OECD Regional Outlook 2019

Chapter 2. Adapting regional development policy to future megatrends

Abstract

This chapter presents an overview of some of the most important megatrends that will affect regional policies over the coming years. It describes how these trends are felt today and how they are likely to evolve in the future. Based on this analysis, the chapter proposes strategies to adapt the policy-making process to future-proof regional policies. It focuses on the question of how to strengthen governance systems to take coming trends into account.

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

A rapidly changing world requires constant reforms to keep public policy up-to-date. While change is not a new phenomenon, its pace is accelerating in many important dimensions. Several emerging global megatrends will have substantial implications for economies and societies across the OECD. New technologies are introduced and adopted rapidly and transform how people live and work. Climate change is likely to intensify and population ageing is starting to be felt in many places. All these changes require policy responses to ensure that the opportunities they present are used and their downsides mitigated. Policy makers will need to adjust and reform established policies more and more quickly to keep pace with these emerging megatrends.

Regional imbalances in economic development have received considerable attention. While many factors cause regional economies to diverge, global megatrends of recent decades have been one important factor. Regional economies have been affected by globalisation and the shift from manufacturing to service sector activities. Regions that adapted well to these changes have been economically successful, whereas those that struggle to adapt lag behind. This chapter shows that future megatrends are just as likely to affect regional development, potentially with even more severe effects. They will provide opportunities to regions that adapt well, but present severe threats to those that cannot adjust.

This chapter presents an overview of some of the most important megatrends that will affect regional policies over the coming years. It describes how these trends are felt today and how they are likely to evolve in the future. Based on this analysis, the chapter proposes strategies to adapt the policy-making process to future-proof regional policies. It focuses on the question of how to strengthen governance systems to take coming trends into account. It does not discuss policy responses to individual megatrends, which will be discussed in detail in Chapters 3 and 4 of this report. These chapters also contain a more detailed discussion of the megatrends that are mentioned throughout this chapter.

Different dimensions of global megatrends

Many global megatrends have been discussed extensively in the public debate. Automation, climate change and ageing are all topics that receive considerable attention in the academic literature as well as in policy debates. There is little disagreement that these factors will have significant effects on national economies and the well-being of people.

The 2019 Regional Outlook puts a special focus on an aspect that has been much less frequently discussed: the regional dimension of global megatrends. Many global megatrends will not affect countries uniformly. On the contrary, it is rare that any trend has identical effects across all regions of a country, let alone across the globe. Even trends that do not seem to have an intrinsic regional dimension produce regionally differentiated outcomes when they interact with regional circumstances. For example, while many new technologies become available simultaneously throughout a country, the way these technologies are used can vary substantially between rural and urban areas. As a consequence, policy responses need to be adapted to regional circumstances and co‑ordinated across all levels of government.

Box 2.1. The objectives and limitations of this report

This report has the objective to help policy makers to prepare for policy challenges that lie ahead and be ready to act when necessary. It discusses likely future megatrends that have a strong subnational dimension and therefore require responses from all levels of government. The report highlights the most important megatrends with regional dimensions that are currently ongoing or emerging. It develops the key implications for regional policy and presents policy recommendations for the scenarios it discusses.

Large parts of Chapters 3, 4 and 5 concern future developments. These chapters, however, are not an attempt to predict the future. It is likely that some megatrends discussed in this report will unfold in a fundamentally different way than expected. Throughout the report, potential future scenarios are mentioned. Policy makers should not take the scenarios as blueprints on which to base policies.

All scenarios presented in this report are based on an assessment of the academic and policy debate. The accuracy of these assessments varies as features of some megatrends are easier to foresee than others. As a consequence, some assessments are likely to remain valid for many years, whereas others will be outdated sooner. For example, it is possible, with relatively high accuracy, to predict demographic trends over one or two decades, but any prediction of the precise nature of technological change over the same time period will be much less accurate. Therefore, the analysis of the effects of technological change will have to be updated within a few years as new information on technological trajectories becomes available.

Even if some of the megatrends discussed in this report will look fundamentally different than expected, it does not invalidate the importance of discussing them today. For all scenarios discussed in this report, there are experts who consider it highly likely that the scenarios will come to pass. Given the potentially drastic consequences of many of the scenarios, policy makers should start preparing for them today despite the uncertainty around them. It is always preferable to plan ahead for a scenario that does not happen than to be unprepared in case it does happen after all.

