A resilience approach focuses its interventions on severe disturbances, whilst allowing small and moderate disturbances to play themselves out. Even when the disturbance is severe, a resilience approach avoids stamping out the disturbance at source and focuses its efforts on mitigating the wider impact of the disturbance on the macroeconomy. The primary aim is the minimisation of the long-run fragilising consequences of the intervention which I have explored in detail in many previous posts(1, 2, 3). Just as small fires and floods are integral to ecological resilience, small disturbances are integral to macroeconomic resilience. Although it is difficult to identify ex-ante whether disturbances are moderate or not, the Greenspan-Bernanke era nevertheless contains some excellent examples of when not to intervene. The most obvious amongst all the follies of Greenspan-era monetary policy were the rate cuts during the LTCM collapse which were implemented with the sole purpose of “saving” financial markets at a time when the real economy showed no signs of stress¹.

The Minsky/Bagehot approach focuses on tackling all disturbances with debt-deflationary consequences at their source. Bagehot asserted in ‘Lombard Street’ that “in wild periods of alarm, one failure makes many, and the best way to prevent the derivative failures is to arrest the primary failure which causes them”. Minsky emphasised the role of both the lender-of-last-resort (LOLR) mechanism as well as fiscal stabilisers in tackling such “failures”. However Minsky was not ignorant of the long-term damage inflicted by a regime where all disturbances were snuffed out at source – the build-up of financial “innovation” designed to take advantage of this implicit protection, the descent into crony capitalism and the growing fragility of a private-investment driven economy², an understanding that was also reflected in his fundamental reform proposals³. Minsky also appreciated that the short-run cycle from hedge finance to Ponzi finance does not repeat itself in the same manner. The long-arc of stabilised cycles is itself a disequilibrium process (a sort of disequilibrium super-cycle) where performance in each cycle deteriorates compared to the last one – an increasing amount of stabilisation needs to be applied in each short-run cycle to achieve poorer results compared to the previous cycle.

Resilience Approach: Policy Implications

As I have outlined in an earlier post, an approach that focuses on minimising the adaptive consequences of macroeconomic interventions implies that macroeconomic policy must allow the “river” of the macroeconomy to flow in a natural manner and restrict its interventions to insuring individual economic agents rather than corporate entities against the occasional severe flood. In practise, this involves:

• De-emphasising the role of conventional and unconventional monetary policy (interest-rate cuts, LOLR, quantitative easing, LTRO) in tackling debt-deflationary disturbances.
• De-emphasising the role of industrial policy and explicit bailouts of banks and other firms⁴.
• Establishing neutral monetary-fiscal hybrid policies such as money-financed helicopter drops as the primary tool of macroeconomic stabilisation. Minsky’s insistence on the importance of LOLR operations was partly driven by his concerns that alternative policy options could not be implemented quickly enough⁵. This concern is less relevant with regards to helicopter drops in today’s environment where they can be implemented almost instantaneously⁶.

Needless to say, the policies we have followed throughout the ‘Great Moderation’ and continue to follow are anything but resilient. Nowhere is the farce of orthodox policy more apparent than in Europe where countries such as Spain are compelled to enforce austerity on the masses whilst at the same time being forced to spend tens of billions of dollars in bailing out incumbent banks. Even within the structurally flawed construct of the Eurozone, a resilient strategy would take exactly the opposite approach which will not only drag us out of the ‘Great Stagnation’ but it will do so in a manner that delivers social justice and reduced inequality.

This narrative naturally begs the question: when was the last time we had a truly disruptive Schumpeterian era of creative destruction. In a previous post looking at the evolution of the post-WW2 developed economic world, I argued that the so-called Golden Age was anything but Schumpeterian – As Alexander Field has argued, much of the economic growth till the 70s was built on the basis of disruptive innovation that occurred in the 1930s. So we may not have been truly Schumpeterian for at least 70 years. But what about the period from at least the mid 19th century till the Great Depression? Even a cursory reading of economic history gives us pause for thought – after all wasn’t a significant part of this period supposed to be the Gilded Age of cartels and monopolies which sounds anything but disruptive.

I am now of the opinion that we have never really had any long periods of constant disruptive innovation – this is not a sign of failure but simply a reality of how complex adaptive systems across domains manage the tension between efficiency,robustness, evolvability and diversity. What we have had is a subverted control revolution where repeated attempts to achieve and hold onto an efficient equilibrium fail. Creative destruction occurs despite our best efforts to stamp it out. In a sense, disruption is an outsider to the essence of the industrial and post-industrial period of the last two centuries, the overriding philosophy of which is automation and algorithmisation aimed at efficiency and control. And much of our current troubles are a function of the fact that we have almost perfected the control project.

The operative word and the source of our problems is “almost”. Too many people look at the transition from the Industrial Revolution to the Algorithmic Revolution as a sea-change in perspective. But in reality, the current wave of reducing everything to a combination of “data & algorithm” and tackling every problem with more data and better algorithms is the logical end-point of the control revolution that started in the 19th century. The difference between Ford and Zara is overrated – Ford was simply the first step in a long process that focused on systematising each element of the industrial process (production,distribution,consumption) but also crucially putting in place a feedback loop between each element. In some sense, Zara simply follows a much more complex and malleable algorithm than Ford did but this algorithm is still one that is fundamentally equilibriating (not disruptive) and focused on introducing order and legibility into a fundamentally opaque environment via a process that reduces human involvement and discretion by replacing intuitive judgements with rules and algorithms. Exploratory/disruptive innovation on the other hand is a disequilibriating force that is created by entrepreneurs and functions outside this feedback/control loop. Both processes are important – the longer period of the gradual shedding of diversity and homogenisation in the name of efficiency as well as the periodic “collapse” that shakes up the system and puts it eventually on the path to a new equilibrium.

Of course, control has been a aim of western civilisation for a lot longer but it was only in the 19th century that the tools of control were good enough for this desire to be implemented in any meaningful sense. And even more crucially, as James Beniger has argued, it was only in the last 150 years that the need for large-scale control arose. And now the tools and technologies in our hands to control and stabilise the economy are more powerful than they’ve ever been, likely too powerful.

If we had perfect information and everything could be algorithmised right now i.e. if the control revolution had been perfected, then the problem disappears. Indeed it is arguable that the need for disruption in the innovation process no longer exists. If we get to a world where radical uncertainty has been eliminated, then the problem of systemic fragility is moot and irrelevant. It is easy to rebut the stabilisation and control project by claiming that we cannot achieve this perfect world.

But even if the techno-utopian project can achieve all that it claims it can, the path matters. We need to make it there in one piece. The current “algorithmic revolution” is best viewed as a continuation of the process through which human beings went from being tool-users to minders and managers of automated systems. The current transition is simply one where the many of these algorithmic and automated systems can essentially run themselves with human beings simply performing the role of supervisors who only need to intervene in extraordinary circumstances. Therefore, it would seem logical that the same process of increased productivity that has occurred during the modern era of automation will continue during the creation of the “vast,automatic and invisible” ‘second economy’. However there are many signs that this may not be the case. What has made things better till now and has been genuine “progress” may make things worse in higher doses and the process of deterioration can be quite dramatic.

The Uncanny Valley on the Path towards “Perfection”

In 1970, Masahiro Mori coined the term ‘uncanny valley’ to denote the phenomenon that “as robots appear more humanlike, our sense of their familiarity increases until we come to a valley”. When robots are almost but not quite human-like, they invoke a feeling of revulsion rather than empathy. As Karl McDorman notes, “Mori cautioned robot designers not to make the second peak their goal — that is, total human likeness — but rather the first peak of humanoid appearance to avoid the risk of their robots falling into the uncanny valley.”

A similar valley exists in the path of increased automation and algorithmisation. Much of the discussion in this section of the post builds upon concepts I explored via a detailed case study in a previous post titled ‘People Make Poor Monitors for Computers’.

The 21st century version of the control project i.e. the algorithmic project consists of two components:
1. More Data – ‘Big Data’.
2. Better and more comprehensive Algorithm.

The process goes hand in hand therefore with increased complexity and crucially, poorer and less intuitive feedback for the human operator. This results in increased fragility and a system prone to catastrophic breakdowns. The typical solution chosen is either further algorithmisation i.e. an improved algorithm and more data and if necessary increased slack and redundancy. This solution exacerbates the problem of feedback and temporarily pushes the catastrophic scenario further out to the tail but it does not eliminate it. Behavioural adaptation by human agents to the slack and the “better” algorithm can make a catastrophic event as likely as it was before but with a higher magnitude. But what is even more disturbing is that this cycle of increasing fragility can occur even without any such adaptation. This is the essence of the fallacy of the ‘defence in depth’ philosophy that lies at the core of most fault-tolerant algorithmic designs that I discussed in my earlier post – the increased “safety” of the automated system allows the build up of human errors without any feedback available from deteriorating system performance.

A thumb rule to get around this problem is to use slack only in those domains where failure is catastrophic and to prioritise feedback when failure is not critical and cannot kill you. But in an uncertain environment, this rule is very difficult to manage. How do you really know that a particular disturbance will not kill you? Moreover the loop of automation -> complexity -> redundancy endogenously turns a non-catastrophic event into one with catastrophic consequences.

This is a trajectory which is almost impossible to reverse once it has gone beyond a certain threshold without undergoing an interim collapse. The easy short-term fix is always to make a patch to the algorithm, get more data and build in some slack if needed. An orderly rollback is almost impossible due to the deskilling of the human workforce and risk of collapse due to other components in the system having adapted to new reality. Even simply reverting to the old more tool-like system makes things a lot worse because the human operators are no longer experts at using those tools – the process of algorithmisation has deskilled the human operator. Moreover, the endogenous nature of this buildup of complexity eventually makes the system fundamentally illegible to the human operator – a phenomenon that is ironic given that the fundamental aim of the control revolution is to increase legibility.

The Sweet Spot Before the Uncanny Valley: Near-Optimal Yet Resilient

Although it is easy to imagine the characteristics of an inefficient and dramatically sub-optimal system that is robust, complex adaptive systems operate at a near-optimal efficiency that is also resilient. Efficiency is not only important due to the obvious reality that resources are scarce but also because slack at the individual and corporate level is a significant cause of unemployment. Such near-optimal robustness in both natural and economic systems is not achieved with simplistically diverse agent compositions or with significant redundancies or slack at agent level.

Diversity and redundancy carry a cost in terms of reduced efficiency. Precisely due to this reason, real-world economic systems appear to exhibit nowhere near the diversity that would seem to ensure system resilience. Rick Bookstaber noted recently, that capitalist competition if anything seems to lead to a reduction in diversity. As Youngme Moon’s excellent book ‘Different’ lays out, competition in most markets seems to result in less diversity, not more. We may have a choice of 100 brands of toothpaste but most of us would struggle to meaningfully differentiate between them.

