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Stop pretending that an economy can be controlled

29 September 2016
by Guest author

NAECAngel Gurría, OECD Secretary-General

The crisis exposed some serious flaws in our economic thinking. It has highlighted the need to look at economic policy with more critical, fresh approaches. It has also revealed the limitations of existing tools for structural analysis in factoring in key linkages, feedbacks and trade-offs – for example between growth, inequality and the environment.

We should seize the opportunity to develop a new understanding of the economy as a highly complex system that, like any complex system, is constantly reconfiguring itself in response to multiple inputs and influences, often with unforeseen or undesirable consequences. This has many implications. It suggests policymakers should be constantly vigilant and more humble about their policy prescriptions, act more like navigators than mechanics, and be open to systemic risks, spillovers, strengths, weaknesses, and human sensitivities. This demands a change in our mind-sets, and in our textbooks. As John Kenneth Galbraith once said, “the conventional view serves to protect us from the painful job of thinking.”

This is why at the OECD we launched an initiative called New Approaches to Economic Challenges (NAEC). With this initiative we want to understand better how the economy works, in all its complexity, and design policies that reflect this understanding. Our aim is to consider and address the unintended consequences of policies, while developing new approaches that foster more sustainable and inclusive growth.

Complexity is a common feature of a growing number of policy issues in an increasingly globalised world employing sophisticated technologies and running against resource constraints.

The report of the OECD Global Science Forum (2009) on Applications of Complexity Science for Public Policy reminds us of the distinction between complicated and complex systems. Traditional science (and technology) excels at the complicated, but is still at an early stage in its understanding of complex phenomena like the climate.

For example, the complicated car can be well understood using normal engineering analyses. An ensemble of cars travelling down a highway, by contrast, is a complex system. Drivers interact and mutually adjust their behaviours based on diverse factors such as perceptions, expectations, habits, even emotions. To understand traffic, and to build better highways, set speed limits, install automatic radar systems, etc., it is helpful to have tools that can accommodate non-linear and collective patterns of behaviour, and varieties of driver types or rules that might be imposed. The tools of complexity science are needed in this case. And we need better rules of the road in a number of areas.

This is not an academic debate. The importance of complexity is not limited to the realm of academia. It has some powerful advocates in the world of policy. Andy Haldane at the Bank of England has thought of the global financial system as a complex system and focused on applying the lessons from other network disciplines – such as ecology, epidemiology, and engineering – to the financial sphere. More generally, it is clear that the language of complexity theory – tipping points, feedback, discontinuities, fat tails – has entered the financial and regulatory lexicon. Haldane has shown the value of adopting a complexity lens, providing insights on structural vulnerabilities that built up in the financial system. This has led to policy suggestions for improving the robustness of the financial system.

Closer to home, Bill White, Chairman of our Economic and Development Review Committee (EDRC) has been an ardent advocate of thinking about the economy as a complex system. He has spoken in numerous OECD meetings – in part as an explanation and in part as a warning – that systems build up as a result of cumulative processes, can have highly unpredictable dynamics and can demonstrate significant non-linearity. As a result Bill has urged policymakers to accept more uncertainty and be more prudent. He also urged economists to learn some exceedingly simple but important lessons from those that have studied or work with complex systems such as biologists, botanists, anthropologists, traffic controllers, and military strategists.

Perhaps the most important insight of complexity is that policymakers should stop pretending that an economy can be controlled. Systems are prone to surprising, large-scale, seemingly uncontrollable, behaviours. Rather, a greater emphasis should be placed on building resilience, strengthening policy buffers and promoting adaptability by fostering a culture of policy experimentation.

At the OECD, we are starting to embrace complexity. For several years we have been mapping the trade “genome” with our Trade in Value Added (TiVA) database to explain the commercial interconnections between countries.

We have examined the possibilities for coupling economic and other systems models, for example environmental (climate) and societal (inequalities). Our work on the Costs of Inaction and Resource Constraints: Implications for Long-term Growth (CIRCLE) is a key example of linking bio-physical models and economic models to gauge the impact of environmental degradation and climate change on the economy.

We are also looking at governing complex systems in areas as diverse as education and international trade policy. And we are looking at the potential for tapping big data – an indispensable element of complexity modelling approaches. But there remains much to do to fully enrich our work with the perspectives of complexity.

The OECD is delighted to work with strong partners – the Institute for New Economic Thinking (INET) Oxford, and the European Commission to help policy-makers advance the use of complex systems thinking to address some of the most difficult challenges.

An important question remains. How can the insights and methods of complexity science be applied to assist policymakers as they tackle difficult problems in areas such as environmental protection, financial regulation, sustainability or urban development?