The regional dimension needs to be at the forefront of policy making when addressing megatrends

The need to develop place-based responses to global megatrends becomes apparent when their region-specific effects are considered. Climate change is an example of a megatrend whose effects vary strongly from region to region (see Chapter 4). In some regions, the most urgent consequences will be increasing hazards for people’s health and safety that need to be addressed by policy makers. Many cities can expect more frequent and more severe heatwaves, which increase health risks for vulnerable population groups that do not have access to air conditioning. In other cases, the economic consequences of climate change will play a large role. For instance, regions whose economy is based on winter tourism might experience serious economic disruption from warmer winters with less snow. In other regions, the preservation of fragile habitats and the threat of loss of biodiversity might be the main concern. Lastly, in some regions, all three elements can be a serious concern. For example, low-lying coastal regions can expect more frequent flooding that puts lives at risk, creates significant economic damage and destroys vulnerable habitats.

Other megatrends will have similarly diverse effects across regions. OECD (2018_[28]) shows that the number of jobs that are at high risk of automation varies strongly from region to region. In many countries, more exposed regions have 50% more jobs at high risk of automation than less exposed regions. In a few countries, the difference is even close to 100%. Thus, the economic challenges from automation will have fundamentally different magnitudes across regions and policies need to be adjusted accordingly.

Too often, national or global trends are generalised with little regard for actual trends at the regional level. For example, urbanisation is a major global trend and ongoing population flows into cities are common in most OECD countries. However, this general trend should not obscure the fact that 20% of urban areas in OECD countries shrunk in population size between 2000 and 2014. There is little reason to expect that this pattern will change in the future. National policies that are tailored only to growing cities will be inadequate for shrinking cities and could harm urban areas that are already struggling.

Considering regional differences is even more important when planning ahead. In hindsight, regionally differing trends are often obvious and it is clear that bespoke strategies would have been needed to respond to them. However, this is not the case when looking ahead. Using the above-mentioned example of urbanisation, it might not be obvious in the context of a fast urbanising country such as the People’s Republic of China (hereafter “China”) that shrinking cities could soon be common. However, it is likely that – once the urban population share in China has plateaued – similar population dynamics as in OECD countries set in and a significant minority of cities will start to loose population. In fact, evidence suggests that this process has already started (Long and Wu, 2016[36]). Understanding this scenario and being able to recognise the first signs of a long-term population decline could help cities to prevent costly policy mistakes, such as investments in unneeded infrastructure.

What changes are likely to come?

Coming megatrends with important implications for regional policy can be divided into three groups. First, technological change will affect regional economies profoundly. Its impact will be felt beyond the economy as many new technologies will be used in daily life. Adapting policies to these new technologies will necessitate reforms in a wide range of policy areas, including tax policies, labour market policies and regulatory policies. Given that many of these policies have an important regional dimension, it is no surprise that regional development policies will also be strongly affected. Second, demographic changes will affect most regions in OECD countries. A major demographic concern in many OECD countries is ageing, but regional demographic patterns are complex due to differences in birth rates as well as domestic and international migration. In almost all OECD countries, there are regions that are ageing and losing population and regions that are gaining new working‑age residents. Often, the former regions are rural areas whereas the latter regions are large urban areas. Third, environmental changes are driven by the human impact on the natural environment. Climate change is the biggest concern, but other environmental changes have profound regional impacts, too. For example, the global acidification of oceans will affect coastal economies.

Technological change

The first major type of changes will stem from new and improving technologies. New technologies will not only affect the daily lives of people, but will also transform how regional economies operate. They will create important opportunities to make economies more productive and improve quality of life. However, many benefits of technologies do not emerge automatically, but require complementary policies, to ensure for instance, that people have the right skills to use the technologies. Furthermore, new technologies require adequate regulation to encourage their rapid diffusion and to limit their possible unintended negative consequences.

Compared to demographic and environmental changes, technological change can occur more rapidly and is therefore less predictable. For example, it took only a few years from the introduction of the smartphone to its widespread adoption. Applications based on smartphone technology gain popularity even faster, sometimes becoming widespread within a few months. While not every new technology will be adopted as fast as smartphones or smartphone applications, it is highly likely that digital technologies in particular – which have very low marginal costs of production and distribution – will continue to spread quickly.

Several technologies that have potentially large effects on regional economies and societies are currently in advanced stages of development or in early stages of market introduction. These include virtual and augmented reality techniques, additive manufacturing (3D printing), autonomous vehicles (self-driving cars), and unmanned aerial vehicles (drones) (OECD, 2016[37]). Furthermore, industrial robots will continue to gain importance in manufacturing processes. As the subsequent sections point out, these technologies will have greatly varying impacts across different regions.