Similarly, almost all biological and ecological complex adaptive systems are a lot less diverse and contain less pure redundancy than conventional wisdom would expect. Resilient biological systems tend to preserve degeneracy rather than simple redundancy and resilient ecological systems tend to contain weak links rather than naive ‘law of large numbers’ diversity. The key to achieving resilience with near-optimal configurations is to tackle disturbances and generate novelty/innovation with an an emergent systemic response that reconfigures the system rather than simply a localised response. Degeneracy and weak links are key to such a configuration. The equivalent in economic systems is a constant threat of new firm entry.

The viewpoint which emphasises weak links and degeneracy also implies that it is not the keystone species and the large firms that determine resilience but the presence of smaller players ready to reorganise and pick up the slack when an unexpected event occurs. Such a focus is further complicated by the fact that in a stable environment, the system may become less and less resilient with no visible consequences – weak links may be eliminated, barriers to entry may progressively increase etc with no damage done to system performance in the stable equilibrium phase. Yet this loss of resilience can prove fatal when the environment changes and can leave the system unable to generate novelty/disruptive innovation. This highlights the folly of statements such as ‘what’s good for GM is good for America’. We need to focus not just on the keystone species, but on the fringes of the ecosystem.

 THE UNCANNY VALLEY AND THE SWEET SPOT

The Business Cycle in the Uncanny Valley – Deterioration of the Median as well as the Tail

Many commentators have pointed out that the process of automation has coincided with a deskilling of the human workforce. For example, below is a simplified version of the relation between mechanisation and skill required by the human operator that James Bright documented in 1958 (via Harry Braverman’s ‘Labor and Monopoly Capital’). But till now, it has been largely true that although human performance has suffered, the performance of the system has gotten vastly better. If the problem was just a drop in human performance while the system got better, our problem is less acute.

AUTOMATION AND DESKILLING OF THE HUMAN OPERATOR

But what is at stake is a deterioration in system performance – it is not only a matter of being exposed to more catastrophic setbacks. Eventually mean/median system performance deteriorates as more and more pure slack and redundancy needs to be built in at all levels to make up for the irreversibly fragile nature of the system. The business cycle is an oscillation between efficient fragility and robust inefficiency. Over the course of successive cycles, both poles of this oscillation get worse which leads to median/mean system performance falling rapidly at the same time that the tails deteriorate due to the increased illegibility of the automated system to the human operator.

THE UNCANNY VALLEY BUSINESS CYCLE

The Visible Hand and the Invisible Foot, Not the Invisible Hand

The conventional economic view of the economy is one of a primarily market-based equilibrium punctuated by occasional shocks. Even the long arc of innovation is viewed as a sort of benign discovery of novelty without any disruptive consequences. The radical disequilibrium view (which I have been guilty of espousing in the past) is one of constant micro-fragility and creative destruction. However, the history of economic evolution in the modern era has been quite different – neither market-based equilibrium nor constant disequilibrium, but a series of off-market attempts to stabilise relations outside the sphere of the market combined with occasional phase transitions that bring about dramatic change. The presence of rents is a constant and the control revolution has for the most part succeeded in preserving the rents of incumbents, barring the occasional spectacular failure. It is these occasional “failures” that have given us results that in some respect resemble those that would have been created by a market over the long run.

As Bruce Wilder puts it (sourced from 1, 2, 3 and mashed up together by me):

The main problem with the standard analysis of the “market economy”, as well as many variants, is that we do not live in a “market economy”. Except for financial markets and a few related commodity markets, markets are rare beasts in the modern economy. The actual economy is dominated by formal, hierarchical, administrative organization and transactions are governed by incomplete contracts, explicit and implied. “Markets” are, at best, metaphors…..
Over half of the American labor force works for organizations employing over 100 people. Actual markets in the American economy are extremely rare and unusual beasts. An economics of markets ought to be regarded as generally useful as a biology of cephalopods, amid the living world of bones and shells. But, somehow the idealized, metaphoric market is substituted as an analytic mask, laid across a vast variety of economic relations and relationships, obscuring every important feature of what actually is…..
The elaborate theory of market price gives us an abstract ideal of allocative efficiency, in the absence of any firm or household behaving strategically (aka perfect competition). In real life, allocative efficiency is far less important than achieving technical efficiency, and, of course, everyone behaves strategically.
In a world of genuine uncertainty and limitations to knowledge, incentives in the distribution of income are tied directly to the distribution of risk. Economic rents are pervasive, but potentially beneficial, in that they provide a means of stable structure, around which investments can be made and production processes managed to achieve technical efficiency.
In the imaginary world of complete information of Econ 101, where markets are the dominant form of economic organizations, and allocative efficiency is the focus of attention, firms are able to maximize their profits, because they know what “maximum” means. They are unconstrained by anything.
In the actual, uncertain world, with limited information and knowledge, only constrained maximization is possible. All firms, instead of being profit-maximizers (not possible in a world of uncertainty), are rent-seekers, responding to instituted constraints: the institutional rules of the game, so to speak. Economic rents are what they have to lose in this game, and protecting those rents, orients their behavior within the institutional constraints…..
In most of our economic interactions, price is not a variable optimally digesting information and resolving conflict, it is a strategic instrument, held fixed as part of a scheme of administrative control and information discovery……The actual, decentralized “market” economy is not coordinated primarily by market prices—it is coordinated by rules. The dominant relationships among actors is not one of market exchange at price, but of contract: implicit or explicit, incomplete and contingent.

James Beniger’s work is the definitive document on how the essence of the ‘control revolution’ has been an attempt to take economic activity out of the sphere of direct influence of the market. But that is not all – the long process of algorithmisation over the last 150 years has also, wherever possible, replaced implicit rules/contracts and principal-agent relationships with explicit processes and rules. Beniger also notes that after a certain point, the increasing complexity of the system is an endogenous phenomenon i.e. further iterations are aimed at controlling the control process itself. As I have illustrated above, after a certain threshold, the increasing complexity, fragility and deterioration in performance becomes a self-fulfilling positive feedback process.

Although our current system bears very little resemblance to the market economy of the textbook, there was a brief period during the transition from the traditional economy to the control economy during the early part of the 19th century when this was the case. 26% of all imports into the United States in 1827 sold in an auction. But the displacement of traditional controls (familial ties) with the invisible hand of market controls was merely a transitional phase, soon to be displaced by the visible hand of the control revolution.

The Soviet Project, Western Capitalism and High Modernity

Communism and Capitalism are both pillars of the high-modernist control project. The signature of modernity is not markets, but technocratic control projects. Capitalism has simply done it in a manner that is more easily and more regularly subverted. It is the occasional failure of the control revolution that is the source of the capitalist economy’s long-run success. Conversely, the failure of the Soviet Project was due to its too successful adherence and implementation of the high-modernist ideal. The significance of the threat from crony capitalism is a function of the fact that by forming a coalition and partnership of the corporate and state control projects, it enables the implementation of the control revolution to be that much more effective.

The Hayekian argument of dispersed knowledge and its importance in seeking equilibrium is not as important as it seems in explaining why the Soviet project failed. As Joseph Berliner has illustrated, the Soviet economy did not fail to reach local equilibria. Where it failed so spectacularly was in extracting itself out of these equilibria. The dispersed knowledge argument is open to the riposte that better implementation of the control revolution will eventually overcome these problems – indeed much of the current techno-utopian version of the control revolution is based on this assumption. It is a weak argument for free enterprise, a much stronger argument for which is the need to maintain a system that retains the ability to reinvent itself and find a new, hitherto unknown trajectory via the destruction of the incumbents combined with the emergence of the new. Where the Soviet experiment failed is that it eliminated the possibility of failure, that Berliner called the ‘invisible foot’. The success of the free enterprise system has been built not upon the positive incentive of the invisible hand but the negative incentive of the invisible foot to counter the visible hand of the control revolution. It is this threat and occasional realisation of failure and disorder that is the key to maintaining system resilience and evolvability.

Notes:

• Borrowing from Beniger, control here simply means “purposive influence towards a predetermined goal”. Similarly, equilibrium in this context is best defined as a state in which economic agents are not forced to change their routines, theories and policies.
• On the uncanny valley, I wrote a similar post on why perfect memory does not lead to perfect human intelligence. Even if a computer benefits from more data and better memory, we may not. And the evidence suggests that the deterioration in human performance is steepest in the zone close to “perfection”.
• An argument similar to my assertion on the misconception of a free enterprise economy as a market economy can be made about the nature of democracy. Rather than as a vehicle that enables the regular expression of the political will of the electorate, democracy may be more accurately thought of as the ability to effect a dramatic change when the incumbent system of plutocratic/technocratic rule diverges too much from popular opinion. As always, stability and prevention of disturbances can cause the eventual collapse to be more catastrophic than it needs to be.
• Although James Beniger’s ‘Control Revolution’ is the definitive reference, Antoine Bousquet’s book ‘The Scientific Way of Warfare’ on the similar revolution in military warfare is equally good. Bousquet’s book highlights the fact that the military is often the pioneer of the key projects of the control revolution and it also highlights just how similar the latest phase of this evolution is to early phases – the common desire for control combined with its constant subversion by reality. Most commentators assume that the threat to the project is external – by constantly evolving guerrilla warfare for example. But the analysis of the uncanny valley suggests that an equally great threat is endogenous – of increasing complexity and illegibility of the control project itself. Bousquet also explains how the control revolution is a child of the modern era and the culmination of the philosophy of the Enlightenment.
• Much of the “innovation” of the control revolution was not technological but institutional – limited liability, macroeconomic stabilisation via central banks etc.
• For more on the role of degeneracy in biological systems and how it enables near-optimal resilience, this paper by James Whitacre and Axel Bender is excellent.

It is easy to find proximate causes for this dynamic and commentators on both sides of the political spectrum attribute much of the above to the neo-liberal revolution – the doctrine of shareholder value maximisation, high-powered managerial incentives, a drive towards increased efficiency etc. The acceleration of this process in the last decade usually gets explained away as the inevitable consequence of a financial bubble with irrationally exuberant banks making unwise loans to fuel the leverage binge. But these narratives miss the obvious elephant in the room – the role of monetary policy and in particular the dominant monetary policy doctrine underpinning the ‘Great Moderation’ which focused on shoring up financial asset prices as the primary channel of monetary stimulus, otherwise known as the ‘Greenspan Put’. All the above proximate causes were the direct and inevitable result of economic actors seeking to align themselves to the central banks’ focus on asset price stabilisation.

