What happens when complacent economists meet a dynamic planet?

The reason is easy to see. The standard economic approach to predicting the future makes assumptions about future technologies, tastes and preferences and institutions and use them to impose long term structural solutions to their models. When looking two or three years ahead, this makes sense. When I was Head of Economic Forecasting, I ran the UK Treasury’s economic model to look at the effects of perturbations in things like interest rates, policies and oil prices on output and inflation assuming the structure of the economy remains unchanged. But over the longer term, it is the structural assumptions that are the very things we care about forecasting. How will technologies emerge, how will tastes and preferences change, what underlies a shift from one network to another, how do consumers change habits and social norms?

Can economists model the future?

Economic models plot a spectrum of low-carbon interventions ranging from low to high cost, in the form of a ‘marginal abatement curve’. This starts with the proverbial ‘low hanging fruit’ of energy efficiency and waste reduction, moving steadily to more costly technologies in more marginal locations. The more ambitious the attempt to decarbonise the economy, the more expensive the investment that needs to be undertaken. As a result, economists routinely predict high costs associated with decarbonisation pathways sufficient to meet the Paris goal of 2 degrees Celsius temperature rise relative to preindustrial times (even more so for 1.5 degrees). Yet this static approach fails to model the processes and dynamics that lead to systemic transformational innovation.
 

For example, conventional economists understand the concept of learning by doing. This says that as you develop and deploy a new technology, you learn through experimentation how to fabricate, fit, engineer and maintain it. This drives innovation which makes goods cheaper and more productive. Many economic models now explicitly incorporate this process. Yet it’s usually not the learning that these economists get wrong, it’s the doing.

Take the figure below. This shows forecast made by the International Energy Agency (IEA) for the deployment of renewables compared with actual outturns. The IEA is arguably the leading authority on energy technologies, yet they systematically and repeatedly underestimate the deployment of renewables and correspondingly overestimate the costs.

IEA renewable capacity forecasts, ex-hydropower

Source: Metayer, Breyer and Fell, 2015.

What the IEA failed to account for is the broader social context, how governments subsidised new technologies when expensive, precipitating price falls such that others would deploy them. They failed to anticipate that once a technology becomes sufficiently competitive, it starts to change the entire environment in which it operates and interacts. New supply lines are formed, behaviours change, and new business lobbies push for more supportive policies. New institutions are created, and old ones repurposed. As costs fall and expectations of market size increase, additional investment is induced and the political and commercial barriers to a transition begin to drop away. A tipping point is eventually reached where incumbent technologies, products and networks become redundant. Those late to recognise the transition stand exposed to stranded or devalued assets. 

Off-model and out-of-mind

Economists using static models generally miss these complex interactions and feedbacks. No model ‘predicted’ that, by now, renewables would be the biggest source of investment in global energy generation, outpacing coal, oil, gas, nuclear and hydro combined. No one predicted that the price of solar photovoltaic (PV) would fall 44% in the two years to the end of August 2017 and by 83% since 2010, a period over which the price of wind turbines dropped 35%. They did not predict that LED lighting would have gone from less than 5% of the global lighting market to more than 40% in the past six years, while plug-in vehicles in Norway alone have gone from around 5% of sales to nearly 50% and account for all the growth in China’s car market.

Instead, the Intergovernmental Panel on Climate Change (IPCC) predicted that meeting a 2°C stabilisation target would cost an average loss in global output of 2.9% to 11.4% a year by 2100. Such spurious precision on costs is risible when economists struggle to predict GDP two years out to one decimal place, let alone over 80 years.

Expectations and psychology shape the real world

To the extent that economists are believed, they become part of the problem. Getting the future wrong has the potential to make the future wrong (by generating what game theorists call an inferior Nash equilibrium). A mayor, politician or businessperson will not want to invest in renewable energy and energy efficiency if ‘experts’ insist it will be prohibitively expensive, hard to finance and lacking in market opportunities. Not investing means forgoing the benefits of learning-by-doing. Underplaying the benefits means framing global climate negations solely in terms of common sacrifice for the greater good. This rarely breeds action as it fosters a ‘tragedy of the commons’ race to the bottom, with distrust and ill-will delaying low-carbon investment and encouraging free-riding on the actions of others. Such paralysis characterises recent decades.