Effective regulation of new technologies must address the fact that they will be used differently in different regions and will have different impacts depending on the regional environment. For example, camera-equipped drones to monitor crops on fields do not pose the same risks to safety and privacy as camera-equipped drones in urban areas. More generally, regulation needs to be sufficiently differentiated to be adequate for regional conditions, but needs to be sufficiently harmonised across regions in order not to create barriers to a widespread adoption of new technologies. This requires the devolution of some regulatory competencies to lower levels of government or alternatively, the inclusion of place-dependent provisions in national regulations, while ensuring ongoing co-ordination of regulation and preventing the overlap of regulatory functions across levels of government (Rodrigo, Allio and Andres-Amo, 2009[38]).

Besides regulating new technologies adequately, policy makers at all levels of government have to respond to the economic transformations that new technologies induce. In this context, technologies that allow the automation of tasks which are currently completed by people will be of particular importance. This includes autonomous vehicles and other technologies that have been mentioned above. However, the technology with the largest potential for automation is artificial intelligence (AI). If AI evolves as rapidly as predicted by some experts, it will completely revolutionise the economy by making humans redundant in a wide-range of jobs (Brynjolfsson, Rock and Syverson, 2017[39]). The consequences of this potential wave of automation and their regional implications are discussed in page 57.

Box 2.2. Blockchain technology for smart regional and local governments

Blockchain and distributed ledger technologies (DLTs) have the potential to transform the functioning of a wide range of industries. DLTs are one of the most disruptive innovations currently shaping the global economy, as they allow an immediate and secure digital transfer of value and ownership in total transparency within a network. Information stored on the public ledger is verified through a cryptographic consensus protocol pre-defined among a group of users, decentralising the decision power among all the nodes of the network. The technology has all the characteristics of a general-purpose technology, which means it is pervasive, improvable over time and able to open up the field for complementary innovations.

The advancement of DLTs constitutes an opportunity for regional and local governments. DLTs are still at an early stage of development, but in recent years blockchain projects have been launched or tested in relevant areas of subnational public administrations such as healthcare, education, secure identity management, shared mobility, energy, land and property registration, automated local tax payments, and water distribution (Grech and Camilleri, 2017[40]). The rate at which entrepreneurs and administrations are experimenting with this technology around the world suggests that it could become mainstream in many domains (Benna, 2018[41]).

Dubai’s administration launched the Dubai Blockchain Strategy, partnering with IBM and Consensys, which aims at delivering “more seamless, safe, efficient and impactful city experiences” through blockchain-based applications and to transform the city into the first “blockchain powered government”. To this end, the government created a USD 275 million start‑up investment fund for blockchain proof of concepts and is working on putting government records on distributed ledgers. Blockchain technology implemented to handle visa applications, bill payments and licence renewals is expected to save up to 25.1 million hours of document processing time (Smart Dubai, 2018[42]).

Another example is Singapore, which has been identified as one of the main cities in the world in terms of initial coin offerings and blockchain-related start-ups (Cohen, 2018[43]). Its GovTech office is exploring various blockchain use cases, the government is establishing a blockchain innovation centre, and the Monetary Authority of Singapore and the Singapore Exchange are looking at blockchain technology in order to create a secure platform for selling tokenized securities.

Demographic change

Rising life expectancy is one of the greatest achievements of human civilisation. Since 1970, life expectancy in OECD countries has increased on average by more than ten years and human welfare has improved drastically through longer and healthier lives (OECD, 2017[44]). Moreover, people who stay healthier for longer are able to contribute longer to society. Average life expectancy at birth in some OECD regions exceeds 84 years. Life expectancy at age 65 is even higher, implying that a large part of the population in OECD countries can expect to live for more than 20 years after retiring. Yet, even though life expectancy has been rising almost everywhere, there is large variability across regions in many countries (Figure 2.1).

Figure 2.1. Life expectancy at birth in TL2 regions, 2016

Note: 2016 or latest available year: data for Australia are for 2015; data for Canada are for 2014, for Japan 2010; for Korea 2014; New Zealand for 2013; and for the United States for 2010.

Source: Calculations based on OECD (2019), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.

Statlink

Rising life expectancy also creates new challenges, especially in regions with low birth rates or population outflows. These regions have to develop new models engaging older residents productively in the economy and helping them to age in place. Policy makers have to adjust services to the needs of an ageing and potentially declining population and compensate for declining tax revenues due to a lower share of economically active residents. Population flows create further challenges that need to be addressed. Regions that experience strong population outflows possibly in combination with low birth rates often struggle to ensure the continued provision of services. Furthermore, these regions have to deal with other challenges, such as preventing blight in neighbourhoods with significant population outflows. In contrast, regions with population inflows face the opposite challenges, including how to provide services to newcomers, how to build sufficient new housing and, if population growth is due to international migration, how to integrate new arrivals.

