-term profits, without any concern for the wider consequences, as "typical male behavior" akin to a "p.e.n.i.s compet.i.tion."3 In other words - and I may be reading between the lines here - the credit crunch was some kind of guy thing.