At the Workshop on Complexity and Policy on 29-30 September at the OECD, we will help find the answer – stimulate new thinking, new policy approaches and ultimately better policies for better lives.

Useful links

The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

Please consult the draft Agenda for more information, and a preliminary background paper “Insights into Complexity and Policy”.

If you are an OECD staff member, please click here to register: Registration

 

NAEC and the Sustainable Development Goals: The Way Forward

28 September 2016
tags:
by Guest author

naecMathilde Mesnard, Senior Advisor to the Secretary-General and OECD New Approaches to Economic Challenges (NAEC) Coordinator, and William Hynes, Senior Economist, NAEC Unit. This article is part of the newly-released Insights book “Debate the Issues: New Approaches to Economic Challenges“.

While global integration has been an engine of growth since the emergence of capitalism, the financial and economic crisis highlighted that the current level of interconnectedness between countries and its impact, positive or negative was poorly understood. This increased complexity has exposed the limitations of prevailing analytical tools, policy frameworks, and governance arrangements. It has also underlined the fact that global challenges can only be addressed through collective co-ordination and action.

The 2030 Agenda for Sustainable Development with the Sustainable Development Goals (SDGs) at its core are based on this new understanding. The goals are universal – applicable to all countries with targets adapted to national circumstances and context. The agenda acknowledges that new approaches are needed to tackle an integrated set of challenges. The SDGs are also transformative – they contribute to systemic change and help anticipate future global threats.

The OECD is actively responding to the agenda with better policies for better lives – drawing on the cumulative experience of member and partner countries and capitalising on its value-added. The New Approaches to Economic Challenges (NAEC) Initiative is helping OECD to prepare for the SDGs – through developing integrated analysis and policy advice for tackling an ambitious set of interlinked goals, as well as the forward-looking transformational agenda. As Doug Frantz has argued, the SDGs and NAEC are like Romeo and Juliet – they are meant for each other.

An Integrated Policy Agenda

The Millennium Development Goals focused mainly on social objectives. Less systematic emphasis was placed on economic growth and jobs as well as environmental sustainability and climate change. A key lesson of the MDGs is that sustained change cannot be achieved through one-dimensional or single sector goals. The SDGs with their much broader coverage require multidimensional policy responses which involves identifying trade-offs, complementarities and unintended consequences of policy choices. This is the only way to improve policy advice for dealing in a more realistic and effective manner with global challenges. It privileges collaboration and coherence in addressing interlinked problems by removing the compartmentalised approach that has too often limited the effectiveness of policies. It also requires a more sophisticated policy design in which systemic spill-overs can be beneficial as well as damaging.

Consideration of these trade-offs should at the first instance be undertaken at the national level. This is where policy-makers can optimise among trade-offs between economic, social and environmental goals. Making policy choices on the basis of their inter-relationships requires systemic and long-term thinking, strategic foresight and strategic governance. Realising this vision has proved elusive but gradually the relevant policy signposts have been put in place. Through the NAEC, analytical frameworks have been broadened to assess better the nexus between economic growth and inequality on the one hand (inclusive growth), and between environment and growth on the other (green growth). Less progress has been made on the social-ecology nexus. Further work is needed to better examine the distributional, employment and skills implications of the transition to environmentally sustainable growth. Eloi Laurent has argued at a NAEC seminar that environmental challenges are in fact social problems that arise largely because of income and power inequalities (Laurent, 2016).

Transformational approaches

With NAEC the OECD is also considering how to cope with the complexity of the world economy replete with numerous interconnections between states, and networks of firms through global and regional value chains. We are increasingly considering the global economy as a complex system. We are measuring the trade and investment linkages between economies – rich and poor – through the Trade in Value Added (TIVA) database. And we are examining how international regulatory co-operation also in tax matters can help ensure a level playing field between jurisdictions.

The policy agenda to meet the SDGs must be transformational to shape a future of intensifying environmental pressures, (e.g. climate change and resource depletion); technological progress and digitalisation as well as rising inequalities.

With NAEC, we are preparing for the future, or possible futures. This requires our Committees and Directorates to keep asking hard questions and challenging assumptions about our understanding of the economy while constantly reviewing our analytical approaches. To ensure that the global goals are reached, we must collectively do the same. We must change our mindsets, approaches and ultimately our economies.

Useful links

The OECD NAEC Unit is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

A new role for science in policy formation in the age of complexity?