Demographic changes can be disaggregated into natural population changes and population changes due to domestic or international migration. In most OECD countries where natural population changes will play a major role, the main concern is low birth rates and population ageing. However, the trend is far from uniform. Some OECD countries, such as Israel, record high birth rates and natural population growth. Within countries, moreover, there can be significant variation in fertility rates across regions. For example, in the United States, fertility rates across different states varied between 1.54 in Rhode Island and 2.26 in South Dakota in 2016 (Martin et al., 2018[45]).

Population flows in OECD countries are often driven by economic opportunities (Chapter 4). People will continue to move from regions with weak economic prospects to economically successful regions. Since it is mostly young people that relocate, this trend will have consequences on the age distribution. By 2050, the share of people aged 65 or older is projected to be 8% higher in European regions whose per capita gross domestic product (GDP) is in the bottom 25% of their country than in regions whose per capita GDP is in the top 25% of their country.

Even though demographic change tends to evolve slowly over decades, recent developments are not always good guides for future changes. Some countries that experienced very little population decline in recent years are likely to experience significant population decline over the coming decades. In the Netherlands and Germany, well below 10% of all regions experienced population declines during the period 2014-17.1 However, population projections show that by 2050, 55% and 79%, respectively, of regions in those two countries are projected to have a lower population than in 2014 (Eurostat, 2016[46]).

Environmental change

Environmental changes will be among the most important trends over the coming decades. The overarching concern in this respect is climate change, but other developments such as a loss of biodiversity or pollution are also highly important in some regional contexts. Environmental changes stand out from other megatrends discussed in this report because they are predominantly threats that have few upsides for humanity. Most other megatrends offer opportunities and challenges at the same time and it is the task of policy makers to ensure that the upsides dominate. In contrast, regional policy related to climate change mostly needs to focus on mitigation and adaptation in order to ensure that the consequences do not become too costly.

Without counteracting policies, global temperatures are likely to increase by more than 4°C by 2100 (IPCC, 2014[47]) (IPCC, 2018[48]). The consequences from such unchecked climate change will be dramatic. They will include extinction of up to 40% of terrestrial species and widespread food insecurity (OECD, 2012[49]). Extreme weather events and natural disasters will increase with corresponding human and monetary losses (IPCC, 2014[47]).

To avoid such catastrophic outcomes, regional and local governments have a series of important levers. Fifty-seven per cent of all public investment in OECD countries is undertaken by subnational levels of government (OECD, 2018[50]). Using this financial capacity to pursue climate-friendly investments is a key condition to limit global warming to 2°C. Investments into energy efficiency, renewable energy and sustainable transport need to be pursued at all levels of government (see also Chapter 4) (OECD, 2017[51]). Beyond dedicated investments in climate change mitigating infrastructure, climate considerations need to be mainstreamed into all investment decisions.

In addition to climate change mitigation at the regional level, regions will have to adapt to climate change. As highlighted in the introduction to this chapter, there is no single strategy for effective adaptation to climate change. The regional effects of climate change vary strongly across regions. Adaptation policies have to respond to the specific combination of threats that climate change poses to each region.

In order to take the right investment decisions for climate change mitigation and adaptation, a number of conditions have to be in place. Most fundamental are appropriate governance arrangements to co-ordinate policies across levels of government and across neighbouring local governments. Furthermore, appropriate long-term planning processes have to be in place. For example, long-term land-use planning needs to ensure not only that infrastructure will have a low lifetime carbon footprint, but also that it is resilient to expected climate change (Chapter 4).

Automation will have important consequences for regional economies

Automation due to technological progress presents considerable opportunities and considerable challenges at the same time. On the one hand, continued technological progress and the resulting automation of economic activities is highly beneficial in many respects. Automation is a key driver for productivity growth, which is the most important long-term determinant of economic growth, and the most important source of long-term wage growth. Consumers benefit from automation because it reduces the prices of many goods and increases purchasing power. Thus, the benefits of automation can be felt by everybody.

On the other hand, automation has important downsides because it leads to job losses for some workers and can greatly disrupt the business models of firms. In aggregate, the downsides of automation are outweighed by its benefits. However, the negative consequences of automation are not evenly distributed across society: some social groups are more strongly affected than others. For many low-skilled manual workers, the downsides of automation may be felt more strongly than the benefits.

Automation not only affects social groups differently; it also has very different effects on different regions. In some regions, the share of jobs at high risk of automation is as low as 4% whereas in others it is close to 40% (OECD, 2018_[28]). In regions where automation occurs gradually and continuously, it is an important source of productivity growth. In these regions, lost jobs are typically replaced with new ones that have been created by firms that become more competitive due to higher productivity. However, in regions where automation occurs rapidly and is unevenly spread across firms, the downsides can outweigh the benefits. In these regions, lost jobs can often not be replaced quickly enough, leading to high unemployment. Likewise, more firms may go out of businesses than are replaced by new start-ups, leading to a deterioration of the overall business environment in the region.

