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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 Mexico. 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

 

Governing Education in a Complex World

23 September 2016
by Guest author

NAECTracey Burns, Project Leader, OECD Directorate for Education and Skills

The famous slogan “KISS” urges listeners to “Keep it simple, stupid!” However, modern policy making is increasingly discovering that not keeping it simple – in fact, embracing the complex – is essential to understanding contemporary systems and making reform work.

Modern societies are made up of a growing number of diverse stakeholders who collaborate through formal and informal channels. The rapid advancement and reach of information and communication technologies has enabled them to play a much more immediate role in decision-making while at the same time the delivery of public services has become more decentralised.

This complexity brings a series of dynamics that the traditional policy cycle is not able to capture. This is not startling news: numerous critics have described the inadequacy of the traditional policy cycle in agriculture, medicine, and education for the last 30 years. What has changed, however, is a growing understanding across a much broader set of actors that we can no longer continue to operate using traditional linear models of reform.

This is not just a theoretical discussion: ignoring the dynamic nature of the governance process makes reform less effective. In education for instance, even very similar schools can react quite differently to the same intervention. A case study of the Netherlands demonstrated how some weak schools benefitted from being labelled as in need of improvement, coming together as a school community to set off a virtuous cycle to improve performance. In contrast other schools struggled when faced with the same label, with some descending into vicious cycles where teachers felt unmotivated, parents moved their children to another school, and overall performance declined. A simple model of reform and governance cannot account for this complexity.

How can complexity be identified? A seminal 2002 paper by Glouberman and Zimmerman distinguishes between three types of problems: the simple, the complicated, and the complex. A simple problem is, for example, baking a cake. For a first time baker, this is not easy, but with a recipe and the ingredients you can be relatively sure that you will succeed. Expertise here is helpful, but not required.

In contrast, a complicated problem would be sending a rocket to the moon. Here, formulas are essential and high level expertise is not only helpful, but necessary. However, rockets are similar to each other in critical ways, and once you have solved the original complicated problem, you can be reasonably certain that you’ll be able to do it again.

Both simple and complicated problems can be contrasted with a complex problem, such as raising a child. As every parent knows, there is no recipe or formula that will ensure success. Bringing up one child provides useful experience, but it is no guarantee of success with another. This is because each child is unique and sometimes unpredictable. Solutions that may work in one case may only partially work, or not work at all, in another.

Returning to the failing school example, it was the unpredictability of the dynamics inherent in the response of the schools and their communities that rendered the problem complex as opposed to merely complicated. Acknowledging the complexity inherent in modern governance is thus an essential first step to effective reform. Successful modern governance:

  • Focuses on processes, not structures. Almost all governance structures can be successful under the right conditions. The number of levels, and the power at each level, is not what makes or breaks a good system. Rather, it is the strength of the alignment across the system, the involvement of actors, and the processes underlying governance and reform.
  • Is flexible and able to adapt to change and unexpected events. Strengthening a system’s ability to learn from feedback is a fundamental part of this process, and is also a necessary step to quality assurance and accountability.
  • Works through building capacity, stakeholder involvement and open dialogue. However it is not rudderless: involvement of a broader range of stakeholders only works when there is a strategic vision and set of processes to harness their ideas and input.
  • Requires a whole of system approach. This requires aligning policies, roles and responsibilities to improve efficiency and reduce potential overlap or conflict (e.g. between accountability and trust, or innovation and risk-avoidance).
  • Harnesses evidence and research to inform policy and reform. A strong knowledge system combines descriptive system data, research findings and practitioner knowledge. The key is knowing what to use, why and how.

Creating the open, dynamic and strategic governance systems necessary for governing complex systems is not easy. Modern governance must be able to juggle the dynamism and complexity at the same time as it steers a clear course towards established goals. And with limited financial resources it must do this as efficiently as possible. Although a challenging task, it is a necessary one.

Useful links

The Simple, the Complicated, and the Complex: Educational Reform through the Lens of Complexity Theory

Governing Education in a Complex World

Education Governance in Action: Lessons from Case Studies

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

Achieving and sharing the benefits of globalisation

21 September 2016
by Guest author

Catherine L. Mann, OECD Chief Economist, and Ken Ash, Director of the OECD Trade and Agriculture Directorate. Today’s post is also being published by OECD Ecoscope

Today’s OECD Interim Economic Outlook warns that trade growth is slowing, contributing to another slowing of global GDP growth in 2016 and with few signs of improvement for 2017. Does it really matter? If we believe the current anti-trade, anti-globalisation rhetoric, we might shrug our shoulders and say “no”. Trade has been so maligned and demonised, some might even be pleased.