As I elaborated upon in an earlier post:

creating any source of stability in a capitalist economy incentivises economic agents to realign themselves to exploit that source of security and thereby reduce risk. Similar to how banks’ adaptation to the intervention strategies preferred by central banks by taking on more “macro” risks, macro-stabilisation incentivises real economy firms to shed idiosyncratic micro-risks and take on financial risks instead. Suppressing nominal volatility encourages economic agents to shed real risks and take on nominal risks. In the presence of the Greenspan/Bernanke put, a strategy focused on “macro” asset price risks and leverage outcompetes strategies focused on “risky” innovation. Just as banks that exploit the guarantees offered by central banks outcompete those that don’t, real economy firms that realign themselves to become more bank-like outcompete those that choose not to…….When central bankers are focused on preventing significant pullbacks in equity prices (the Greenspan/Bernanke put), then real-economy firms are incentivised to take on more systematic risk and reduce their idiosyncratic risk exposure.

The focus on cost reduction and layoffs is also a result of this increased market-sensitivity combined with the macro-stabilisation commitment encourages low-risk process innovation and discourages uncertain and exploratory product innovation. The excesses of some forms of private equity are often instances in which they apply the maximum possible leverage to extract the rents available via the Greenspan Put. Dividend recaps are one such instance.

James Kwak summarises the case of Simmons Bedding Company:

In 2003, for example, THL bought Simmons (the mattress company) for $327 million in cash and $745 million in debt. In 2004, Simmons (now run by THL) issued more debt and paid a $137 million dividend to THL; in 2007, it issued yet more debt and paid a $238 million dividend to THL. Simmons filed for bankruptcy in 2009.

The obvious question here is why banks and financial institutions would lend so much money and allow firms to lever up so dramatically. Kwak lays the blame on the financial bubble, principal-agent problems, bankers bonus structures etc. TED counters that lenders do in fact typically make informed decisions and also correctly points out that the rest of corporate America is not immune to such leveraged mishaps either. Both explanations ignore the fact that this sort of severely tail-risk heavy loan is exactly the payoff which maximises the banks‘ and their employees’ own moral hazard rent extraction. In an earlier post, I identified that many hedge fund strategies are an indirect beneficiary of moral hazard rents – the same argument also applies to some private equity strategies.

But as I have noted on many occasions, the moral hazard problem from tail-risk hungry TBTF financial institutions is simply the tip of the iceberg. It was not only the banks with access to cheap leverage that were heavily invested in “safe” assets, but also asset managers, money market mutual funds and even ordinary investors. The Greenspan/Bernanke Put incentivises a large proportion of real and financial actors in the economy into taking on more and more tail risk with the expectation that the Fed will avoid any outcomes where these risks will be realised.

Too many commentators fail to recognise that so much of what has made the neo-liberal era a thinly disguised corporate welfare state can be traced to the impact of a supposedly “neutral” macroeconomic policy instrument that in reality has grossly regressive consequences. To expect corporate America to not take advantage of the free lunch offered to it by the Fed is akin to dangling a piece of meat in front of a tiger and expecting it not to bite your hand off.

governments should fully guarantee all bank deposits — and impose much tighter restrictions on risk-taking by banks. Banks should be forced to shed activities like derivatives trading that regulators cannot easily examine…..Banks must therefore be restricted to those activities, like making traditional loans and simple hedging operations, that a regulator of average education and intelligence can monitor.

There are a couple of problems with his idea – for one it may not be possible to effectively regulate bank risk-taking. On many previous occasions, I have asserted that regulations cannot restrain banks from extracting moral hazard rents from the guarantee provided by the state/central bank to bank creditors and depositors. The primary reason for this is the spread of financial innovation during the last fifty years that has given banks an almost infinite variety of ways in which it can construct an opaque and precisely tailored payoff that provides a steady stream of profits in good times in exchange for a catastrophic loss in bad times. As I have shown, the moral hazard trade is not a “riskier” trade but a combination of high leverage and a severely negatively skewed payoff with a catastrophic tail risk.

Minsky himself understood the essentially ephemeral nature of the financial system of the 50s from his work on the early stages of the process of financial innovation that allowed the financial system to unshackle itself from the effective control of the central bank and the regulator. As he observes:

The banking system came out of the war with a portfolio heavily weighted with government debt, and it was not until the 1960s that banks began to speculate actively with respect to their liabilities. It was a unique period in which finance mattered relatively little; at least, finance did not interpose its destabilizing ways……The apparent stability and robustness of the financial system of the 1950s and early 1960s can now be viewed as an accident of history, which was due to the financial residue of World War 2 following fast upon a great depression.

Amar Bhide’s idea essentially seeks to turn back the clock and forbid much of the innovation that has taken place in the last few decades. In particular, derivatives businesses will be forbidden for deposit-taking banks. This is a radical idea and one that is a significant improvement on the current status quo. But it is not enough to mitigate the moral hazard problem. To illustrate why this is the case, let me take an example of how as a banker, I would construct such a payoff within a “narrow banking”-like mandate. Let us assume that banks can only take deposits and make loans to corporations and households. They cannot hedge their loans or engage in any activities related to financial market positions even as market makers, and they cannot carry any off balance-sheet exposures, commitments etc. Although this would seem to be a sufficiently narrow mandate to prevent rent extraction, it is not. Banks can simply lend to other firms that take on negatively skewed bets. You may counter that banks should only be allowed to lend to real economy firms. But do we expect regulators to audit not only the banks under their watch but also the firms to whom they lend money? In the first post on this blog, I outlined how the synthetic super-senior CDO tranche was the quintessential rent-extraction product of the derivatives revolution. But at its core, the super-senior tranche is simply a severely negatively skewed bond – a product that pays a small positive spread in good times and loses you all your money in bad times. There is no shortage of ways in which such a negatively skewed payoff can be constructed by simple structured bank loans.

What the synthetic OTC derivatives revolution made possible was for the banking system to structure such payoffs in an essentially infinite amount without even going through the trouble of making new loans or mortgages – all that was needed was a derivatives counterparty. Without derivatives, banks would have to lend money to generate such a payoff – this only makes it a little harder to extract rents but it still does not change the essence of the problem. Even more crucially, the potential for such rent extraction is unlimited compared to other avenues for extracting rent. If the state pays a higher price for an agricultural crop compared to the market, at least the losses suffered by the taxpayer are limited by physical constraints such as arable land available. But when the rent extraction opportunity goes hand in hand with the very process that creates credit and broad money, the potential for rent extraction is virtually unlimited.

Even if we assume that rent extraction can be controlled by more stringent regulations, there remains one problem. There is simply no way that incumbent large banks, especially those with a large OTC derivatives franchise, can shed their derivatives business and still remain solvent. The best indication of how hard it is to unwind complex derivatives portfolios was the experience of Warren Buffett in unwinding the derivatives portfolio which he inherited from the General Re acquisition. As Buffett notes, unwinding the portfolio of a relatively minor player in the derivative market under benign market conditions and no internal financial pressure took years and cost him $404 million. If we asked any of the large banks, let alone all of them at once, to do the same in the current fragile market conditions the cost of doing so will comfortably bankrupt the entire banking sector. The modern TBTF bank with its huge OTC derivatives business is akin to a suicide bomber with his finger on the button that is holding us hostage – this is the reason why regulators handle them with kid gloves.

In other words, even if our dream of limited and safe banking is viable we have a ‘can’t get there from here’ problem. This does not mean that there are no viable solutions but we need to be more creative. Amar Bhide makes a valid point when he argues that “Why not also make all short-term deposits, which function much like currency, the explicit liability of the government?” But the solution is not to allow private banks to reap the rents from cheap deposit financing but to allow each citizen and corporation access to a public deposit account. The simplest implementation of this would be a system similar to the postal savings system where all deposits are necessarily backed by short-term treasury bills. If the current stock of T-bills is not sufficient to back the demand for such deposits, the Treasury should shift the maturity profile of its debt until the demand is met. In such a system, there would be no deposit insurance i.e. all investment/deposit alternatives except for the state system will be explicitly risky and unprotected.

One criticism of such a system would be that the benefits of maturity transformation would be lost to the economy i.e. unless short-term deposits are deployed to match long-term investment projects, such projects would not find adequate funding. But as I have argued and the data shows, household long-term savings (which includes pensions and life insurance) is more than sufficient to meet the long-term borrowing needs of the corporate and the household sector in both the United States and Europe.

The “regulate and insure” model ignores the ability of banks to arbitrage any regulatory framework. But the status quo is also unacceptable. However the system is sufficiently levered and fragile that allowing market forces to operate or simply forcing a drastic structural change upon incumbent banks by regulatory fiat implies an almost certain collapse of the incumbent banks. Creating a public deposit option is the first step in implementing a sustainable transition to a resilient financial system, one in which instead of shackling incumbent banks we separate them from the risk-free depository system.

Note: My views on this topic and some other related topics which I hope to explore soon have been significantly influenced by uber-commenter K. For a taste of his broader ideas which are similar to mine, try this comment which he made in response to a Nick Rowe post.

This debate bears an uncanny resemblance to a similar debate as to the causes of the financial crisis – many commentators blame the persistently irrational nature of human judgement for the recurrence of financial crises. Others such as Amar Bhide blame the unwise deference to imperfect financial models over human judgement. In my opinion, both perspectives miss the true dynamic. These disasters are not driven by human error or systems error alone but by fatal flaws in the interaction between human intelligence and complex, near fully-automated systems.

In a recent article drawing upon the black box transcripts, Jeff Wise attributes the crash primarily to a “simple but persistent mistake on the part of one of the pilots”. According to Wise, the co-pilot reacted to the persistent stall warning by “pulling back on the stick, the exact opposite of what he must do to recover from the stall”.

But there are many hints that the story is nowhere near as simple. As Peter Garrison notes :

every pilot knows that to recover from a stall you must get the nose down. But because a fully developed stall in a large transport is considered highly unlikely, and because in IFR air traffic vertical separation, and therefore control of altitude, is important, transport pilots have not been trained to put the nose down when they hear the stall warning — which heralds, after all, not a fully developed stall, but merely an approaching one. Instead, they have been trained to increase power and to “fly out of the stall” without losing altitude. Perhaps that is what the pilot flying AF447 intended. But the airplane was already too deeply stalled, and at too high an altitude, to recover with power alone.

The patterns of the AF447 disaster are not unique. As Chris Sorensen observes, over 50 commercial aircrafts have crashed in “loss-of-control” accidents in the last five years, a trend for which there is no shortage of explanations:

Some argue that the sheer complexity of modern flight systems, though designed to improve safety and reliability, can overwhelm even the most experienced pilots when something actually goes wrong. Others say an increasing reliance on automated flight may be dulling pilots’ sense of flying a plane, leaving them ill-equipped to take over in an emergency. Still others question whether pilot-training programs have lagged behind the industry’s rapid technological advances.