28 September 2016
by Guest author

NAECVladimir Šucha, Director General, European Commission, Joint Research Centre

The recent financial crisis was a wakeup call for both scientists and policy makers. It exposed new and unknown links between economic magnitudes but also between various parts of our modern, globalised world. It further helped to reveal the limitations of some approaches in economics as well as social sciences which proved to be unsuitable for this new world.

The crisis, above all, showed that the economy is a highly complex, dynamic and evolving undertaking, with the potential, at times, to produce unpredictable (and often undesired) outcomes. Finally, it showed the need to embrace more appropriately this complexity in the science underlying policy analysis as well as in the policy making process itself.

So, eight years on from the beginning of the crisis, have scientists and policy makers moved out of their comfort zone?  Are new ways of thinking being embraced? Are they being applied in practice? What do we have to do to ensure that they result in better policies and, ultimately, fairer and more resilient societies?

As the European Commission’s science and knowledge service, the Joint Research Centre (JRC) is supposed to bridge the gap between science and policy makers, as is the OECD. Based on our experience, we believe that a good deal of progress has been made. However, there is still a lot of work to do if the science dealing with such complexity is to deliver its full potential.

Complexity science, of course, has been around for some decades now.  It is the scientific study of complex systems, where many components interact producing a global conduct that could not easily be predicted using simple models only which are based on the ordinary inter-action between the individual constituent elements of such systems. Since such systems can be found in many areas of life, complexity science is used in a number of different fields, including biology, social sciences, computer science, transport, energy and critical infrastructure protection.

It has developed quickly in the last few decades. Concepts such as non-linearity, self-adaptation, emergence, chaotic dynamics and multiple equilibria, are now firmly established. Valuable tools have been developed, such as sensitivity analysis, scenario modelling and foresighting, network science and dynamic systems modelling, which allow these concepts to be applied appropriately.

Economics was relatively late to embrace these concepts and tools. However, following the crisis, there is an increased interest in applying them, particularly to financial markets.

The JRC is moving in this direction. For example, our researchers employ network science to estimate inter-linkages between the banking sector and other institutional investors and how shocks could propagate within the system.

However, our impression is that, in spite of the stronger interest in recent years, complexity economics still needs to spread more widely among economists. It should not be the preserve of a small number of outsiders only.

We also feel that it is still not as useful as it could be for policy making. This is because it remains rather abstract. In many cases, it can help us to understand the theoretical characteristics or basis of a phenomenon but it is still difficult to use it for practical problem solving. This may either be because the related models are not sufficiently detailed or because the data used are not sufficiently adequate for the problem under consideration.

There are, of course, many novel sources of data available. The task is to develop innovative paradigms for their collection, and also new methods for their analysis, since large amounts of data can often obscure rather than clarify an issue if the appropriate techniques for interpreting and making sense of them are not available.

Scientists, therefore, need to develop new approaches for gathering and organizing data, such as how to deal with Big Data or else text and data mining. They also need to explore models and tools for data analysis in a policy context, including indicators, innovative visualisations and impact evaluation methods.

The good news is that policy makers are now opening up, at least to some extent.  Most of them now realise that attention to the inter-linkages between policy areas and the related objectives, and improving evidence on the simultaneous movement of various targets and policy levers, is essential.

They know that the impact of regulation cannot be judged only on the basis of its specific achievements inside a given context but that it may also produce unintended (and undesired) consequences in other areas outside the context under consideration.  There is therefore a potential demand for the greater use of complexity science to understand such wider linkages in complex systems.

However, it can be difficult to explain counter-intuitive results to politicians and policy makers.

Equally, while scientists must make policy makers aware of the complexity of the systems they are dealing with, it is important not to overburden them. If they feel that these systems are so complex that no one can possibly understand or influence them, the result will be inertia and defeatism.

Moreover, there is little point in using complexity science to understand the linkages in systems, unless policy makers are prepared to strive for integrated solutions working with one another, across silos. All are committed to doing this in theory but it does not always happen in practice. DG JRC sees part of its role as organising forums on complex issues, where policy makers from different fields can meet, along with scientists from different disciplines.

It is also important to involve those stakeholders most affected by the phenomena under review. DG JRC is experimenting with new ways of directly involving stakeholders in the “co-design” of public interventions. This is all part of developing a multi-faceted perspective.

Finally, there is a job to do in helping policy makers and politicians to develop simple messages to persuade the public of the merits of the solutions arrived at using complex science.

These are only some very basic reflections on why DG JRC welcomes this event. We are keen to further extend our cooperation with OECD and the Institute for New Economic Thinking in the area of Complexity and Policy. By cooperating more closely, we believe that we can further improve the role of science in policy formation in our current world of ever increasing complexity.