But that would be the wrong answer. Open trade and cross-border investment are key vectors for diffusion of new technologies and competition, which are central to achieving productivity gains and improving well-being. New research published today by the OECD in conjunction with the Interim Economic Outlook suggests that a substantial part of the post-crisis slowdown in total factor productivity growth could be reversed if trade intensity were to recover. In short, weak trade is one of the factors that will keep the economy in a “low-growth” trap where sluggish trade and investment lead to diminished growth expectations and rising financial risks.

Over decades, trade has been responsible for drawing hundreds of millions of people out of poverty – and we mean one and two-dollar-a-day poverty – in emerging and developing countries. Trade could perform this same miracle for the many millions still living in abject poverty in poor countries in Asia and Africa, if other conditions are also right of course.  Salaries and working conditions are almost always better in companies that trade than in those that do not, and this is true in countries at all levels of development. Households gain hugely from trade because it increases choice and reduces prices.

The prospects of millions of workers in the global economy depend on their participation in global value chains, as highlighted by statistics developed by the OECD with the WTO on Trade in Value-Added (TiVA).  The main insight from these data is first, in order to export efficiently, a company has to also import efficiently. A second key insight is the importance of high quality services to support trade and trade-intensive activities.  It should be of great concern that there are signs that the development of global value chains appears to have gone into reverse in recent years.

The OECD paper published today looks at the reasons for the trade slowdown and back-tracking in the development of global value chains. Several factors are at play, some of them cyclical in nature, others structural like the changing role of China in the global economy. Increasingly murky protectionism is contributing to the slowdown, as is the failure to implement any really ground-breaking global new trade initiatives for more than a decade. Without entering into a rather futile debate about when the slowdown really started or the exact contribution of structural versus cyclical drivers, let us instead ask what governments can do to reverse it.

The OECD Interim Economic Outlook calls for implementation of a package of measures to boost demand, including through collective fiscal action focussed on raising investment and productive spending, and structural reforms. Removing barriers to trade and creating the conditions for people to reap the potential benefits of trade should be at the heart of the structural reform agenda.

First, governments should put their weight behind efforts to further lower trade barriers and unnecessary trade costs by implementing the Trade Facilitation Agreement, vigorously pursuing the reduction of restrictions on services trade, including by concluding the trade in Services Agreement (TISA), co-operating to reduce costly and unnecessary regulatory differences, concluding the Agreement on Environmental Goods, and by coming to the table to deliver a good result at the 11th WTO Ministerial Conference a little over a year from now.  They should reduce remaining barriers to foreign direct investment. There are unilateral, bilateral, plurilateral and multilateral channels available if governments want to provide those growth opportunities that are currently lacking.

Second, governments need to step in to ensure that the benefits of trade are fairly shared. Governments should help those affected by the churn and disruption caused by globalisation. Benefits from trade are diffuse and long-term in nature. Losses tend to be sharp and very concentrated on individuals and regions. The people most affected are sometimes those with the least capacity to adjust. An unemployed steel worker does not take much comfort from knowing that programmers in Silicon Valley are thriving, or that T-shirts and smartphones are cheaper. What he or she needs is a decent job, new training and skills, and a robust social safety net to help through the transition.

Making trade work better for more people is not just about persuading them, although clearer and more honest communication is important. It is about ensuring that the full panoply of structural policies is put to work to ensure that people are able to reap the benefits that more open trade, technology, and investment will bring. This means paying attention to infrastructure, well-functioning financial markets, education and skills, clear and transparent institutions and rule of law – all the things that make an economy nimble Trade policy cannot be made in a vacuum but rather must be part of the fabric of domestic policies.  If we are not able to do this, growing public scepticism, particularly in the most advanced economies, may mean that further market opening will be difficult, if not impossible. Such a result would impoverish many across the world.

Useful links

 

Economic complexity, institutions and income inequality

20 September 2016

NAECCésar Hidalgo and Dominik Hartmann, Macro Connections, The MIT Media Lab

Is a country’s ability to generate and distribute income determined by its productive structure? Decades ago Simon Kuznets proposed an inverted-u-shaped relationship describing the connection between a country’s average level of income and its level of income inequality. Kuznets’ curve suggested that income inequality would first rise and then fall as countries’ income moved from low to high. Yet, the curve has proven difficult to verify empirically. The inverted-u-shaped relationship fails to hold when several Latin American countries are removed from the sample, and in recent decades, the upward side of the Kuznets curve has vanished as inequality in many low-income countries has increased. Moreover, several East-Asian economies have grown from low to middle incomes while reducing income inequality.

