It may be a little too much to claim that a reformation is underway in economic theory, but the protracted global economic crisis has brought thinkers previously considered heterodox increasingly into the mainstream of policy discussions, while currents of thinking that challenge orthodox neoclassical economics – like complexity, network and evolutionary economics – are gathering strength and gaining ground.
At IPPR, we’ve been running a programme of seminars and events looking at these new currents of thought, entitled New Era Economics, and today we’ve published some preview chapters from a forthcoming collection of essays from leading practitioners of this thinking.
Eric Beinhocker, author of The Origin of Wealth and executive director of the Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, has this neat summary table on the differences between orthodox and new economic thinking in his chapter:
One of the purposes of the new IPPR collection is to draw these conceptual advances closer to public policymaking, so as to begin to think through how they might change economic policy in countries like the UK. One example from Eric’s chapter is illustrative:
In 2006, economists at the US Federal Reserve conducted an analysis of what would happen to the US economy if house prices suddenly dropped by 20 per cent. Officials at the central bank had noted the unprecedented run-up in house prices and become concerned. They ran the analysis on their state of the art macroeconomic model and the answer that came back was ‘not much’. Growth might soften, or there might even be a mild recession, but nothing that a few small interest rate cuts couldn’t handle. The model had done exactly what such traditional models are designed to do. It assumed everyone would behave rationally, markets would function efficiently, and the system would smoothly selfcorrect back to full-employment equilibrium.
At around the same time, Fed chairman Alan Greenspan was repeatedly asked by the media, congressmen, and others whether there was a housing bubble. Greenspan, a devotee of efficient market theory and fan of Ayn Rand’s libertarian philosophy, consistently replied that the run-up in prices must have good rational reasons, there was little evidence of a bubble, and even if there was, the Fed should not intervene to burst it as the markets would eventually self-correct and government intervention would likely do more harm than good.
We all know what happened. The bubble burst and it triggered a catastrophic financial collapse, almost instantly wiped out $10.8 trillion in wealth in the US alone, nearly led to a second great depression, and we are still dealing with the consequences, most notably the ongoing euro crisis. In late 2008, Greenspan gave his famous mea culpa saying, ‘I have found a flaw’ in orthodox free-market theory, ‘I don’t know how significant or permanent it is. But I have been very distressed by that fact.’
Might new economic techniques and models have given a different view? Might they have helped policymakers avoid such a disastrous outcome? A team of researchers led by John Geanakoplos at Yale, Robert Axtell at George Mason, my colleague Doyne Farmer at Oxford and Peter Howitt at Brown think so. They have constructed an agent-based model of the housing market that gives new insights into what caused the bubble and the model could eventually become a tool to assist policymakers in designing strategies for preventing or managing future bubbles.
Their model is radically different from the kind of models the Fed used in its 2006 analysis. Rather than look at the economy topdown and in aggregate, they model the system bottom-up. Their model has individual households in it, and for those owning houses rather than renting, individual mortgages backed by houses of a certain value. This population of households is heterogeneous – some have mortgages they can easily afford, some don’t, and the terms of their mortgages may differ. The households are assumed to behave in ways consistent with how behavioural economists tell us real people behave. Rather than doing elaborate calculations the agents in the model use rules of thumb (for example, one shouldn’t take on a mortgage more than three times one’s annual income) but individuals vary in their use of such rules (some might be more conservative, others more risk-taking). They also introduce institutional realism, for example if interest rates drop you might consider refinancing, but you might not automatically do it if the hassle factor is too high.
The initial version of the model uses detailed mortgage and household data from a single metropolitan area – Washington, DC. The team eventually plan to calibrate it with data from other major cities, and possibly the whole of the US and other countries such as the UK as well. Their preliminary findings reproduce the dynamics of the bubble building up and then bursting. Unlike traditional analyses, the model doesn’t gently self-correct, it crashes (see figure 1a). They then run policy experiments on the model, asking what policymakers might have done to prevent the bubble forming, or at least stopped it building once it was clear there was one. Analyses using traditional models have tended to blame the bubble on overly loose monetary policy from the Fed. So the team tested scenarios where policymakers raise interest rates. The bubble is indeed moderated, but not eliminated (figure 1b). But interest rates are a blunt instrument and such tightening would also have slowed growth in the rest of the economy. So the team also tried a regulatory intervention – preventing banks from loosening their loan-to-value ratios. During the heat of the bubble, banks competed with each other to give loans, loosening their standards. The team’s model shows that by intervening to prevent these standards from slipping, policymakers might have eliminated a key dynamic in the bubble’s formation, prevented the subsequent bust, and done so more effectively and without the collateral damage caused by a big interest rate hike (figure 1c).
Agent-based model of the US housing market, 1998–2010 (index 1 = first period)
Source: Geanakoplos J et al (2012) ‘Getting at Systemic Risk via an Agent-Based Model of the Housing Market’, American Economic Review Papers and Proceedings 2012, 102(3): 53–58
Given the size of the housing bubble that built up, and the massive consequences of that bubble bursting in countries like the US, it is clear that developing new economic models of this kind could have very significant implications for policymakers. Our collection is the first sustained attempt to set out how the new economic thinking might be applied to areas of policy such as macroeconomic frameworks, innovation and skills, climate change and more.
You can read all the preview chapters here. The full book will be available in the week of 20 August.