But simply invoking terms such as “automation addiction” or blaming disasters on irrational behaviour during times of intense stress does not get at the crux of the issue.

People Make Poor Monitors for Computers

Airplane automation systems are not the first to discover the truth in the comment made by David Jenkins that “computers make great monitors for people, but people make poor monitors for computers.” As James Reason observes in his seminal book ‘Human Error’:

We have thus traced a progression from where the human is the prime mover and the computer the slave to one in which the roles are very largely reversed. For most of the time, the operator’s task is reduced to that of monitoring the system to ensure that it continues to function within normal limits. The advantages of such a system are obvious; the operator’s workload is substantially reduced, and the [system] performs tasks that the human can specify but cannot actually do. However, the main reason for the human operator’s continued presence is to use his still unique powers of knowledge-based reasoning to cope with system emergencies. And this is a task peculiarly ill-suited to the particular strengths and weaknesses of human cognition…..

most operator errors arise from a mismatch between the properties of the system as a whole and the characteristics of human information processing. System designers have unwittingly created a work situation in which many of the normally adaptive characteristics of human cognition (its natural heuristics and biases) are transformed into dangerous liabilities.

As Jeff Wise notes, it is impossible to stall an Airbus in most conditions. AF447 however went into a state known as ‘alternate law’ which most pilots have never experienced where the airplane could be stalled:

“You can’t stall the airplane in normal law,” says Godfrey Camilleri, a flight instructor who teaches Airbus 330 systems to US Airways pilots….But once the computer lost its airspeed data, it disconnected the autopilot and switched from normal law to “alternate law,” a regime with far fewer restrictions on what a pilot can do. “Once you’re in alternate law, you can stall the airplane,” Camilleri says….It’s quite possible that Bonin had never flown an airplane in alternate law, or understood its lack of restrictions. According to Camilleri, not one of US Airway’s 17 Airbus 330s has ever been in alternate law. Therefore, Bonin may have assumed that the stall warning was spurious because he didn’t realize that the plane could remove its own restrictions against stalling and, indeed, had done so.

This inability of the human operator to fill in the gaps in a near-fully automated system was identified by Lisanne Bainbridge as one of the ironies of automation which James Reason summarised:

the same designer who seeks to eliminate human beings still leaves the operator “to do the tasks which the designer cannot think how to automate” (Bainbridge,1987, p.272). In an automated plant, operators are required to monitor that the automatic system is functioning properly. But it is well known that even highly motivated operators cannot maintain effective vigilance for anything more than quite short periods; thus, they are demonstrably ill-suited to carry out this residual task of monitoring for rare, abnormal events. In order to aid them, designers need to provide automatic alarm signals. But who decides when these automatic alarms have failed or been switched off?

As Robert Charette notes, the same is true for airplane automation:

operators are increasingly left out of the loop, at least until something unexpected happens. Then the operators need to get involved quickly and flawlessly, says Raja Parasuraman, professor of psychology at George Mason University in Fairfax, Va., who has been studying the issue of increasingly reliable automation and how that affects human performance, and therefore overall system performance. ”There will always be a set of circumstances that was not expected, that the automation either was not designed to handle or other things that just cannot be predicted,” explains Parasuraman. So as system reliability approaches—but doesn’t quite reach—100 percent, ”the more difficult it is to detect the error and recover from it,” he says…..In many ways, operators are being asked to be omniscient systems administrators who are able to jump into the middle of a situation that a complex automated system can’t or wasn’t designed to handle, quickly diagnose the problem, and then find a satisfactory and safe solution.

Stored Routines Are Not Effective in Rare Situations

As James Reason puts it:

the main reason why humans are retained in systems that are primarily controlled by intelligent computers is to handle ‘non-design’ emergencies. In short, operators are there because system designers cannot foresee all possible scenarios of failure and hence are not able to provide automatic safety devices for every contingency. In addition to their cosmetic value, human beings owe their inclusion in hazardous systems to their unique, knowledge-based ability to carry out ‘on-line’ problem solving in novel situations. Ironically, and notwithstanding the Apollo 13 astronauts and others demonstrating inspired improvisation, they are not especially good at it; at least not in the conditions that usually prevail during systems emergencies. One reason for this is that stressed human beings are strongly disposed to employ the effortless, parallel, preprogrammed operations of highly specialised, low-level processors and their associated heuristics. These stored routines are shaped by personal history and reflect the recurring patterns of past experience……

Why do we have operators in complex systems? To cope with emergencies. What will they actually use to deal with these problems? Stored routines based on previous interactions with a specific environment. What, for the most part, is their experience within the control room? Monitoring and occasionally tweaking the plant while it performs within safe operating limits. So how can they perform adequately when they are called upon to reenter the control loop? The evidence is that this task has become so alien and the system so complex that, on a significant number of occasions, they perform badly.

Wise again identifies this problem in the case of AF447:

While Bonin’s behavior is irrational, it is not inexplicable. Intense psychological stress tends to shut down the part of the brain responsible for innovative, creative thought. Instead, we tend to revert to the familiar and the well-rehearsed. Though pilots are required to practice hand-flying their aircraft during all phases of flight as part of recurrent training, in their daily routine they do most of their hand-flying at low altitude—while taking off, landing, and maneuvering. It’s not surprising, then, that amid the frightening disorientation of the thunderstorm, Bonin reverted to flying the plane as if it had been close to the ground, even though this response was totally ill-suited to the situation.

Deskilling From Automation

As James Reason observes:

Manual control is a highly skilled activity, and skills need to be practised continuously in order to maintain them. Yet an automatic control system that fails only rarely denies operators the opportunity for practising these basic control skills. One of the consequences of automation, therefore, is that operators become de-skilled in precisely those activities that justify their marginalised existence. But when manual takeover is necessary something has usually gone wrong; this means that operators need to be more rather than less skilled in order to cope with these atypical conditions. Duncan (1987, p. 266) makes the same point: “The more reliable the plant, the less opportunity there will be for the operator to practise direct intervention, and the more difficult will be the demands of the remaining tasks requiring operator intervention.”

Opacity and Too Much Information of Uncertain Reliability

Wise captures this problem and its interaction with a human who has very little experience in managing the crisis scenario:

Over the decades, airliners have been built with increasingly automated flight-control functions. These have the potential to remove a great deal of uncertainty and danger from aviation. But they also remove important information from the attention of the flight crew. While the airplane’s avionics track crucial parameters such as location, speed, and heading, the human beings can pay attention to something else. But when trouble suddenly springs up and the computer decides that it can no longer cope—on a dark night, perhaps, in turbulence, far from land—the humans might find themselves with a very incomplete notion of what’s going on. They’ll wonder: What instruments are reliable, and which can’t be trusted? What’s the most pressing threat? What’s going on? Unfortunately, the vast majority of pilots will have little experience in finding the answers.

A similar scenario occurred in the case of the Qantas-owned A380 which took off from Singapore in November 2010:

Shortly after takeoff from Singapore, one of the hulking A380’s four engines exploded and sent pieces of the engine cowling raining down on an Indonesian island. The blast also damaged several of the A380’s key systems, causing the unsuspecting flight crew to be bombarded with no less than 54 different warnings and error messages—so many that co-pilot Matt Hicks later said that, at one point, he held his thumb over a button that muted the cascade of audible alarms, which threatened to distract Capt. Richard De Crespigny and the rest of the feverishly working flight crew. Luckily for passengers, Qantas Flight 32 had an extra two pilots in the cockpit as part of a training exercise, all of whom pitched in to complete the nearly 60 checklists required to troubleshoot the various systems. The wounded plane limped back to Singapore Changi Airport, where it made an emergency landing.

Again James Reason captures the essence of the problem:

One of the consequences of the developments outlined above is that complex, tightly-coupled and highly defended systems have become increasingly opaque to the people who manage, maintain and operate them. This opacity has two aspects: not knowing what is happening and not understanding what the system can do. As we have seen, automation has wrought a fundamental change in the roles people play within certain high-risk technologies. Instead of having ‘hands on’ contact with the process, people have been promoted “to higher-level supervisory tasks and to long-term maintenance and planning tasks” (Rasmussen, 1988). In all cases, these are far removed from the immediate processing. What direct information they have is filtered through the computer-based interface. And, as many accidents have demonstrated, they often cannot find what they need to know while, at the same time, being deluged with information they do not want nor know how to interpret.

Absence of Intuitive Feedback

Among others, Hubert and Stuart Dreyfus have shown that human expertise relies on an intuitive and tacit understanding of the situation rather than a rule-bound and algorithmic understanding. The development of intuitive expertise depends upon the availability of clear and intuitive feedback which complex, automated systems are often unable to provide.

In AF447, when the co-pilot did push forward on the stick (the “correct” response), the behaviour of the stall warning was exactly the opposite of what he would have intuitively expected:

At one point the pilot briefly pushed the stick forward. Then, in a grotesque miscue unforeseen by the designers of the fly-by-wire software, the stall warning, which had been silenced, as designed, by very low indicated airspeed, came to life. The pilot, probably inferring that whatever he had just done must have been wrong, returned the stick to its climb position and kept it there for the remainder of the flight.

Absence of feedback prevents effective learning but the wrong feedback can have catastrophic consequences.

The Fallacy of Defence in Depth

In complex automated systems, the redundancies and safeguards built into the system also contribute to its opacity. By protecting system performance against single faults, redundancies allow the latent buildup of multiple faults. Jens Rasmussen called this ‘the fallacy of defence in depth’ which James Reason elaborates upon:

the system very often does not respond actively to single faults. Consequently, many errors and faults made by the staff and maintenance personnel do not directly reveal themselves by functional response from the system. Humans can operate with an extremely high level of reliability in a dynamic environment when slips and mistakes have immediately visible effects and can be corrected……Violation of safety preconditions during work on the system will probably not result in an immediate functional response, and latent effects of erroneous acts can therefore be left in the system. When such errors are allowed to be present in a system over a longer period of time, the probability of coincidence of the multiple faults necessary for release of an accident is drastically increased. Analyses of major accidents typically show that the basic safety of the system has eroded due to latent errors.

This is exactly what occurred on Malaysia Airlines Flight 124 in August 2005:

The fault-tolerant ADIRU was designed to operate with a failed accelerometer (it has six). The redundant design of the ADIRU also meant that it wasn’t mandatory to replace the unit when an accelerometer failed. However, when the second accelerometer failed, a latent software anomaly allowed inputs from the first faulty accelerometer to be used, resulting in the erroneous feed of acceleration information into the flight control systems. The anomaly, which lay hidden for a decade, wasn’t found in testing because the ADIRU’s designers had never considered that such an event might occur.