Together, these findings undermine the empirical robustness of Kuznets’ curve, and indicate that GDP per capita is a measure of economic development that is insufficient to explain variations in income inequality. This agrees with recent work arguing that inequality depends not only on a country’s rate or stage of growth, but also on its type of growth and institutions. Hence, we should expect that more nuanced measures of economic development, such as those focused on the types of products a country exports, should provide information on the connection between economic development and inequality that transcends the limitations of aggregate output measures such as GDP.

Scholars have argued that income inequality depends on a variety of factors, from an economy’s factor endowments, geography, and institutions, to its historical trajectories, changes in technology, and returns to capital. The combination of these factors should be expressed in the mix of products that a country makes. For example, colonial economies that specialised in a narrow set of agricultural or mineral products tend to have more unequal distributions of political power, human capital, and wealth. Conversely, sophisticated products, like medical imaging devices or electronic components, are typically produced in diversified economies that require more inclusive institutions. Complex industries and complex economies thrive when workers are able to contribute their creative input to the activities of firms.

This suggests a model of heterogeneous industries in which firms survive only when they are able to adopt or discover the institutions and human capital that work best in that industry. According to this model, the composition of products that a country exports should tell us about a country’s institutions and about the quality of its human capital. This model would also suggest that a country’s mix of products should provide information that explains inequality and that might escape aggregate measures of development such as GDP, average years of schooling, or survey-based measures of formal and informal institutions.

With our colleagues from the MIT Media Lab, we used the Economic Complexity Index (ECI) to capture information about an economy’s level of development which is different from that captured in measures of income. Economic complexity is a measure of the knowledge in a society that gets translated into the products it makes. The most complex products are sophisticated chemicals and machinery, whereas the least complex products are raw materials or simple agricultural products. The economic complexity of a country depends on the complexity of the products it exports. A country is considered complex if it exports not only highly complex products  but also a large number of different products. To calculate the economic complexity of a country, we measure the average ubiquity of the products it exports, then the average diversity of the countries that make those products, and so forth.

For example, in 2012, Chile’s average income per capita and years of schooling ($21,044 at PPP in current 2012 US$ and 9.8 mean years of schooling) were comparable to Malaysia’s income per capita and schooling ($22,314 and 9.5), even though Malaysia ranked 24th in the ECI ranking while Chile ranked 72nd. The rankings reflect differences in these countries’ export structure: Chile largely exports natural resources, while Malaysia exports a diverse range of electronics and machinery (see illustration here). Moreover, these differences in the ECI ranking also point more accurately to differences in these countries’ level of income inequality. Chile’s inequality as measured through the Gini coefficient (0.49) is significantly higher than that of Malaysia (0.39)

We separated the correlation between economic complexity and income inequality from the correlation between income inequality and average income, population, human capital (measured by average years of schooling), export concentration, and formal institutions. Our results document a strong and robust correlation between the economic complexity index and income inequality. This relationship is robust even after controlling for measures of income, education, and institutions, and the relationship has remained strong over the last fifty years. Results also show that increases in economic complexity tend to be accompanied by decreases in income inequality.

Our findings do not mean that productive structures solely determine a country’s level of income inequality. On the contrary, a more likely explanation is that productive structures represent a high-resolution expression of a number of factors, from institutions to education, that co-evolve with the mix of products that a country exports and with the inclusiveness of its economy. Still, because of this co-evolution, our findings emphasize that productive structures are not only associated with income and economic growth, but also with how income is distributed.

We advance methods that enable a more fine-grained perspective on the relationship between productive structures and income inequality. The method is based on introducing the Product Gini Index or PGI, which estimates the expected level of inequality for the countries exporting a given product. Overlaying PGI values on the network of related products allows us to create maps that can be used to anticipate how changes in a country’s productive structure will affect its level of income inequality. These maps provide means for researchers and policy-makers to explore and compare the complex co-evolution of productive structures, institutions and income inequality for hundreds of economies.

Useful links

This article is based on Linking Economic Complexity, Institutions and Income Inequality, by D. Hartmann, M.R. Guevara, C. Jara-Figueroa, M. Aristarán, C.A. Hidalgo.

The Atlas of Economic Complexity

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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