Policies to ensure that automation is beneficial for regions need to be place-based. They have to encourage ongoing innovation within the region to facilitate gradual automation and productivity growth and pre-empt disruptive changes with negative effects. To do so, policies have to be tailored to the specific strengths and weaknesses of a region, for example taking into account its sectoral composition; skill levels in the workforce; relations between public actors, businesses and research institutions; as well as its geographical location.

The effects of automation can be felt today, but they are likely to increase in the future. Furthermore, potentially rapid automation creates two risks. First, if technological progress leads to the rapid automation of many jobs, there is a risk that lost jobs cannot be replaced quickly enough with new jobs. Second, there is often a gap in the skills profile between jobs lost due to automation and newly created jobs. Thus, there is a risk that rapid automation leads to high unemployment during long transition periods. Some estimates suggest that close to 50% of jobs are at a high risk of automation (Frey and Osborne, 2013[52]). Recent OECD work puts the number of jobs at high risk of automation at 14% (Nedelkoska and Quintini, 2018[53]). These aggregate numbers hide significant subnational variation. As highlighted above, the number of jobs at high risk of automation varies by a factor of ten between the least and most affected regions within OECD countries.

Unemployment is not the only consequence of automation that threatens to increase inequality. There is also a risk that technological progress will supress wages for large parts of the population. Technological progress not only increases the number of tasks that can be completed by machines, it also reduces the costs of these machines. Thus, the substitutability between human labour and machine labour will increase, while the marginal costs of machine labour will decrease. In other words, human workers will compete increasingly with machines for jobs, but machines will become cheaper over time (OECD, 2018[54]). For example, an accountant whose current competitive advantage is that he or she lives within driving distance of a commercial hub, will increasingly have to compete with accounts around the world, who will be able to communicate and interact efficiently from distant locations through telepresence technologies (Baldwin, 2019[55]). Or else, an account whose primary competitive advantage is the ability to detect complex patterns in financial data, will have to compete with machine-learning software that is potentially cheaper (ibid.).

Machines are not only becoming better at doing tasks that only humans could do previously, they are also becoming cheaper. Thus, even if machines could not become better in doing human tasks, they would be used more simply because they become cheaper. This twofold competition from machines will not only put downward pressure on wages in jobs where machine labour is a direct substitute for human labour; it will also put pressure on wages in all sectors where workers have similar skills. This trend is already observable. Median wage growth has been lower than productivity growth since 1995. As a consequence, the labour share (i.e. the share of net national income that is received as labour compensation) has declined by 3.5 percentage points, from 71.5% to 68% (OECD, 2018[54]).

The decline in labour share is partly due to the emergence of new dominant firms at the technological frontier that are highly capital-intensive and have low labour shares. As will be discussed in the following sections, new digital technologies are likely to increase the importance of such firms and put further pressure on the labour share.

The information and communications (ICT) sector stands out from other sectors because markets in it tend to be dominated by a few highly productive firms, which can distribute their products globally at very low marginal costs. Thus, the most productive firms that develop the best product tend to capture a large market share. In addition, network effects and economies of scale can lead to natural monopolies (OECD, 2017[56]). To ensure ongoing competition and prevent a loss of consumer welfare, competition policy needs to prevent the emergence of such monopolies.

In recent years, in particular the importance of owning data has been highlighted as an important reason for the emergence of natural monopolies. Many recent products and algorithms in the ICT sector rely on large volumes of data for their development, and their refinement increases with greater data availability. This creates a self-perpetuating advantage for dominant firms that can collect more data from their users than competitors with a smaller user base (Furman and Seamans, 2018[57]).

Artificial intelligence could be a key technology in the future

In the future, the importance of digitalisation will increase, even though the magnitude and speed of this increase is subject to debate. A decisive factor in determining the importance of the digital economy will be the role of artificial intelligence.

The term artificial intelligence describes a set of technologies that allow machines to mimic cognitive functions. Currently, the technology is used in a wide range of contexts, but mostly for selective applications such as pattern recognition. Nevertheless, many economists and computer scientists predict that it will soon become a general-purpose technology (Klinger, Mateos-Garcia and Stathoulopoulos, 2018[58]) (Brynjolfsson, Rock and Syverson, 2017[39]). If this will be case, AI could have considerable economic consequences.

