"It is difficult to make predictions, especially about the future" - so goes the saying. The same applies to economic modelling. This is not just the trite point that it is hard to make predictions about things that are uncertain, though you’d be surprised how many economic models are expressed as deterministic, that is, without probability bounds. Pindyck makes the valid point that key inputs are often chosen arbitrarily; even the best model spits rubbish out if you pump rubbish in. But I want to focus on the slightly more subtle point that the very things that are most interesting when it comes to making predictions decades ahead are those which are hardest to model. The result is that more often than not, they are simply not modelled and consequently the models tell us little about how the future will evolve and still less about the true costs and benefits of long run policies such as those to promote renewable technologies and resource efficiency.

 

An economic model is essentially a simplified framework for describing the workings of the economy. It exerts the discipline of forcing the modeller to formally articulate assumptions and tease out relationships behind those assumptions. Models are used for two main purposes: simulating (e.g. how would the world change relative to some counterfactual if we assume a change in this or that variable) and forecasting (e.g. what the world might look like in 2030). Economic models are great tools for simulations – given what we know about the behavioural workings of the economy, and taking these mostly as given, how might the economy respond to, say, an energy price spike? But models are much less effective at providing forecasts precisely not least because when making forecasts, very little can be taken as given. The further out the forecast, the larger the structural uncertainties making model projections at best illustrative, especially when trying to forecast the impact of non-marginal impulses such as climate change impacts or the transformation of the global energy system.

 

Models used by finance ministries, banks and central banks take the underlying structure of the economy as given and analyse perturbations on the margins through estimated behavioural equations. Both estimated ‘new Keynesian’ and computable general equilibrium models rely on assumptions about pre-determined long term trends or ‘convexity’ associated with diminishing marginal returns and diminishing marginal products, in order to converge on a steady state. Because they rarely look forward beyond a four year horizon, such simplifying assumptions make for good approximations of reality. In fact at the Treasury, we fixed the main forecast variables (GDP growth, unemployment, trade balances, inflation, etc…) first and then ran the model. This is actually the norm for macroeconomic forecasting – the model is essentially used as a consistency check and not a source of projections. Provided the forecast residuals are in line with past patterns, then fixing the forecast path beforehand is validated because the projections are compatible with past estimated behaviour. But looking further out, the uncertainties grow and so do the chances that structural breaks push the economy onto new paths driven by new technologies, institutions and behaviours. Characterising key variables, like output, as reverting to a deterministic mean is convenient but unrealistic the further out you look.

 

This causes problems for economic forecasts tasked with examining the impact of large transformative change such as transitioning to a resource efficient global economy over longer periods. The requirement that a model tends towards a steady-state equilibrium means many key dynamics are modelled as tendencies towards that equilibrium, rather than determinants of it. 'Change' and heterogeneity are modelled as transient states. Yet the real world is what economists call endogenous – that is, subject to systemic changes that originate within the system. Heterogeneous processes breed feedback loops which become permanent features of the system, requiring a theory of the long run characterised by such processes. Economic factors that are subject to economies of scale, capital and institutional lock in, irreversibilities, new networks and path-dependencies are hard to estimate empirically (in some cases they have never happened, yet) and even harder to model because of  the non-linear dynamics. Shocks will have persistent effects (think 9/11), policy choices will have large and amplified implications (think defence spending and the internet) making prediction increasingly difficult.  Different meteorological models and forecast runs make consistent and accurate global forecasts over a two week period, but then start to diverge because of the infamous ‘butterfly wing’ effect. Beyond a month or so, such forecasts diverge wildly and are considered next to useless. The same is true with economic models over long periods.

 

Take some real world examples. Investing in renewable energy technologies pushes their price down as a result of experimentation and learning from mistakes; so-called “learning-by-doing”. These price falls then make the investment increasingly attractive relative to conventional technologies where the gains from additional learning or scaling are smaller. As costs come down, investment increases and engineers learn how to cheaply install, connect and repair the technology (one reason why solar PV is considerably cheaper in Germany than in the US), planning institutions are updated and new networks are built or transfigured. Consumers change behaviour and demand efficiency, recycling and pedestrianisation.

 

Very quickly, a region can switch from one technology network to another as learning and experience makes it more attractive than the incumbent. But such inherently bi-polar path-dependent dynamics are hard—if not impossible—to model. Cities planned on a model of dense development with integrated public transport become many orders of magnitude less resource intensive than cities based on a sprawling car based model, despite having the same levels of income. Once built, they are hard to change retrospectively as behaviours and infrastructures become locked-in. Constituencies lobby for lower petrol prices and more highway lanes in the sprawling city and cycling lanes, public transport and congestion charging in the dense efficient one. The decisions made by planners in China, India and elsewhere will go a long way to determining the efficiency and resource-security of their economies as a whole. They also create sizable new markets which stimulate innovators and investors across the world.