Useful links

The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

Big Data, Complexity Theory and Urban Development

27 September 2016
by Guest author

NAECRicardo Herranz, Managing Director, Nommon Solutions and Technologies, Madrid

We are living in the era of cities: more than 50% of the world population is already living in urban areas, and most forecasts indicate that, by the end of this century, the world’s population will be almost entirely urban. In this context, there is an emerging view that the global challenges of poverty eradication, environmental sustainability, climate change, and sustainable and secure energy are all intimately linked to cities, which are simultaneously places where these global problems emerge and solutions can be found. In the short term, cities are facing the major challenge of overcoming the financial and economic crisis and emerging stronger from it. In the long term, they need to deal with structural challenges related to globalisation, climate change, pressure on resources, demographic change, migration, and social segregation and polarisation. Many of these challenges are shared by cities from developed and developing countries, while others depend on geographical, institutional, socioeconomic and cultural differences.

When addressing these problems, policy makers and society at large face a number of fundamental problems. The many components of the urban system are strongly interwoven, giving rise to complex dynamics and making it difficult to anticipate the impact and unintended consequences of public action. Cities are not closed systems, but they are part of systems of cities. Urban development policies are subject to highly distributed, multi-level decision processes and have a profound impact on a wide variety of stakeholders, often with conflicting or contradictory objectives.

In the past few years we have seen the emergence of concepts such as the smart city, urban informatics, urban analytics and citizen science, which are seen to hold great promise for improving the functioning of cities. However, arguably most of this potential still remains to be realised. The concept of the smart city has been coined as a fusion of ideas about how information and communication technologies can help address critical issues relating to cities. Essential to this concept is the notion of an integrated approach to the synergies and trade-offs between different policy domains that are closely interrelated, but have traditionally been addressed separately, such as land use, transport and energy. This integrated approach would be facilitated by the ability to analyse the increasingly large data streams generated by the ubiquitous sensorisation of the built environment and the pervasive use of personal mobile devices. In parallel, smart devices and social media are also producing new forms of public participation in urban planning. The opportunities are vast, but so are the challenges.

Much hope has been placed in the explosion of big data for establishing the foundations of a new science of cities. During the last 20 years, the dominant trend in urban modelling has changed from aggregate, equilibrium models to bottom-up dynamic models (activity-based and agent-based models) that seek to represent cities in more disaggregated and heterogeneous terms. This increasing model sophistication comes with the need for abundant, fine-grained data for model calibration and validation, hindering the operational use of state-of-the-art modelling approaches. The emergence of new sources of big data is enabling the collection of spatio-temporal data about urban activity with an unprecedented level of detail, providing us with information that was not available from surveys or census data. This has already yielded important practical advances in fields like transportation planning, but it is more questionable, at least for the moment, that big data has produced substantial advances in our understanding of cities. In principle, the potential is there: while research on cities has historically relied on cross-sectional demographic and economic datasets, often consisting of relatively small samples, we have now large-scale, detailed longitudinal data that can allow us to test new hypotheses about urban structure and dynamics. On the other hand, there is a risk that big data leads to a shift in focus towards short-term, predictive, non-explanatory models, abandoning theory. Connecting the smart city and big data movements with the knowledge developed in the last decades in fields like regional science, urban economics and transportation modelling appears as an essential condition to overcome this problem and take advantage of the opportunities offered by big data for the formulation of better theories and policy approaches.

Both empirical work and theoretical advances are needed to cope with the new challenges raised by energy scarcity and climate change, emerging technologies like self-driving cars, and the changes in social relationships, the new activities and the new forms of sharing economy enabled by social media and electronic communications, among other factors that are leading to profound changes in urban structure and dynamics. Equally challenging is to integrate data and models into governance processes: policy assessment and participatory planning are still largely based on qualitative considerations, and there is a sense that state-of-the-art urban models are immature with respect to institutional integration and operational use. New forms of data sharing and visualisation, digital participation and citizens’ engagement are promising tools to tackle this question, but here again, we still have to figure out how to share data and specialised knowledge in a form that fluidly intersects participatory decision making process and bridges the gap between implicit and explicit knowledge. Recent advances in areas such as network theory, agent-based computational modelling and group decision theory, and more generally the intrinsically holistic and eclectic approach advocated by complexity science, appear as a suitable framework for the development of a new science of cities which can in turn lead to new advances in the way cities are planned and managed, allowing us to address the enormous challenges related to urban development in the 21st century.