A general-purpose technology is a technology with a range of characteristics which makes it particularly well-placed to generate longer term productivity increases and economic growth across a range of industries (OECD, 2010, p. 7[59]). In other words, a general‑purpose technology is a technology, such as the wheel or electricity generation, that in itself forms the basis for new technologies. By enabling a large number of subsequent innovations in diverse areas, general-purpose technologies are highly disruptive for the entire economy.

Some of the technologies that artificial intelligence will enable have already emerged. For example, image recognition is well advanced. Error rates are approximately at human levels and improving fast. While image recognition does not necessarily appear to be a key technology, it has important implications. Among them are self-driving cars that can identify images of their surroundings from cameras, radar and LIDAR,2 and respond appropriately (Brynjolfsson, Rock and Syverson, 2017[39]). Self-driving cars technology will trigger large productivity gains in the transport sector and will affect many important aspects related to the functioning of cities (Chapter 3). However, if AI becomes a general-purpose technology, self-driving cars would be just one of many future innovations that are based on the technology. A wide range of further technologies, many of which are not yet imagined, could also be based on it.

If artificial intelligence becomes a general-purpose technology, it will change the economy profoundly. It would lead to an increased risk of unemployment because of automation and would put downward pressure on wages for a broad share of the population. Moreover, a large share of the value added would be derived from the algorithms behind the technology. Since these are likely to be owned by a limited number of companies for the above-mentioned reasons, economic concentration could increase unless counteracting policies are implemented.

At this point, there is no consensus that AI will become a general-purpose technology. While many experts expect AI to have dramatic effects, others doubt that the technology in its current form has the potential to be used beyond specific applications. Sceptical economists, for instance, raise the question of why AI has not led to measurable productivity growth despite its enormous progress in recent years (Furman and Seamans, 2018[57]). A growing number of computer scientists and engineers directly involved in developing AI argue that the current technology faces inherent limitations that restrict its applications. They predict that such limitations cannot be overcome by an evolution of current technologies. Instead, continued progress would require a fundamental redesign of basic methods. As of today, it is unclear if and how fast these methods can be developed (Marcus, 2018[60]).

Rapid, widespread adoption of artificial intelligence would pose severe challenges for many regions

For individual regions, the consequences of the emergence of AI as a general-purpose technology could be even more dramatic than for countries. Under a scenario of a rapid adoption of AI, a number of compounding factors would affect regional economies and the capacity of many regions to respond to it.

First, regions would be affected by job losses of varying magnitudes. OECD (2018_[28]) shows that there are significant differences in the share of jobs at risk of automation across regions. If AI is rapidly adopted in many economic sectors over the coming years, it is likely that most of the jobs currently considered at risk of automation would be lost quickly. Given that today’s projections of jobs at risk of automation are based on the most likely evolution of artificial intelligence, a more rapid spread of the technology would probably lead to even greater job losses. Thus, it is particularly important for regions with a high share of jobs at risk of automation to track the evolution of AI to be aware of the potential risks from it.

Second, the creation and provision of AI algorithms would most likely capture a significant share of value added. This would come at the expense of more traditional economic activities such as manufacturing, but could also affect other activities such as the provision of intellectual services. For example, already today, some back office legal services are being replaced by artificial intelligence algorithms (Barton, 2016[61]). This will harm regions that have a strong base in these activities without corresponding strengths in ICT development related to them. It would also affect the tax revenues of regional and local governments that rely on business taxes by affecting the profits of firms in those sectors.

Third, the shift in value-added creation could severely disrupt the business models of many firms to the point that they go out of business. This would lead to increased unemployment even among workers whose jobs are not directly affected by automation. Furthermore, it would have important feedback effects for suppliers of those firms. Since these are frequently based in the same region, the disruptive effects on regional economies would go beyond the firms directly affected by the emergence of AI.

Fourth, AI that disrupts existing modes of production will reshape global value chains. At this point in time, any detailed prediction of how global value chains will evolve if artificial intelligence becomes a general-purpose technology is impossible. However, it is likely that regions relying on the provision of cheap labour for their position within global value chains will be most profoundly affected. One of the consequences of AI is that human labour will decline in relative importance in the production process. Any competitive advantage due to cheap labour costs will be less important if automation becomes more relevant. Thus, it seems likely that firms will base their location decisions less on the availability of cheap labour and more on other factors, such as market access. This could have profound consequences, for example, for regions in Latin America that currently rely on cheap labour as their comparative advantage.

Without counteracting policy measures, the consequences of a rapid, widespread adoption of AI could be a further polarisation between few regions that dominate the technological frontier in the field and a large number of regions that would struggle to keep pace economically. Klinger, Mateos-Garcia and Stathoulopoulos (2018[58]) find that already today research activity on AI is clustered in a few locations. Furthermore, the regional distribution of activity has become more stable since 2012. Thus, AI seems to follow a trajectory that is similar to other ICT technologies, which are also heavily concentrated in a few regions.