Again, defence-in-depth systems are uniquely unsuited to human expertise as Gary Klein notes:

In a massively defended system, if an accident sneaks through all the defenses, the operators will find it far more difficult to diagnose and correct it. That is because they must deal with all of the defenses, along with the accident itself…..A unit designed to reduce small errors helped to create a large one.

Two Approaches to Airplane Automation: Airbus and Boeing

Although both Airbus and Boeing have adopted the fly-by-wire technology, there are fundamental differences in their respective approaches. Whereas Boeing’s system enforces soft limits that can be overridden at the discretion of the pilot, Airbus’ fly-by-wire system has built-in hard limits that cannot be overridden completely at the pilot’s discretion.

As Simon Calder notes, pilots have raised concerns in the past about Airbus‘ systems being “overly sophisticated” as opposed to Boeing’s “rudimentary but robust” system. But this does not imply that the Airbus approach is inferior. It is instructive to analyse Airbus’ response to pilot demands for a manual override switch that allows the pilot to take complete control:

If we have a button, then the pilot has to be trained on how to use the button, and there are no supporting data on which to base procedures or training…..The hard control limits in the Airbus design provide a consistent “feel” for the aircraft, from the 120-passenger A319 to the 350-passenger A340. That consistency itself builds proficiency and confidence……You don’t need engineering test pilot skills to fly this airplane.

David Evans captures the essence of this philosophy as aimed at minimising the “potential for human error, to keep average pilots within the limits of their average training and skills”.

It is easy to criticise Airbus‘ approach but the hard constraints clearly demand less from the pilot. In the hands of an expert pilot, Boeing’s system may outperform. But if the pilot is a novice, Airbus’ system almost certainly delivers superior results. Moreover, as I discussed earlier in the post, the transition to an almost fully automated system by itself reduces the probability that the human operator can achieve intuitive expertise. In other words, the transition to near-autonomous systems creates a pool of human operators that appear to frequently commit “irrational” errors and is therefore almost impossible to reverse.

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People Make Poor Monitors for Some Financial Models

In earlier post, I analysed Amar Bhide’s argument that a significant causal agent in the financial crisis was the replacement of discretion with models in many areas of finance – for example, banks’ mortgage lending decisions. In his excellent book, ‘A Call for Judgement’, he expands on this argument and amongst other technologies, lays some of the blame for this over-mechanisation of finance on the ubiquitous Black-Scholes-Merton (BSM) formula. Although I agree with much of his book, this thesis is too simplistic.

There is no doubt that BSM has many limitations – amongst the most severe being the assumption of continuous asset price movements, a known and flat volatility surface, and an asset price distribution free of fat tails. But the systemic impact of all these limitations is grossly overstated:

• BSM and similar models have never been used as “valuation” methods on a large scale in derivatives markets but as a tool which tries to back out an implied volatility and generate useful hedge ratios by taking market prices for options as a given. In other words, volatility plays the role of the “wrong number in the wrong formula to get the right price”.
• When “simple” BSM-like models are used to price more exotic derivatives, they have a modest role to play. As Emanuel Derman puts it, practitioners use models as “interpolating formulas that take you from known prices of liquid securities to the unknown values of illiquid securities”.

Nevertheless, this does not imply that financial modelling choices have no role to play in determining system resilience. But the role was more subtle and had to do less with the imperfections of the models themselves as with the imperfections of how complex models used to price complex products could be used by human traders.

Since the discovery of the volatility smile, traders have known that the interpolation process to price exotic options requires something more than a simple BSM model. One would assume that traders would want to use a model that was accurate and comprehensive as possible. But this has rarely been the case. Supposedly inferior local volatility models still flourish and even in some of the most complex domains of exotic derivatives, models are still chosen based on their intuitive similarities to a BSM-like approach where the free parameters can be thought of as volatility or correlation e.g. The Libor Market Model.

The choice of intuitive understanding over model accuracy is not unwarranted. As all market practitioners know, there is no such thing as a perfect derivatives pricing model. Paul Wilmott hit the nail on the head when he observed that *“the many improvements on Black-Scholes are rarely improvements, the best that can be said for many of them is that they are just better at hiding their faults. Black-Scholes also has its faults, but at least you can see them”.

However, as markets have evolved, maintaining this balance between intuitive understanding and accuracy has become increasingly difficult:

• Intuitive yet imperfect models require experienced and expert traders. Scaling up trading volumes of exotic derivatives however requires that pricing and trading systems be pushed out to novice traders as well as non-specialists such as salespeople.
• With the increased complexity of derivative products, preserving an intuitive yet sufficiently accurate model becomes an almost impossible task.
• Product complexity combined with the inevitable discretion available to traders when they use simpler models presents significant control challenges and an increased potential for fraud.

In this manner, the same paradoxical evolution that have been observed in nuclear plants and airplane automation is now being experienced in finance. The need to scale up and accommodate complex products necessitates the introduction of complex, unintuitive models in combination with which human intuitive expertise is unable to add any value. In such a system, a novice is often as good as a more experienced operator. The ability of these models to tackle most scenarios on ‘auto-pilot’ results in a deskilled and novice-heavy human component in the system which is ill-equipped to tackle the inevitable occasion when the model fails. The failure is inevitably taken as evidence of human failure upon which the system is made even more automated and more safeguards and redundancies are built into the system. This exacerbates the problem of absence of feedback when small errors occur. The buildup of latent errors again increases and failures become even more catastrophic.

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My focus on airplane automation and financial models is simply illustrative. There are ample signs of this incompatibility between human monitors and near-fully automated systems in other domains as well. For example, Andrew Hill observes:

In developed economies, Lynda Gratton writes in her new book The Shift, “when the tasks are more complex and require innovation or problem solving, substitution [by machines or computers] has not taken place”. This creates a paradox: far from making manufacturers easier to manage, automation can make managers’ jobs more complicated. As companies assign more tasks to machines, they need people who are better at overseeing the more sophisticated workforce and doing the jobs that machines cannot….

The insight that greater process efficiency adds to the pressure on managers is not new. Even Frederick Winslow Taylor – these days more often caricatured as a dinosaur for his time-and-motion studies – pointed out in his century-old The Principles of Scientific Management that imposing a more mechanistic regime on workers would oblige managers to take on “other types of duties which involve new and heavy burdens”…..

There is no doubt Foxconn and its peers will be able to automate their labour-intensive processes. They are already doing so. The big question is how easily they will find and develop managers able to oversee the highly skilled workforce that will march with their robot armies.

This process of integrating human intelligence with artificial intelligence is simply a continuation of the process through which human beings went from being tool-users to minders and managers of automated systems. The current transition is important in that for the first time, many of these algorithmic and automated systems can essentially run themselves with human beings performing the role of supervisors who only need to intervene in extraordinary circumstances. Although it seems logical that the same process of increased productivity that has occurred during the modern ‘Control Revolution’ will continue during the creation of the “vast,automatic and invisible” ‘second economy’, the incompability of human cognition with near-fully automated systems suggests that it may only do so by taking on an increased risk of rare but catastrophic failure.

Drawing upon the work of Mancur Olson, I have explored how the buildup of special interests means that stability is self-reinforcing. Once rent-seeking has achieved sufficient scale, “distributional coalitions have the incentive and..the power to prevent changes that would deprive them of their enlarged share of the social output”. But what if we “solve” the Olsonian problem? Would that mitigate the problem of increased stabilisation and fragility? In this post, I will argue that the cycle of fragility and collapse has much deeper roots than any particular form of democracy.

In this analysis, I am going to move away from ecological analogies and instead turn to an example from modern medicine. In particular, I am going to compare the experience and history of psychiatric medication in the second half of the twentieth century to some of the issues we have already looked at in macroeconomic and ecological stabilisation. I hope to convince you that the uncanny similarities in the patterns observed in stabilised systems across such diverse domains are not a coincidence. In fact, the human body provides us with a much closer parallel to economic systems than even ecological systems with respect to the final stages of stabilisation. Most ecological systems collapse sooner simply because the limits to which resources will be spent in an escalating fashion to preserve stability are much smaller. For example, there are limits to the resources that will be deployed to prevent a forest fire, no matter how catastrophic. On the other hand, the resources that will be deployed to prevent collapse of any system that is integral to human beings are much larger.

Even by the standards of this blog, this will be a controversial article. In my discussion of psychiatric medicine I am relying primarily on Robert Whitaker’s excellent but controversial and much-disputed book ‘Anatomy of an Epidemic’. Nevertheless, I want to emphasise that my ultimate conclusions are much less incendiary than those of Whitaker. In the same way that I want to move beyond an explanation of the economic crisis that relies on evil bankers, crony capitalists and self-interested technocrats, I am trying to move beyond an explanation that blames evil pharma and misguided doctors for the crisis in mental health. I am not trying to imply that fraud and rent-seeking does not have a role to play. I am arguing that even if we eliminate them, the aim of a resilient economic and social system would not be realised.

THE PUZZLE

The puzzle of the history of macroeconomic stabilisation post-WW2 can be summarised as follows. Clearly every separate event of macroeconomic stabilisation works. Most monetary and fiscal interventions result in a rise in the financial markets, NGDP expectations and economic performance in the short run. Yet,

• we are in the middle of a ‘great stagnation’ and have been for a few decades.
• the frequency of crises seems to have risen dramatically in the last fifty years culminating in the environment since 2008 which is best described as a perpetual crisis.
• each recovery seems to be weaker than the previous one and requires an increased injection of stimulus to achieve results that were easily achieved by a simple rate cut not that long ago.

Similarly, the history of mental health post-WW2 too has been a puzzle and is summarised by Whitaker as follows:

The puzzle can now be precisely summed up. On the one hand, we know that many people are helped by psychiatric medications. We know that many people stabilize well on them and will personally attest to how the drugs have helped them lead normal lives. Furthermore, as Satcher noted in his 1999 report, the scientific literature does document that psychiatric medications, at least over the short term, are “effective.” Psychiatrists and other physicians who prescribe the drugs will attest to that fact, and many parents of children taking psychiatric drugs will swear by the drugs as well. All of that makes for a powerful consensus: Psychiatric drugs work and help people lead relatively normal lives. And yet, at the same time, we are stuck with these disturbing facts: The number of disabled mentally ill has risen dramatically since 1955, and during the past two decades, a period when the prescribing of psychiatric medications has exploded, the number of adults and children disabled by mental illness has risen at a mind-boggling rate.