 

But none of this is incorporated into standard models because the full interaction of an endogenous system is fiendishly complex to replicate and any error spreads through the model like a malignant disease. Modelling therefore requires abstraction. Not all variables can be included and not all causal processes simulated. But abstracting is fine until you abstract from the key properties of the system and then purport to forecast that system as whole. In most models, innovation which drives long term economic prospects is assumed to just happen and key features of capitalism such as the tendency towards oligopoly are assumed away in favour of the more tractable assumption of competition.

 

Malthus’s mistake was famously to take the structure of the global economy as given. His model assumed that technologies and processes would remain unchanged, such that the world would run low on resources in the face of growing population and demand. In fact, every extra human mouth came with a human brain. And it was human innovation that allowed agricultural yields to rocket and industrialisation to provide an unprecedented array of consumer possibilities. Modelling innovation requires understanding the unintended consequences which result from knowledge spills-overs from one sector to another. Mariana Mazzucatto tells a compelling story of how almost all the radical technologies behind the iPhone were funded by government, mostly through defence research funds: this includes the internet, GPS, touchscreen display, and even the new voice-activated Siri personal assistant. These underlying dynamics are known and predictable as processes, but not in terms of specific outcomes. Consequently, almost all models abstract these key relationships away.

 

There are also a number of behavioural dynamics to account for. For example, global collective action is subject to gaming where the pay-offs to individual agent’s action (countries, regions, businesses) depends on how others act. If enough players act, a critical mass or ‘tipping set’ is reached where it pays to act also. For example it may pay for a firm or economy to delay the cost of resource efficient investment, but as others make the investment, it may increasingly struggle to sell into new markets where efficiency standards improve (as was the case with US car manufacturers). Such dynamics can be modelled, but are hard to incorporate into existing integrated models. Who could have deterministically modelled the opening up of world trade in the second half of the last century? Yet these underlying dynamics are known and predictable and ultimately it is these that will shape the world.

 

The potential benefits from policy which directs structural change will ultimately dwarf the first round distortionary costs associated with a carbon tax here, or a new standard for efficiency there. As with defence spending, a concerted effort to push green R&D is likely to have multiplied impacts on innovation across the economy. This means that for policymakers, planning a competitive and resource-efficient economy fit for a challenged twenty-first century might make a more important priority than figuring out the short run cost of a set renewables, relevant though that is. Yet models focus almost exclusively on narrow questions of the latter type, just because they can, generating a set of costs for each action, with little understanding of the full potential benefits. The resulting predisposition towards postponing action short-circuits the more pertinent questions of how structural change can be brought about in a transparent, market-friendly manner; one which promotes competition and growth and limits the scope for rent-seeking by vested interests.

 

Does this mean we should jettison economic models? Absolutely not. We should still try to estimate and model known complex processes. Models are essential tools in helping us formulate, examine and understand interactive relationships. Yet while integrated economic models applied to the long term have produced valuable insights, they were never designed to serve as estimates of the total impacts of things like policies to reduce emissions and improve resource efficiency. Fully integrated endogenous systems make modelling long periods very hard because even small errors persist and explode and alter the outputs of the model like a malignant disease. Models attempt to get around this problem by assuming that key parts of that story are predetermined, yet this makes them no more realistic. What is required is a coherent theory behind long run processes of systemic change and these are best modelled separately, and not as part of a fully integrated model which imparts false notions of determinism and precision. Models are not the whole story; they are merely supporting sentences to the story and must be understood and treated as such. In a sufficiently complex system it may actually be easier to drive change than to predict it.

 

 

 

 

 

 

I recently returned from the UNEP International Resources Panel meeting in Berlin where the familiar question was asked; can we be green and grow? Setting aside for the moment the question of whether GDP growth is the only objective we should aim for (clearly it isn't; but if you aspire to alleviate poverty, promote healthy, educated populations, and foster gender opportunities you should not oppose it) this question mostly misses the point. As the panel clearly explains, the debate about resource-efficient green growth is precisely about sustaining growth and delivering human wellbeing. It is not at the periphery of the growth story, articulating narrow concerns about polar bears and coral reefs; it is the growth story.

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Indeed, the key driver of both growth and resource efficiency is what economists call total factor productivity (TFP). This is a catch-all concept that covers all the things that allow us to get more and more out of fewer and fewer resources. It is a proxy for innovation not just in technologies, but also in processes, institutions and behaviours. So green and growth are two sides of the same coin – if we do not become more resource efficient we are destined to meet resource constraints on key resources, which will push up prices at the same time as increasing use of unconventional fuel resources destabilises the climate. With the welcome industrialisation of countries like India and China, global consumption is accelerating at an exponential rate. But the good news that innovation and human ingenuity as reflected in TFP are also growing at an accelerating rate. Small changes in the rate of the latter relative to the former will determine whether we can decouple from material use and live within the earth's resource envelope.