For regional development policy, a rapid transition to AI will create significant challenges even in regions where the benefits dominate. Regions would have to respond to the above-mentioned consequences of the (potentially disruptive) economic transition that a shift to artificial intelligence entails. This will require a variety of measures, including retraining programmes for laid-off workers and capacity-building programmes for firms to adjust to the new market environment (OECD, 2018[8]). These programmes need to be tailored to regional and local conditions instead of following national blueprints. For example, a region in Latin America that relies on its integration in a global value chain of a car manufacturer needs other policies than a region in East Asia that has an absolute advantage in manufacturing consumer electronics.

Global megatrends will be felt differently in urban and rural areas

The most important characteristic that determines how megatrends will affect a region is the region’s degree of urbanisation. Economic trends, new technologies as well as demographic and environmental changes will affect urban and rural regions in fundamentally different ways. Partly, this is because some trends will have very different characteristics in urban and rural areas. For example, most urban areas are likely to experience population inflows, whereas many rural areas are losing residents. Partly, it is because the same trend will have very different consequences in the two types of regions. A new technology such as autonomous vehicles will lead to very different outcomes in urban and rural areas even though the underlying technology will be identical. For example, urban areas face a much more severe threat from increasing congestion due to autonomous vehicles than rural areas.

The subsequent sections discuss how coming megatrends will be felt differently in urban and rural regions. However, it is important to keep in mind that there are few regions in OECD countries that are entirely urban or entirely rural. Most regions contain a mix of urban and rural areas in varying proportions. Thus, most regions will face some of the challenges and opportunities that global megatrends will pose to urban areas, just as they will face some of the challenges and opportunities that global megatrends will pose to rural areas. Furthermore, urban and rural regions do not form uniform categories. Within each class of regions, there are large variations in important dimensions, such as human capital levels, geographic location, and importantly the quality of its administration and leadership. These factors will have important influences on how regions will be affected by future developments.

How coming megatrends will affect urban areas

Cities are well-placed to benefit from future trends. They are likely to reap the largest economic benefits from new technologies that will further increase the importance of the knowledge-based service economy. Cities could also see significant improvements in quality of life due to new technologies that improve public service delivery and mitigate the negative externalities from high population densities. Last, but not least, many cities will continue to have economically favourable demographic profiles because they will continue to attract young and well-educated residents.

Yet, none of the potential benefits will accrue automatically and not all cities will benefit from them. The high density of people and economic activity in cities provides a comparative advantage in knowledge-intensive activities. Cities offer the frequent face-to-face interactions and create the knowledge spillovers that are indispensable for these activities (OECD, 2015[62]). Consequently, the concentration of knowledge-intensive services in cities is a universal pattern across OECD countries (OECD, 2018[8]). If the importance of these activities increases, cities will be the main beneficiary. However, it is unclear if newly emerging knowledge-intensive activities will be located in all cities or clustered in a few cities. As discussed above, already today there is a strong clustering of firms working on artificial intelligence in a few cities. The more strongly value creation in the future will rely on this technology (or any other single technology), the more likely it is that the economic benefits will be clustered in a few places.

Even cities that will benefit economically the most from coming megatrends will face serious challenges. A key task for them will be to avoid becoming a victim of their own success and ensure that all residents benefit from their prosperity. Unequal income distributions combined with high costs of living can make it more and more difficult for low- and middle-income households to live in economically successful cities. (OECD, 2016[63]). For example, median house prices in San Francisco exceeded USD 1.6 million in 2018 (Paragon Real Estate, 2018[64]). At these levels, adequate housing is becoming increasingly unaffordable even for upper middle-class households and is far out of reach for low-income households.

A related risk for successful cities is income segregation. Wealthier cities tend to be more segregated by income than less wealthy cities. Cities in the highest income quartile have an approximately 25% higher degree of segregation than cities in the lowest income quartile (OECD, 2018[65]). Recent seminal work by Chetty and Hendren (2018[66]) shows that such segregation has dramatic effects on the subsequent economic and social life outcomes of children growing up in disadvantaged neighbourhoods. As discussed above, technological change is likely to lead to increased labour market polarisation between a small group of highly qualified workers whose jobs cannot be automated and a larger group of less-skilled workers whose wages are suppressed due to competition from machines. In such a world, successful cities will have to increase their efforts to prevent social segregation from growing worse.