Useful links

OECD work on urban development

The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

 

Complexity Theory and Evolutionary Economics

26 September 2016
by Guest author

NAECRobert D. Atkinson, President, Information Technology and Innovation Foundation

If there was any possible upside from the destruction stemming from the financial crisis and Great Recession it was that neoclassical economics’ intellectual hegemony began to be more seriously questioned.  As such, the rising interest in complexity theory is a welcome development. Indeed, approaching economic policy from a complexity perspective promises significant improvements.  However, this will only be the case if we avoid a Hayekian passivity grounded in the view that action is too risky given just how complex economic systems are. This would be a significant mistake for the risk of non-action in complex systems is often higher than the risk of action, especially if the latter is informed by a rigorous thinking grounded in robust argumentation.

The flaws of neoclassical economics have long been pointed out, including its belief of the “economy as machine”, where, if policymakers pull a lever they will get an expected result. However, despite what Larry Summers has written, economics is not a science that applies for all times and places. It is a doctrine and as economies evolve so too should doctrines. After the Second World War, when the United States was shifting from what Michael Lind calls the second republic (the post-Civil War governance system) to the third republic (the post-New-Deal, Great Society governance structure), there was an intense intellectual debate about the economic policy path America should take.  In Keynes-Hayek: The Clash That Defined Modern Economics, Nicholas Wapshott described this debate between Keynes (a proponent of the third republic), who articulated the need for a larger and more interventionist state, and Hayek (a defender of the second republic), who worried about state over-reach. Today, we are in need of a similar great debate about the future of economic policy for the emerging “fourth republic.”

If we are to develop such an economic doctrine to guide the current socio-technical economic system, then complexity will need to play a foundational role.  But a risk of going down the complexity path is that proponents may substitute one ideology for another.  If today’s policy makers believe that economic systems are relatively simple and that policies generate only first-order effects, policymakers who have embraced complexity may believe that second, third, and fourth order effects are rampant. In other words, the butterfly in Mexico can set off a tornado in Texas. If things are this complex, we are better off following Hayek’s advice to intervene as little as possible. At least with a mechanist view, policymakers felt they could do something and perhaps they got it right.  Hayekian complexity risks leading to inaction.

This gets to a second challenge, “group think.”  Many advocates of complexity point to complex financial tools (such as collateralized debt obligations, CDOs) as the cause of the financial crisis.  Regulators simply didn’t have any insight because of the complexity of the instruments.  But these tools were symptoms.  At the heart of crisis, at least in the United States, was mortgage origination fraud.  The even more serious problem was intellectual: virtually all neoclassical economists subscribed to the theory that in an efficient market, all the information that would allow an investor to predict the next price move is already reflected in the current price. If housing prices increase 80 percent in just a few years, then their actual worth increased 80 percent. So any reset of economics has to be based not just on replacing many of the basic tenants of neoclassical economics, it has to be based on replacing a troubling tendency toward group think.  Yet, replacing the former may indeed be harder than the latter.

So where should we go with complexity? I believe that a core component of complexity is and should be evolution.  In an evolutionary view, an economy is an “organism” that is constantly developing new industries, technologies, organizations, occupations, and capabilities while at the same time shedding older ones that new technologies and other evolutionary changes make redundant. This rate of evolutionary change differs over time and space, depending on a variety of factors, including technological advancement, entrepreneurial effort, domestic policies, and the international competitive environment.  To the extent that neoclassical models consider change, it is seen as growth more than evolution. In other words, market transactions maximize static efficiency and consumer welfare. As Alan Blinder writes, “Can economic activities be rearranged so that some people are made better off, but no one is made worse off? If so we have uncovered an inefficiency. If not, the system is efficient.”

In complexity or evolutionary economics, we should be focusing not on static allocative efficiency, but on adaptive efficiency. Douglass North argues that: “Adaptive efficiency…is concerned with the kinds of rules that shape the way an economy evolves through time. It is also concerned with the willingness of a society to acquire knowledge and learning, to induce innovation, to undertake risk and creative activity of all sorts, as well as to resolve problems and bottlenecks of the society through time.” Likewise, Richard Nelson and Sidney G. Winter wrote in their 1982 book An Evolutionary Theory of Economic Change, “The broader connotations of ‘evolutionary’ include a concern with processes of long-term and progressive change.”

This provides a valuable direction. It means that a key focus for economic policy should be to encourage adaptation, experimentation and risk taking. It means supporting policies to intentionally accelerate economic evolution, especially from technological and institutional innovation. This means not only rejecting neo-Ludditism in favor of techno-optimism, it means the embrace of a proactive innovation policy. And it means enabling new experiments in policy, recognizing that many will fail, but that some will succeed and become “dominant species.” Policy and program experimentation will better enable economic policy to support complex adaptive systems.

Useful links

The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

 

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