Whitaker then asks the obvious but heretical question – “Could our drug-based paradigm of care, in some unforeseen way, be fueling this modern-day plague?” and answers the question in the affirmative. But what are the precise mechanisms and patterns that underlie this deterioration?

Adaptive Response to Intervention and Drug Dependence

The fundamental reason why interventions fail in complex adaptive systems is the adaptive response triggered by the intervention that subverts the aim of the intervention. Moreover once the system is artificially stabilised and system agents have adapted to this new stability, the system cannot cope with any abrupt withdrawal of the stabilising force. For example, Whitaker notes that

Neuroleptics put a brake on dopamine transmission, and in response the brain puts down the dopamine accelerator (the extra D2 receptors). f the drug is abruptly withdrawn, the brake on dopamine is suddenly released while the accelerator is still pressed to the floor. The system is now wildly out of balance, and just as a car might careen out of control, so too the dopaminergic pathways in the brain……In short, initial exposure to neuroleptics put patients onto a path where they would likely need the drugs for life.

Whitaker makes the same observation for benzodiazepines and antidepressants:

benzodiazepines….work by perturbing a neurotransmitter system, and in response, the brain undergoes compensatory adaptations, and as a result of this change, the person becomes vulnerable to relapse upon drug withdrawal. That difficulty in turn may lead some to take the drugs indefinitely.

(antidepressants) perturb neurotransmitter systems in the brain. This leads to compensatory processes that oppose the initial acute effects of a drug…. When drug treatment ends, these processes may operate unopposed, resulting in appearance of withdrawal symptoms and increased vulnerability to relapse.

Similarly, when a central bank protects incumbent banks against liquidity risk, the banks choose to hold progressively more illiquid portfolios. When central banks provide incumbent banks with cheap funding in times of crisis to prevent failure and creditor losses, the banks choose to take on more leverage. This is similar to what John Adams has termed the ‘risk thermostat’ – the system readjusts to get back to its preferred risk profile. The protection once provided is almost impossible to withdraw without causing systemic havoc as agents adapt to the new stabilised reality and lose the ability to survive in an unstabilised environment.

Of course, in economic systems when agents actively intend to arbitrage such commitments by central banks, it is simply a form of moral hazard. But such an adaptation can easily occur via the natural selective forces at work in an economy – those who fail to take advantage of the Greenspan/Bernanke put simply go bust or get fired. In our brain the adaptation simply reflects homeostatic mechanisms selected for by the process of evolution.

Transformation into a Pathological State, Loss of Core Functionality and Deterioration of the Baseline State

I have argued in many posts that the successive cycles of Minskyian stabilisation have a role to play in the deterioration in the structural performance of the real economy which has manifested itself as ‘The Great Stagnation’. The same conclusion holds for many other complex adaptive systems and our brain is no different. Stabilisation kills much of what makes human beings creative. Innovation and creativity are fundamentally disequilibrium processes so it is no surprise that an environment of stability does not foster them. Whitaker interviews a patient on antidepressants who said: “I didn’t have mood swings after that, but instead of having a baseline of functioning normally, I was depressed. I was in a state of depression the entire time I was on the medication.”

He also notes disturbing research on the damage done to children who were treated for ADHD with Ritalin:

when researchers looked at whether Ritalin at least helped hyperactive children fare well academically, to get good grades and thus succeed as students, they found that it wasn’t so. Being able to focus intently on a math test, it turned out, didn’t translate into long-term academic achievement. This drug, Sroufe explained in 1973, enhances performance on “repetitive, routinized tasks that require sustained attention,” but “reasoning, problem solving and learning do not seem to be [positively] affected.”……Carol Whalen, a psychologist from the University of California at Irvine, noted in 1997 that “especially worrisome has been the suggestion that the unsalutary effects [of Ritalin] occur in the realm of complex, high-order cognitive functions such as flexible problem-solving or divergent thinking.”

Progressive Increase in Required Dosage

In economic systems, this steady structural deterioration means that increasing amounts of stimulus need to be applied in successive cycles of stabilisation to achieve the same levels of growth. Whitaker too identifies a similar tendency:

Over time, Chouinard and Jones noted, the dopaminergic pathways tended to become permanently dysfunctional. They became irreversibly stuck in a hyperactive state, and soon the patient’s tongue was slipping rhythmically in and out of his mouth (tardive dyskinesia) and psychotic symptoms were worsening (tardive psychosis). Doctors would then need to prescribe higher doses of antipsychotics to tamp down those tardive symptoms.

At this point, some of you may raise the following objection: so what if the new state is pathological? Maybe capitalism with its inherent instability is itself pathological. And once the safety nets of the Greenspan/Bernanke put, lender-of-last-resort programs and too-big-to-fail bailouts are put in place why would we need or want to remove them? If we simply medicate the economy ad infinitum, can we not avoid collapse ad infinitum?

This argument however is flawed.

• The ability of economic players to reorganise to maximise the rents extracted from central banking and state commitments far exceeds the the resources available to the state and the central bank. The key reason for this is the purely financial nature of this commitment. For example, if the state decided to print money and support the price of corn at twice its natural market price, then it could conceivably do so forever. Sooner or later, rent extractors will run up against natural resource limits – for example,limits on arable land. But when the state commits to support a credit money dominant financial system and asset prices then the economic system can and will generate financial “assets” without limit to take advantage of this commitment. The only defense that the CB and the state possess is regulations aimed at maintaining financial markets in an incomplete, underdeveloped state where economic agents do not possess the tools to game the system. Unfortunately as Minsky and many others have documented, the pace of financial innovation over the last half-century has meant that banks and financialised corporates have all the tools they need to circumvent regulations and maximise rent extraction.
• Even in a modern state that can print its own fiat currency, the ability to maintain financial commitments is subordinate to the need to control inflation. But doesn’t the complete absence of inflationary pressures in the current environment prove that we are nowhere close to any such limits? Not quite – As I have argued before, the current macroeconomic policy is defined by an abandonment of the full employment target in order to mitigate any risk of inflation whatsoever. The inflationary risk caused by rent extraction from the stabilisation commitment is being counterbalanced by a “reserve army of labour”. The reason for giving up the full employment is simple – As Minsky identified, once the economy has gone through successive cycles of stabilisation, it is prone to ‘rapid cycling’.

Rapid Cycling and Transformation of an Episodic Illness into a Chronic Illness

Minsky noted that

A high-investment, high-profit strategy for full employment – even with the underpinning of an active fiscal policy and an aware Federal Reserve system – leads to an increasingly unstable financial system, and an increasingly unstable economic performance. Within a short span of time, the policy problem cycles among preventing a deep depression, getting a stagnant economy moving again, reining in an inflation, and offsetting a credit squeeze or crunch.

In other words, an economy that attempts to achieve full employment will yo-yo uncontrollably between a state of debt-deflation and high, variable inflation – somewhat similar to a broken shower that only runs either too hot or too cold. The abandonment of the full employment target enables the system to postpone this point of rapid cycling.

The structural malformation of the economic system due to the application of increasing levels of stimulus to the task of stabilisation means that the economy has lost the ability to generate the endogenous growth and innovation that it could before it was so actively stabilised. The system has now been homogenised and is entirely dependent upon constant stimulus. The phenomenon of ‘rapid cycling’ explains a phenomenon I noted in an earlier post which is the apparently schizophrenic nature of the markets, turning from risk-on to risk-off at the drop of a hat. It is the lack of diversity that causes this as the vast majority of agents change their behaviour based on absence or presence of stabilising interventions.

Whitaker again notes the connection between medication and rapid cycling in many instances:

As early as 1965, before lithium had made its triumphant entry into American psychiatry, German psychiatrists were puzzling over the change they were seeing in their manic-depressive patients. Patients treated with antidepressants were relapsing frequently, the drugs “transforming the illness from an episodic course with free intervals to a chronic course with continuous illness,” they wrote. The German physicians also noted that in some patients, “the drugs produced a destabilization in which, for the first time, hypomania was followed by continual cycling between hypomania and depression.””

(stimulants) cause children to cycle through arousal and dysphoric states on a daily basis. When a child takes the drug, dopamine levels in the synapse increase, and this produces an aroused state. The child may show increased energy, an intensified focus, and hyperalertness. The child may become anxious, irritable, aggressive, hostile, and unable to sleep. More extreme arousal symptoms include obsessive-compulsive and hypomanic behaviors. But when the drug exits the brain, dopamine levels in the synapse sharply drop, and this may lead to such dysphoric symptoms as fatigue, lethargy, apathy, social withdrawal, and depression. Parents regularly talk of this daily “crash.”

THE PATIENT WANTS STABILITY TOO

At this point, I seem to be arguing that stabilisation is all just a con-game designed to enrich evil bankers, evil pharma etc. But such an explanation underestimates just how deep-seated the temptation and need to stabilise really is. The most critical component that it misses out on is the fact that the “patient” in complex adaptive systems is as eager to choose stability over resilience as the doctor is.

The Short-Term vs The Long-Term

As Daniel Carlat notes, the reality is that on the whole, psychiatric drugs “work” at least in the short term. Similarly, each individual act of macroeconomic stabilisation such as a lender-of-last-resort intervention, quantitative easing or a rate cut clearly has a positive impact on the short-term performance of both asset markets and the economy.

Whitaker too acknowledges this:

Those are the dueling visions of the psychopharmacology era. If you think of the drugs as “anti-disease” agents and focus on short-term outcomes, the young lady springs into sight. If you think of the drugs as “chemical imbalancers” and focus on long-term outcomes, the old hag appears. You can see either image, depending on where you direct your gaze.

The critical point here is that just like in forest fires and macroeconomies, the initial attempts to stabilise can be achieved easily and with very little medication. The results may seem even miraculous. But this initial period does not last. From one of many cases Whitaker quotes:

at first, “it was like a miracle,” she says. Andrew’s fears abated, he learned to tie his shoes, and his teachers praised his improved behavior. But after a few months, the drug no longer seemed to work so well, and whenever its effects wore off, there would be this “rebound effect.” Andrew would “behave like a wild man, out of control.” A doctor increased his dosage, only then it seemed that Andrew was like a “zombie,” his sense of humor reemerging only when the drug’s effects wore off. Next, Andrew needed to take clonidine in order to fall asleep at night. The drug treatment didn’t really seem to be helping, and so Ritalin gave way to other stimulants, including Adderall, Concerta, and dextroamphetamine. “It was always more drugs,” his mother says.