But things are changing. Social psychologists have long understood that solving coordination problems requires building expectations into models and generating ‘common knowledge.’¹ The big innovation of the Paris Agreement is that it ditched the language of ‘burden-sharing’ and focussed instead on nationally determined voluntary contributions. This reflects the reality that self-interest, not shared sacrifice for the greater good, breeds cooperation. This in turn builds on a growing appreciation of the opportunities associated with a low-carbon transition. These include not only commercial opportunities associated with deploying (and fabricating and exporting) cheap and increasingly competitive new clean technologies, but also benefits from reductions in waste and inefficiency, improved energy security and reduced particulate pollution and congestion from clean compact cities. Particulate pollution alone causes welfare losses equal to 6% of global economic output annually, double that in Northern India and Eastern China.
 

The case for conditional optimism

But not all economists presuppose a costly transition by assumption. Some have focussed instead on the dynamics of growth embodied in these important network effects and feedbacks. Last year, Paul Romer deservedly won the Nobel Prize in Economics. Romer was an architect of something called endogenous growth theory. This challenged the classical economic orthodoxy by asserting that growth in total factor productivity does not just happen. It results from investments made in human, physical and other forms of capital with growing knowledge having the potential to complement and positively impact the productivity of other forms of capital.² The key intuition is that increasing returns to ideas overcome the diminishing returns to factors like labour and capital. Investing in mobile or wired computers induces smart ideas on how to use them, enhancing the returns to developing new software and algorithms. This further increases the value of, and demand for computers. Investment in physical and knowledge capital begets increased output and generates resources for further investment.

Endogenous growth theory gives the purveyors of ‘the dismal science’ the evidence base required to be optimistic about a low carbon transition. Romer distinguished between ‘complacent optimism’ and ‘conditional optimism’. He writes: “Complacent optimism is the feeling of a child waiting for presents. Conditional optimism is the feeling of a child who is thinking about building a treehouse. ‘If I get some wood and nails and persuade some other kids to help do the work, we can end up with something really cool.’” He adds “What the theory of endogenous technological progress supports is conditional optimism, not complacent optimism… Instead of suggesting that we can relax because policy choices don’t matter, it suggests to the contrary that policy choices are even more important than traditional theory suggests.”

What Romer is saying is that the barriers to overcoming major challenges are not primarily economic or technological, they are political, cultural and institutional. This explains why economists consistently get the future wrong. We do not know the form of the technologies and behaviours that will ultimately deliver a zero net carbon world, nor do we know the precise costs. What we do know is that these costs will be a direct function of the decisions we take today. We also know that once it comes, change can come fast and be transformative. The bottom line then becomes self-evident – when faced with systemic technological transformation, economists and investors would do well to spend less time predicting future and more time shaping it.

Dimitri Zenghelis is Project Leader for the Wealth Economy project centred at Cambridge University and a Senior Visiting Fellow at the London School of Economics. He was Head of Climate Policy at the Grantham Research Institute at the LSE and Acting Chief Economist for the Global Commission on the Economy and Climate. Previously, he headed the Stern Review Team at the Office of Climate Change, London, and was a lead author on the Stern Review on the Economics of Climate Change, commissioned by the then Chancellor Gordon Brown. Before working on climate change, Dimitri was Head of Economic Forecasting at HM Treasury. He currently advises the Mayor of London and the UK Committee on Climate Change and was a Coordinating Lead Author for the UN Global Environment Outlook, GEO-6. Dimitri is also on the advisory board of the Oxford Sustainable Finance Programme.

Please note that Dimitri Zenghelis’ views and opinions are his own and not necessarily a reflection of those of the Lombard Odier Group. 

¹ K. Thomas, O.S. Haque, S. Pinker, and P. DeScioli, “The Psychology of Coordination and Common Knowledge,” Journal of Personality and Social Psychology 107 (2014): 657–76.
² See Romer, P., 1990. Endogenous technological change. Journal of Political Economy, 98(5), pp. S71–S102.; Solow (1994), ‘Perspectives on growth theory’, Journal of Economic Perspectives, 8(1), 45–54. Acemoglu, Daron (2009). "Endogenous Technological Change". Introduction to Modern Economic Growth. Princeton University Press. pp. 411–533.