IT ‘S TIME TO JUNK THE FLAWED ECONOMIC MODELS THAT MAKE THE WORLD A DANGEROUS PLACE
It’s 26 years since Paul Romer shook the discipline of economics with a single research paper. Entitled Endogenous Technological Change, Romer’s article showed how information technology changed something fundamental about our world – moving the focus of economics away from land, labour and capital towards “people, ideas and things”. Last week, a generation later, Romer published what many see as an equally significant intervention. Macroeconomics, he argues, is like a science that has not only stalled for three decades, but has actually gone backwards in its ability to understand reality.
In the late 1970s, as the old certainties of Keynesianism collapsed, a new generation of economists moved the discipline on to the terrain of super-abstract equations. Their assumption was that the economy tends towards equilibrium, and that only unpredictable shocks from outside the system can disturb it. Since the shocks come from outside, for the purposes of these mathematical models, the economist has to imagine what they might be. In The Trouble With Macroeconomics, Romer mocks these imaginary disruptions. He compares the result to a kind of physics that only works if there are “trolls, gremlins and aether”.
It’s not a new line of attack. The Kingston University economist Steve Keen has long argued that reliance on flawed models contributed to the scale of the 2008 crash – by encouraging decision-makers to underestimate risks, economic theory has the power to make the world more dangerous. But Keen is a lifelong rebel; Romer is a doyen of the profession, and from the heart of the US academic mainstream. His attack on some of the most esteemed and influential economists of our time is a big thing.
And the stakes are big, too. One of the theories that, even now, eight years after the crash, continues to disorient policymakers is the assumption that actions by central banks are irrelevant. A total of $12tn (£9.1tn) has been printed by central banks to stave off global depression, yet the threat remains real. Stagnation is a threat that keeps central bankers, governments and social theorists awake at night – with the palliative always being looser monetary policy.
Yet orthodox economic theory insists it would have no real effect if the central banks pulled all this support – since the equations tell them there is no correlation between monetary policy and output. Mark Carney or Mario Draghi could double interest rates and slash quantitative easing and the economy should grow at just the same rate, says the theory.
Romer, scathingly, calls this “post-real” economics, and suggests a horribly simple explanation for its popularity: human frailty. Comparing the economics elite with its equivalent in theoretical physics, Romer notes the same problems: over-confidence, “an unusually monolithic community”, near-religious group loyalties, a tendency to disregard results that don’t match the theory – and too little consideration of the risks of being wrong.
This is not just a problem for economics. Romer says the parallels between bad physics and bad economics suggest there might be a “general failure mode” in any discipline that becomes over-reliant on maths. Basically, the kudos goes to people at the cutting edge of designing mathematical models, not to those whose models match reality. If Romer is right, there are big implications for the way governments and central banks make policy. Instead of abstract models, you would need something much closer to reality – and, with the rise of computer simulation technologies, that is close at hand.
The agent-based model, instead of reducing reality to a few variables, tries to replicate reality – and its randomness – in detail. Such models are common in weather prediction, or city transport planning: think of them as a professional version of the computer game Sim City. In an agent-based model, you don’t try to work out whether a million people will, on aggregate, buy more bread or less bread. You create a million digital “people” and unleash them in world with digital bread and digital money.
Oxford professor J Doyne Farmer has long advocated the adoption of agent-based modelling in economics; the Bank of England’s chief economist, Andy Haldane, is a convert. Reality, says Haldane, is not only more complex than the maths-based economics imagines, it is also not rational. The sum of buying and selling decisions we take each day – from the cappuccino and croissant on the way to work, to the fund we keep our pension in – are driven by something other than the rationality that mainstream economists assume. As a result, while the old, maths-based economist expects stability and assumes a “gremlin” where it is disrupted, the heterodox economist expects big and unpredictable shocks.
If you stood close enough to Marx’s grave in Highgate, since Romer’s paper got released last week, you might hear a deep Germanic chuckle coming from beneath the stones. Marx, too, was a fan of abstraction – and worried so much that he was out of the loop of maths-based economics in the 1870s that he produced a 1,000-page notebook documenting his attempts to learn differential calculus.
But, in the end, Marx wanted to use maths to model the core unpredictabilities that have become so obvious to us in the past 10 years: boom-bust cycles that the professionals told us were impossible; depressions that the giants of modern academia assured us had been solved decades ago. Marx never even came close to achieving this – and since then, economics has veered between stability and instability theories, about every 25 years.
Romer’s huge mea culpa on behalf of mainstream economics is a sign that, after a decade-long hunt for trolls and gremlins as the cause of crisis, academia now has to begin the search for the cause of instablity inside the system, not outside it. My hunch is that the answer lies in large, agent-based simulations, in which millions of virtual people take random decisions driven by irrational urges – such as sex and altruism – not just the pursuit of wealth.
What the left can bring to the design of these models are the insights that still draw lines of emnity through elite campuses: that class, gender and race exist as economic facts; that the 1% always acts with more information than the 99%; that crises are unavoidable but can be mitigated by accepting they might happen.
And above all: that sacking or excluding people who insist “capitalism is unstable” is a bad idea if you are running, say, a treasury, a major political party or a central bank.