Medication Seen as Revealing Structural Flaws

One would think that the functional and structural deterioration that follows constant medication would cause both the patient and the doctor to reconsider the benefits of stabilisation. But this deterioration too can be interpreted in many different ways. Whitaker gives an example where the stabilised state is seen to be beneficial by revealing hitherto undiagnosed structural problems:

in 1982, Michael Strober and Gabrielle Carlson at the UCLA Neuropsychiatric Institute put a new twist into the juvenile bipolar story. Twelve of the sixty adolescents they had treated with antidepressants had turned “bipolar” over the course of three years, which—one might think—suggested that the drugs had caused the mania. Instead, Strober and Carlson reasoned that their study had shown that antidepressants could be used as a diagnostic tool. It wasn’t that antidepressants were causing some children to go manic, but rather the drugs were unmasking bipolar illness, as only children with the disease would suffer this reaction to an anti-depressant. “Our data imply that biologic differences between latent depressive subtypes are already present and detectable during the period of early adolescence, and that pharmacologic challenge can serve as one reliable aid in delimiting specific affective syndromes in juveniles,” they said.

Drug Withdrawal as Proof That It Works

The symptoms of drug withdrawal can also be interpreted to mean that the drug was necessary and that the patient is fundamentally ill. The reduction in withdrawal symptoms when the patient goes back on provides further “proof” that the drug works. Withdrawal symptoms can also be interpreted as proof that the patient needs to be treated for a longer period. Again, quoting from Whitaker:

Chouinard and Jones’s work also revealed that both psychiatrists and their patients would regularly suffer from a clinical delusion: They would see the return of psychotic symptoms upon drug withdrawal as proof that the antipsychotic was necessary and that it “worked.” The relapsed patient would then go back on the drug and often the psychosis would abate, which would be further proof that it worked. Both doctor and patient would experience this to be “true,” and yet, in fact, the reason that the psychosis abated with the return of the drug was that the brake on dopamine transmission was being reapplied, which countered the stuck dopamine accelerator. As Chouinard and Jones explained: “The need for continued neuroleptic treatment may itself be drug-induced.”

while they acknowledged that some alprazolam patients fared poorly when the drug was withdrawn, they reasoned that it had been used for too short a period and the withdrawal done too abruptly. “We recommend that patients with panic disorder be treated for a longer period, at least six months,” they said.

Similarly, macroeconomic crises can and frequently are interpreted as a need for better and more stabilisation. The initial positive impact of each intervention and the negative impact of reducing stimulus only reinforces this belief.

SCIENCE AND STABILISATION

A typical complaint against Whitaker’s argument is that his thesis is unproven. I would argue that within the confines of conventional “scientific” data analysis, his thesis and others directly opposed to it are essentially unprovable. To take an example from economics, is the current rush towards “safe” assets a sign that we need to produce more “safe” assets? Or is it a sign that our fragile economic system is addicted to the need for an ever-increasing supply of “safe” assets and what we need is a world in which no assets are safe and all market participants are fully aware of this fact?

In complex adaptive systems it can also be argued that the modern scientific method that relies on empirical testing of theoretical hypotheses against the data is itself fundamentally biased towards stabilisation and against resilience. The same story that I trace out below for the history of mental health can be traced out for economics and many other fields.

Desire to Become a ‘Real’ Science

Whitaker traces out how the theory attributing mental disorders to chemical imbalances was embraced as it enabled psychiatrists to become “real” doctors and captures the mood of the profession in the 80s:

Since the days of Sigmund Freud the practice of psychiatry has been more art than science. Surrounded by an aura of witchcraft, proceeding on impression and hunch, often ineffective, it was the bumbling and sometimes humorous stepchild of modern science. But for a decade and more, research psychiatrists have been working quietly in laboratories, dissecting the brains of mice and men and teasing out the chemical formulas that unlock the secrets of the mind. Now, in the 1980s, their work is paying off. They are rapidly identifying the interlocking molecules that produce human thought and emotion…. As a result, psychiatry today stands on the threshold of becoming an exact science, as precise and quantifiable as molecular genetics.

Search for the Magic Bullet despite Complexity of Problem

In the language of medicine, a ‘magic bullet’ is a drug that counters the root cause of the disease without adversely affecting any other part of the patient. The chemical-imbalance theory took a ‘magic bullet’ approach which reduced the complexity of our mental system to “a simple disease mechanism, one easy to grasp. In depression, the problem was that the serotonergic neurons released too little serotonin into the synaptic gap, and thus the serotonergic pathways in the brain were “underactive”. Antidepressants brought serotonin levels in the synaptic gap up to normal, and that allowed these pathways to transmit messages at a proper pace.”

Search for Scientific Method and Objective Criteria

Whitaker traces out the push towards making psychiatry an objective science with a defined method and its implications:

Congress had created the NIMH with the thought that it would transform psychiatry into a more modern, scientific discipline…..Psychiatrists and nurses would use “rating scales” to measure numerically the characteristic symptoms of the disease that was to be studied. Did a drug for schizophrenia reduce the patient’s “anxiety”? His or her “grandiosity”? “Hostility”? “Suspiciousness”? “Unusual thought content”? “Uncooperativeness”? The severity of all of those symptoms would be measured on a numerical scale and a total “symptom” score tabulated, and a drug would be deemed effective if it reduced the total score significantly more than a placebo did within a six-week period. At least in theory, psychiatry now had a way to conduct trials of psychiatric drugs that would produce an “objective” result. Yet the adoption of this assessment put psychiatry on a very particular path: The field would now see short-term reduction of symptoms as evidence of a drug’s efficacy. Much as a physician in internal medicine would prescribe an antibiotic for a bacterial infection, a psychiatrist would prescribe a pill that knocked down a “target symptom” of a “discrete disease.” The six-week “clinical trial” would prove that this was the right thing to do. However, this tool wouldn’t provide any insight into how patients were faring over the long term.

It cannot be emphasised enough that even increasing the period of the scientific trial is not enough to give us definitive answers. The argument that structural flaws are being uncovered or that withdrawal proves that the drug works cannot be definitively refuted. Moreover, at every point of time after medication is started, the short-term impact of staying on or increasing the level of medication is better than the alternative of going off the medication. The deeper issue here is also that in such a system, statistical analysis that tries to determine the efficacy of the intervention cannot deal with the fact that the nature of the intervention itself is to shift the distribution of outcomes into the tail and continue to do so as long as the level of medication keeps increasing.

The Control Agenda and High Modernism

The desire for stability and the control agenda is not simply a consequence of the growth of Olsonian special interests in the economy. The title of this post is inspired by Holling and Meffe’s classic paper on this topic in ecology. Their paper highlights that stabilisation is embedded within the command-and-control approach which itself is inherent to the high modernist way that James Scott has criticised.

Holling and Meffe also recognise that it is a simplistic application of “scientific” methods that underpins this command-and-control philosophy:

much of present ecological theory uses the equilibrium definition of resilience, even though that definition reinforces the pathology of equilibrium-centered command and control. That is because much of that theory draws predominantly from traditions of deductive mathematical theory (Pimm 1984) in which simplified, untouched ecological systems are imagined, or from traditions of engineering in which the motive is to design systems with a single operating objective (Waide & Webster 1976; De Angelis et. al. 1980; O’Neill et al. 1986), or from small-scale quadrant experiments in nature (Tilman & Downing 1994) in which long-term, large-scale successional or episodic transformations are not of concern. That makes the mathematics more tractable, it accommodates the engineer’s goal to develop optimal designs, and it provides the ecologist with a rationale for utilizing manageable, small sized, and short-term experiments, all reasonable goals. But these traditional concepts and techniques make the world appear more simple, tractable, and manageable than it really is. They carry an implicit assumption that there is global stability – that there is only one equilibrium steady-state, or, if other operating states exist, they should be avoided with safeguards and regulatory controls. They transfer the command-and-control myopia of exploitive development to similarly myopic demands for environmental regulations and prohibitions.

Those who emphasize ecosystem resilience, on the other hand, come from traditions of applied mathematics and applied resource ecology at the scale of ecosystems, such as the dynamics and management of freshwater systems (Fiering 1982) forests (Clark et al. 19759, fisheries (Walters 1986) semiarid grasslands (Walker et al. 1969), and interacting populations in nature (Dublin et al. 1990; Sinclair et al. 1990). Because these studies are rooted in inductive rather than deductive theory formation and in experience with the effects of large-scale management disturbances, the reality of flips from one stable state to another cannot be avoided (Helling 1986).

My aim in this last section is not to argue against the scientific method but simply to state that we have adopted too narrow a definition of what constitutes a scientific endeavour. Even this is not a coincidence. High modernism has its roots firmly planted in Enlightenment rationality and philosophical viewpoints that lie at the core of our idea of progress. In many uncertain domains, genuine progress and stabilisation that leads to fragility cannot be distinguished from each other. These are topics that I hope to explore in future posts.

Italian banks will be able to borrow [from the ECB] at 1 per cent, while the Italian state is borrowing at 6–7 per cent. It doesn’t take a finance specialist to see that the Italian state will be able to ask Italian banks to finance part of the government debt at a much lower rate.

In other words, the ECB will not finance fiscal deficits directly but will be more than happy to do so via the Eurozone banking system. But this plan still has a few critical flaws:

• As Sony Kapoor notes, “By doing this, you are strengthening the link between banks and sovereigns, which has proven so dangerous in this crisis. Even if useful in the short term, it would seriously increase the vulnerability of both banks and sovereigns to future shocks.” In other words, if the promise to exclude the possibility of inflicting losses on sovereign debt-holders is broken at any point of time in the future, then sovereign default will coincide with a complete decimation of the incumbent banks in Europe.
• European banks are desperately capital-constrained as the latest EBA estimates on the capital shortfall faced by European banks shows. In such a condition, banks will almost certainly take on increased sovereign debt exposures only at the expense of lending to the private sector and households. This can only exacerbate the recession in the Eurozone.
• Sarkozy’s comment also hints at the deep unfairness of the current proposal. If default and haircuts are not on the table, then allowing banks to finance their sovereign debt holdings at a lower rate than the yield they earn on the sovereign bonds (at the same tenor) is simply a transfer of wealth from the Eurozone taxpayer to the banks. Such a privilege may only be extended to the banks if banking is a “perfectly competitive” sector which it is far from being even in a boom economy. In the midst of an economic crisis when so many banks are tottering, it is even further away from the ideal of perfect competition.

