Why Did Economists Fail to Predict the Great Recession?
The Great Recession of 2008 had been the most severe global economic downturn since the Great Depression of the 1930s. However, despite its sheer magnitude and the origins deeply rooted within the financial sector that are painfully obvious in hindsight, the vast majority of economists at the time seemingly failed to predict it. As a result, the discipline of economics had fallen under criticism and scrutiny, from both the general public and its own community, with debate continuing to be rife. Even the late Queen, on a visit to the London School of Economics shortly following the financial turmoil, felt compelled to ask, ‘Why did nobody notice it?’ – the answer she would receive four years later, on a visit to the Bank of England, was that economists placed too much faith into the efficiency of markets which, in combination with the lack of awareness over the interconnectedness of the financial system, resulted in too little emphasis being placed on regulative measures, as reported by Vale (2012). While this is certainly true, it is too simplified of an answer and requires closer inspection. Identifying and understanding these is crucial for economics to retain its prestige and validity as a social science, for they provide insights into the limitations of mainstream macroeconomics and how to overcome them. It is morally necessary to use this knowledge towards generating a realistic understanding of the economy in today’s world, where economists dominate government decision-making and thus have an impact on the lives of ordinary people.
Certain factors resulted in mainstream macroeconomic modelling failing to detect the lead up and offer causes to the Great Recession. One of these was a lack of awareness over the significance of the financial sector, and the impact it could harbour onto the wider economy. Perhaps this was due to postwar recessions preceding the Great Recession appearing to be largely disconnected from financial markets, leaving them to be considered an unnecessary detail. As such, a majority of macroeconomic models, including the dynamic stochastic general equilibrium (DSGE) models (Christiano et al., 2018) that are used as the standard in academia and central banks to forecast economic activity, had oversimplified the financial sector by excluding elements such as debt, household balance sheets and asset prices (Hendry and Muelbauer, 2018, pp.296-298), all of which were leading contributors to the financial crisis. In other words, the lack of attempt at modelling the economy in a more realistic manner by incorporating financial features had resulted in economists failing to detect any imminent financial crisis and how it could effect the wider economy, largely due to the misconception that such an attempt was needless.
The former is closely correlated to another failure of mainstream macroeconomic modelling: unrealistic assumptions. Some of the most central and widely declared assumptions that led to the failure of detecting the Great Recession are those of rational expectations and the belief in a self-correcting market that always tends to an equilibrium. The former of these is the idea of ‘homo economicus’ (or the ‘economic man’): individuals are believed to be capable of computing information without failure, and thus always commit to the course of action that maximises utility given their preferences. This also often inherently assumes that individuals possess perfect information of market conditions, though as Stiglitz noted in 2010, this was certainly not true during the financial crisis (more on this later). Nevertheless, the latter is self-explanatory: DSGE models (as their name suggests) revolve around a ‘general equilibrium’ that the market always returns to. Deviations from this equilibrium were expected, though they would be small and short-lived. The idea of a self-correcting market that tends to an equilibrium, much like the idea of homo economicus, appears to be something that mainstream economics assumes axiomatically (Arnsperger and Varoufakis, 2008). However, the expected non-volatile nature of deviations from market equilibrium was influenced by the ‘Great Moderation’. This period, spanning from the 1980s until its abrupt end marked by the crisis, was characterised by the decreasing volatility of output throughout the business cycle. Although debate surrounds its cause, the independence of central banks and improvements in the conduct of monetary policy that followed soon after offer the most compelling explanation. As DSGE models used previous data to create economic forecasts, an economic shock of that magnitude was bound to remain undetected.
Given these assumptions, it is easy to see why the regulation of markets was viewed with scepticism. The idea of a self-correcting market implies that such regulation is unnecessary and potentially harmful, as it could lead to the misallocation of resources. Moreover, the assumption of homo economicus implies that consumers know best, and thus intervention of any kind contradicts their wishes. On the other hand, another reason behind the lacking emphasis towards regulation was the aforementioned lack of understanding over the complexity and interconnectedness of the financial system. As Stiglitz remarks, the complexity of sophisticated financial products such as collateralized debt obligations stumped experts in the field, including rating agencies and regulatory authorities. This meant that the high risk attached to them remained unregulated, and those purchasing them remained unaware of their risk and thus uninsured.
The detachment from realism in mainstream economics is best explained by the instrumentalist methodology of mainstream economics. At its most extreme, this is the idea that the realism of an assumption is irrelevant, as long as it allows for an accurate prediction to be calculated. This methodology has famously been defended by prominent mainstream economists such as Lucas (1981, p.270), who stated that modelling has to be ‘artificial, abstract, patently unreal’. To understand this more easily, think of economic modelling today: by abstracting details and simplifying reality, we are able to calculate and understand an outcome more easily. The methodology of instrumentalism has led to the increasing ‘formalisation’ of economics, becoming characterised by elaborate mathematical modelling. For this reason, nuanced concepts such as uncertainty have been virtually eradicated from mainstream economics and replaced by mathematical concepts such as risk. Under risk, the specific outcome of an action is unknown, but the probability distribution of possible outcomes occurring is calculable (for instance, think of the ‘stochastic’ element of DSGE models). On the other hand, future outcomes are thought to be inherently unknowable under uncertainty. At the end of the day, however, instrumentalism and the formalisation of economics can in fact be helpful when implemented correctly. Of course, if models lead to false conclusions, the lack of realism becomes problematic, though if the model provides an accurate result, the simplification of the economy becomes a virtue by allowing for economic phenomena to be understood more coherently. Furthermore, formalisation allows economics to be expressed in a more easily interpretable manner. After all, policymakers require concrete statistics to design effective policy rather than abstract ideas. It is not through the pursuit of completely abstracting reality and neglecting nuanced concepts such as uncertainty that accurate economic modelling is achieved, nor through the complete disregard of simplicity in modelling. Instead, extremes must be avoided, and a careful balance between instrumentalism and realism must be pursued when designing assumptions. Had such a methodology been implemented more closely prior to the crisis, perhaps economists may have been more capable of foreseeing it.