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The challenge for us as a society is to recognise that innovation does not happen in a vacuum. Innovation is very the human response to necessity. Some of this innovation will be driven by the market. Indeed, this process has already begun. Rising prices for commodities are already sending a price signal which will induce innovation, promote efficiency and change behaviour. But without public intervention to address numerous market failures and information asymmetries (not least the failure to properly price scarce resources), the pace of this innovation is likely to be too slow to safeguard wellbeing. At the same time, vested interests will obstruct change in the interests of business as usual, delaying the transition to resource efficiency to the point where irreversible (or expensive to reverse) thresholds are reached, such as climate feedbacks, the loss of biodiversity or the depletion of essential basic elements like phosphorous and potassium. The story of policy progress over recent decades, in the face of known risks, provides ample testimony to this risk.

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In order to understand the risks of delaying action, it is important to understand the dynamics of human innovation. The development of ideas, technologies and behaviours are subject to mutually reinforcing path-dependencies which once set in train, can be difficult to break out of, but which can lead to very different outcomes depending on choices made at the start. For example, the path of technical innovation is not God-given. It is the cumulative consequence of choices made. Conscious investment in new equipment enables new ideas and induces bright ideas on how to use them. Investment in physical and knowledge capital drives increasing returns to scale in production, where more knowledge begets increased output and liberates resources for further investment: a virtuous growth spiral in which future output becomes “path-dependent.” But the lock-in can also be psychological as well as technical.

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Perhaps the most obvious exemplar of this is cities. As the World Bank and OECD and others have shown, cities of similar per capita incomes and similar populations can have vastly different resource calls (as proxied by greenhouse gas emissions per head). Many cities in North America such as Phoenix and Atlanta, built around a model of sprawling suburbs and extensive car use, have emissions which are up to five times higher than cities in Europe and Asia (such as Copenhagen, Amsterdam, Barcelona or Hong Kong) which are built on the model of dense residential concentration and extensive public transport. Yet infrastructural and behavioural lock-in now makes it hard to retrofit resource hungry cities. These cities are now at a major disadvantage as the costs of resources increase. Residents of such cities have little to gain from policies to discourage car use and provide cycle and pedestrian facilities, in contrast to denser cities where city authorities are held accountable, and rewarded, for the provision of such resource-efficient facilities.  China, will not be able to afford the profligate urbanisation based on extensive car use and suburbanisation.. This explains why the Chinese government has set tough efficiency standards and championed green economic sectors in its twelfth five year plan. 

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The central role for public intervention makes it important that policies are carefully designed to avoid market failures being replaced by policy failures.  Unconstrained public intervention can lead to inefficiency, market distortions and scope for rent-seeking by vested interests, lobbying to benefit from public disbursements. Policy must, where possible, be designed to be non-discriminatory making use of market instruments such as pricing which leave technological choice to innovators. Yet strategic choices, for example involving research funding for specific sectors, will need to be made so public decisions must be well-informed and transparent.

The barriers to a resource-efficient economy are not economic or technological, they are cultural, institutional and political.  This requires an understanding of the political economy of change where specific losers are easy to identify and potential winners are diffuse (all of us), and in the case of technologies yet to be developed, potential. This means the former often should louder and influence politicians more effectively than the former.

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The need to tackle the resource-inefficiency has been known for decades, yet policy progress has at times been painfully slow. Change must be made acceptable and profitable, with losers identified and compensated, retooled and reskilled. In the nineteenth century, the whaling industry strongly opposed electrification because it provided the wax for candles and jobs and livelihoods were at stake. Such resistance is understandable but must not be allowed to hold society to hostage. The same logic applies today. The transition to a resource-efficient economy is inevitable and those countries, cities and companies that manage that transition and remain open to change stand to benefit.

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Entrepreneurs and innovators are waiting for a clear signal to develop the new technologies; if you don't ask you won't get they cry. There is no shortage of private money or ingenuity, yet there remains a shortage of perceived opportunity and a lack of confidence in  the public sector’s commitment to promote viable new markets. Without clear, coherent and credible green policies, the private innovation, investment and creativity required to boost growth and jobs in a deflated economy and leave a lasting infrastructural and technological legacy and will remain elusive.

 

 

 

 

A thought-provoking article I read over Easter whilst nibbling the ears off chocolate bunnies.

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​Ronald Dworkin (no, not Richard Dawkins) died recently. In his memory, the NY review of Books published an excerpt from his new book ‘Religion without God’. It tries to uncover the meaning of ‘religious’, and the cognitive nature of value judgments and moral convictions.
 
http://www.nybooks.com/articles/archives/2013/apr/04/religion-without-god/?pagination=false
 
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