There is a simple solution that tackles all three of the above problems – extend the generous terms of refinancing sovereign debt to the entire populace of the Eurozone such that the market for the “support of sovereign debt” is transformed into something close to perfectly competitive. In practise, this simply requires undertaking a program of fast-track banking licenses to new banks with low minimum size requirements on the condition that they restrict their activities to a narrow mandate of buying sovereign debt. This plan can correct all the flaws of the current proposal:

• Instead of being concentrated within the incumbent failing banks, the sovereign debt exposure of the Eurozone would be spread in a diversified manner within the population. This will also help in making the “no more haircuts” commitment more time-consistent. The wider base of sovereign debt holders will reduce the possibility that the commitment will be reversed by democratic means. The only argument against this plan is that such a concentrated new bank is too risky but that assumes that there is still default risk on Eurozone sovereign debt and that the commitment is not credible.
• The plan effectively injects new capital into the banking sector allowing incumbent bank capital to be deployed towards lending to the private sector and households. If sovereign debt spreads collapse, then the plan will also shore up the financial position of the incumbent banks thus injecting further capital available to be deployed.
• The plan is fair. If the current crisis is indeed just a problem of high interest rates fuelling an increased risk of default, then interest rates will rapidly fall to a level much closer to the refinancing rate. To the extent that rates stay elevated and spreads do not converge, it will provide a much more accurate reflection of the real risk of default. No one will earn a supra-normal rate of return.

On this blog, I have criticised the indiscriminate provision of “liquidity” backstops by central banks on many occasions. I have also asserted that key economic functions must be preserved, not the incumbent entities that provide such functions. In times of crisis, central banking interventions are only fair when they are effectively accessible to the masses. At this critical juncture, the socially just policy may also be the only option that can save the single currency project.

As Steve Roth notes, business investment has been anaemic throughout the neo-liberal era. JW Mason reminds us that the neo-liberal transition also coincided with a dramatically increased financialisation of the real economy. Throughout my series of posts on crony capitalism, I have argued that the structural and cyclical problems of the developed world are inextricably intertwined. The anaemic trend in business investment is the reason why the developed world has been in a ‘great stagnation’ for so long. This ‘investment deficit’ manifests itself as the ‘corporate savings glut’ and an increasingly financialised economy. The cause of the investment deficit is an increasingly financialised, cronyist, demosclerotic system where incumbent corporates do not face competitive pressure to engage in risky exploratory investment.

Business investments can typically either operate upon the scale of operations (e.g. capacity,product mix) or they can change the fundamental character of operations (e.g. changes in process, product). Investments in scaling up operations are most easily influenced by monetary policy initiatives which reduce interest rates and raise asset prices or direct fiscal policy initiatives which operate via the multiplier effect. Investments in process innovation require the presence of price competition within the industry. Investments in exploratory product innovation require not only competition amongst incumbent firms but competition from a constant and robust stream of new entrants into the industry.

In an economy where new entrants are stymied by an ever-growing ‘License Raj’ that costs the US economy an estimated $100 billion per year, a web of regulations that exist primarily to protect incumbent large corporates and a dysfunctional patent regime, it is not surprising that exploratory business investment has fallen so dramatically. A less cronyist and more dynamically competitive economy without the implicit asset-price protection of the Greenpan/Bernanke put will have lesser profits in aggregate but more investment. Incumbents need to be compelled to take on risky ventures by the threat of extinction and obsolescence. Increased investments in risky exploratory ventures will not only drag the economy out of the ‘Great Stagnation’ but it will result in a reduced share of GDP flowing to corporate profits and an increased proportion of GDP flowing towards wages. In turn, this enables the economy to achieve a sustainable state of full employment and even a higher level of sustainable consumption without households having to resort to increased leverage as they had to during the Great Moderation.

Alexander Field has illustrated how even the growth of the Golden Age of the 50s and the 60s was built upon the foundations of Pre-WW2 innovation. If this thesis is correct, the ‘Great Stagnation’ was inevitable and in fact understates how long ago the innovation deficit started. The Great Moderation far from being the cure was simply a palliative that postponed the inevitable end-point of the evolution of the macroeconomy through successive cycles of Minskyian stabilisation. As I noted in a previous post:

The neoliberal transition unshackled the invisible hand (the carrot of the profit motive) without ensuring that all key sectors of the economy were equally subject to the invisible foot (the stick of failure and losses and new firm entry)….“Order for all” became “order for the classes and disorder for the masses”….In this increasingly financialised economy, the increased market-sensitivity combined with the macro-stabilisation commitment encourages low-risk process innovation and discourages uncertain and exploratory product innovation. This tilt towards exploitation/cost-reduction without exploration kept inflation in check but it also implied a prolonged period of sub-par wage growth and a constant inability to maintain full employment unless the consumer or the government levered up. For the neo-liberal revolution to sustain a ‘corporate welfare state’ in a democratic system, the absence of wage growth necessitated an increase in household leverage for consumption growth to be maintained. 

When commentators such as James Livingston claim that tax cuts for businesses will not solve our problems and that we need a redistribution of income away from profits towards wages to trigger increased aggregate demand via aggregate consumption, I agree with them. But I disagree with the conclusion that the secular decline in business investment is inevitable, acceptable and unrelated to the current cyclical downturn. The fact that business investment during the Great Moderation only increased when consumption demand went up is a symptom of the corporatist nature of the economy. When the household sector has reached a state of peak debt and the financial system has reached its point of peak elasticity, simply running increased fiscal deficits without permitting the corporatist superstructure to collapse simply takes us to the end-state that Minsky himself envisioned: an economy that attempts to achieve full employment will yo-yo uncontrollably between a state of debt-deflation and high,variable inflation – somewhat similar to a broken shower that only runs either too hot or too cold. The only way in which the corporatist status quo can postpone collapse is to abandon the goal of full employment which is exactly the path that the developed world has taken.  This only substitutes an economic fragility with a deeper social fragility.

Stability for all is synonymous with an environment of permanent innovative stagnation. The Schumpeterian solution is to transform the system into one of instability for all. Micro-fragility is the key to macro-resilience but this fragility must be felt by all economic agents, labour and capital alike. In order to end the stagnation and achieve sustainable full employment, we need to allow incumbent banks and financialised corporations to collapse and dismantle the barriers to entry of new firms that pervade the economy. The risk of a deflationary contraction from allowing such a collapse be prevented in a simple and effective manner with a system of direct transfers to individuals as Steve Waldman has outlined. This solution also reverses the flow of rents that have exacerbated inequality over the past few decades.

Note: I went through a much longer version of the same argument with an emphasis on the relationship between employment and technology adapted to US economic history in a previous post. The above logic explains my disagreements with conventional Keynesian theory and my affinity with Post-Keynesian theory. Minsky viewed his theory as a  ’investment theory of the cycle and a financial theory of investment’ and my views are simply a neo-Schumpeterian take on the same underlying framework.

The Borio-Disyatat paper is especially recommended. It explains best why the savings glut thesis itself is a product of a faulty ‘Loanable Funds’ view of money. Much more appropriate is the credit/financing view of money that Borio and Disyatat take. The best explanation of this credit view is Chapter 3 (’Credit and Capital’) in Joseph Schumpeter’s book ‘Theory of Economic Development’. As Agnès Festré notes, Hayek had a very similar theory of credit but a very different opinion as to its implications:

both Hayek and Schumpeter make use of the mechanism of forced saving in their analyses of the cyclical upswing in order to describe the real effects of credit creation. In Schumpeter’s framework, the relevant redistribution of purchasing power is from traditional producers to innovators with banks playing a crucial complementary role in meeting demand for finance by innovating firms. The dynamic process thus set into motion then leads to a new quasi-equilibrium position characterised by higher productivity and an improved utilisation of resources. For Hayek, however, forced saving is equivalent to a redistribution from consumers to investing producers as credit not backed by voluntary savings is channelled towards investment activities, in the course of which more roundabout methods of production are being implemented. In this setting, expansion does not lead to a new equilibrium position but is equivalent to a deviation from the equilibrium path, that is to an economically harmful distortion of the relative (intertemporal) price system. The eventual return to equilibrium then takes place via an inevitable economic crisis.

Schumpeter viewed this elasticity of credit as the ‘differentia specifica’ of capitalism. Although this view combined with his vision of the banker as a ‘capitalist par excellence’ may have been true in an unstabilised financial system, it is not accurate in the stabilised financial system that his student Hyman Minsky identified as the reality of the modern capitalist economy. Successive rounds of stabilisation mean that the modern banker is more focused on seeking out bets that will be validated by central bank interventions than funding disruptive entrepreneurial activity. Moreover, we live in a world where maturity transformation is no longer required to meet our investment needs. The evolution and malformation of the financial system means that Hayek’s analysis is more relevant now than it probably was during his own lifetime.

The entire long arc of post-WW2 macroeconomic policy in the developed world can be described as a flawed exercise in macroeconomic stabilisation. But there is no better example of this principle than the Euro currency project as the below graph (from Pictet via FT Alphaville) illustrates.

Instead of a moderately volatile mix of different currencies and interest rates, we now have a mostly stable currency union prone to the occasional risk of systemic collapse. If this was all there is to it, then it is not clear that the Euro is such a bad idea. After all, simply shifting the volatility out to the tails is not by itself a bad outcome. But the resilience-stability tradeoff is more than just a simple transformation in distribution. Economic agents adapt to a prolonged period of stability in such a manner that the system cannot “withstand even modest adverse shocks”. “Normal” disturbances that were easily absorbed prior to the period of stabilisation are now sufficient to cause a catastrophic transition. Izabella Kaminska laments the fact that sovereign spreads for many Eurozone countries (vs 10Y Bunds) now exceed pre-Euro levels. But the real problem isn’t so much that spreads have blown out but that they have blown out after a prolonged period of stability.

One way to analyse this evolution is via Axel Leijonhufvud’s ‘corridor hypothesis’. Leijonhufvud postulated that a macroeconomy will adapt well to small shocks but “outside of a certain zone or “corridor” around its long-run growth path, it will only very sluggishly react to sufficiently large, infrequent shocks.” In Leijonhufvud’s own view, the driver of this “demand failure” outside the corridor was the “exhaustion of liquid buffers reinforced by dysfunctional revisions of permanent income expectations” Stability reduces the width of the corridor to the point where even a small shock is enough to push the system outside the corridor – the primary driver of this process is a progressive reduction of liquid buffers in the good times such that even a small shock will exhaust them.

In my earlier posts comparing the dilemmas of managing a stabilised economic system to those of a fire-suppressed forest and a flood-suppressed river system, I claimed that managing a stabilised and fragile system is akin to choosing between the frying pan and the fire. Minsky too recognised that the stabilisation program would eventually run into a cul-de-sac where “the choice seems to become whether to accomodate to an increasing inflation or to induce a debt-deflation process that can lead to a serious depression”. What he could not possibly have foreseen is that instead of turning towards the safety valve of inflation, the developed world would instead choose to abandon the goal of full employment. This of course simply chooses social fragility over macroeconomic fragility. The consequences of the abandonment of the Euro project pale in comparison to the forces that may be unleashed by the combination of social fragility via unemployment and a perceived democratic deficit.