It is because of this that I believe most of the misconceived assumptions held by mainstream economics is not the product of instrumentalism, but instead a concerning lack of interdisciplinary knowledge that a worrying majority of economists hold. Economics is rightly considered by most to be a ‘social science’. Despite this, economists at the precipice of the crisis seemingly refused to learn valuable lessons from the other social sciences. In a paper that shamelessly and pretentiously declares ‘the superiority of economists’, Fourcade et al. detail that between 2000-2009, 40.3% of citations in the American Economic Review originated from the top 25 economics journals. This is strikingly high compared to the American Political Science Review and the American Sociological Review, with 17.5% and 22% of their citations respectively originating from the top 25 journals of their according social science. What this implies is a visible reluctance in economists to explore valuable ideas from other social sciences, despite many of these ideas relating directly to economic phenomena. As John Maynard Keynes beautifully put it:
‘The master-economist . . . must be a mathematician, historian, statesman, philosopher – in some degree. He must understand symbols and speak in words. He must contemplate the particular in terms of the general and touch abstract and concrete in the same flight of thought. He must study the present in the light of the past for the purposes of the future. No part of man’s nature or his institutions must lie entirely outside his regard’.
How can the economist formulate effective policy without mathematics? How can the economist learn from his past mistakes without knowledge of history? How can the economist accurately grasp the dynamic nature of the economy without understanding factors such as the political influences over it? How can he formulate an intellectually sound argument without an understanding of logic or metaphysical foundations? How can he understand man’s nature without studying human psychology? Economics is a phenomenal tool to solve many allocative issues, however, it is in no way superior to other social sciences as it relies on their insights to fuel its modelling toolkit, or at the very least rigorous and intellectually sound economics does so.
The crisis had clearly demonstrated, for example, that human psychology has a much greater impact on the wider economy than previously thought. In build up to the crisis, herding behaviour became rife, with individuals copying each other in signing up for complex subprime mortgages that were largely misunderstood (McDonald, 2009). This could also have been observed in the bank runs that followed as the crisis unravelled: as one consumer panics to withdraw their savings, other consumers panic in response and seek to do the same. Of course, this heavily contradicts the rationality assumption that was held so dearly by economists, resembling the concept of ‘bounded’ rationality more closely. Nevertheless, it is perhaps because of these lessons that behavioural economics has continued to gain increasing traction in mainstream economics. Its efforts to implement human psychology into understanding economics provide hope for the future of the field and prove that instrumentalism and realism are not mutually exclusive.
On the other hand, unlike mainstream economics, which is only now adopting interdisciplinary ideas more heavily, the ideas of heterodox economists such as Post-Keynesians, not to be confused with mainstream New-Keynesians, have long implemented concepts of historical path dependence, bounded rationality and fundamental uncertainty into their modelling. Because of their pursuit of realism, Post-Keynesians have relied less on mathematical formulation, though this is not to say that econometric Post-Keynesian models are non-existent, as exemplified by the works of Steve Keen, which include some very detailed Post-Keynesian econometric modelling.
But it is through this pursuit of realism that the ideas of economists such as Hyman Minsky, particularly those of his financial instability hypothesis, have brought him out of obscurity for providing a compelling explanation to financial crises that correlated very closely to that of the 2008 financial crash. Indeed, some have labelled the crisis a ‘Minsky moment’ (Lavoie, 2016).
This is not to say that heterodox economics is ‘superior’ to orthodox mainstream economics in any way. Their approach to studying and modelling economics are fundamentally different and impossible to compare in any ‘objective’ manner. What I am arguing, however, is that ideas from every corner of the discipline must be considered and incorporated where necessary. Modelling is dependent on context; whereby certain assumptions and their level of reality are better suited for certain scenarios over others. For instance, it is understandable to see why the financial sector had been abstracted so heavily from DSGE models, for its significance had not been made relevant prior to the crisis. Because of this, the idea of linearity – that the size of the shock determined the size of the impact – reigned supreme, though it is evident that, in a post-Great Recession, world such an assumption would be erroneous. It is for this reason that I argue there is no ‘one best model’, or an inherent ‘best’ school of thought in the discipline. Instead, a pragmatic approach that considers a wide range of appropriate assumptions from a variety of schools of thought, and an approach that uses a variety of models or tweaks existing models to suit a given context, are the best hope that economics has going forward, similarly to the proposition of Blanchard (2014, 2018).
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