Beyond segregation, affordability, and in particular housing affordability, will continue to be a major challenge in many successful cities. Across the OECD, large urban areas have attracted population at a rate of approximately 0.9% per year since 2000 (OECD, 2018[67]). This population growth will likely continue for two reasons. First, job opportunities are likely to continue to shift towards knowledge-intensive services that are based in cities. Second, cities also host many low-skilled service jobs that provide alternative employment for workers who have been made redundant due to automation.

Currently, many cities build fewer housing units than needed for the new arrivals. To reduce market prices for housing, construction has to increase in economically successful cities. To allow low-skilled workers in the service sector to live in cities, it is furthermore important to provide sufficient affordable housing at below-market prices. Given that the urban core is largely built-up, such new housing construction has to occur through densification or – where further densification is not possible – in newly built neighbourhoods that offer good access to jobs.

Continued population growth in cities will lead to a further increase in the already high share of population in urban areas across the globe (see Chapter 4). This trend is most pronounced non-OECD countries. New OECD research indicates that in 2015, 54% of the world’s population lived in functional urban areas with more than 50 000 inhabitants. The largest of those urban areas is Greater Tokyo in Japan with a population of 36 million, followed by Greater Jakarta with 29 million inhabitants and Kolkata in India with 27 million inhabitants. However, the country where urbanisation had arguably the most transformative impact is China. For example, within a 200-kilometre radius around Shanghai, there are 62 more functional urban areas with a total population of 48 million. Together with the inhabitants of Shanghai, they form an urban megaregion with a total of more than 72 million city dwellers. If these people made up a country within the OECD, it would be the sixth-largest OECD country by population.

However, it is important to emphasise that despite overall continuing urbanisation, not all cities will grow in the future. Demographic trends in cities tend to follow economic trends. Thus, cities that will struggle economically are likely to have stagnating or even declining population levels. These cities will face a fundamentally different set of challenges. They need to scale back public services to match lower population levels and tax revenues without sacrificing quality. Cities that are affected by significant population loss have to reconvert developed land into undeveloped land to reduce costs for infrastructure maintenance and ensure the attractiveness of the urban fabric. These challenges are not a new phenomenon. Between 2000 and 2014, 38 out of 290 metropolitan areas in the OECD lost population (OECD, 2018[67]). Among urban areas of all sizes, the share of cities with shrinking population reaches 20%. To develop adequate policy responses, it is important that these population declines are anticipated through realistic population projections.

It is not only economic and demographic trends that will shape cities over the coming years. Cities will also be affected by new technologies that will profoundly alter the day-to-day lives of their residents. Many technologies have the potential to improve quality of life for residents and make cities more efficient. However, few technologies will have this effect in the absence of any government intervention. Effective regulations are key to ensuring that new technologies improve well-being in cities.

The most impactful technological development for day-to-day life in cities in the intermediate future will arguably be the emergence of self-driving vehicles (see Chapter 3). This technology will transform urban mobility patterns and will reshape how cities look. It will make commuting much more convenient than today, drastically increase mobility for residents who cannot use cars today and free up large amounts of public space that is currently used for parking. However, without guiding policy interventions, it is likely that the technology will have important downsides that could outweigh its benefits. Among the primary risks is an increase in congestion due to growing traffic as well as increasing suburban sprawl. Furthermore, many benefits will require accompanying government interventions to materialise. For example, autonomous vehicles will reduce the need for parking spaces. However, freed-up parking spaces will only be a benefit to cities if the space is put to uses that are socially beneficial.

From a public policy perspective, new technologies offer city governments the opportunity to become more efficient, more sustainable, more resilient and more responsive (see Chapter 3). The Internet of Things can help monitor natural resources consumption and improve management of resources within a systemic circular economy approach (see Chapter 4). New ICT systems make it possible to analyse information in real time. For example, cleaning agents can be deployed where the general public signals the need for it through dedicated smartphone apps. This enables the administration to respond more quickly to problems and at the same time use scarce resources where they are needed most. Smartphone technology can also be used to increase the resilience of cities. Early warning and information applications help cities to increase their disaster preparedness and can reduce the loss of lives in case of catastrophic events.

How new technologies will affect rural areas

Technological change presents a threat and an opportunity in equal measure to rural areas. On the one hand, rural areas will be threatened by an ongoing or even accelerating shift to the knowledge-based service economy described above. Rural areas rely to a much larger degree on extractive and manufacturing activities than more densely populated areas do (OECD, 2018[8]). Thus, any decline in the share of value-added obtained from these activities will harm them disproportionally. On the other hand, many new technologies can help rural regions to overcome the economic challenges that they currently face. Thereby, they can mitigate the disadvantages from an accelerating shift towards economic activities that have traditionally been based in cities. If used well, these technologies have the potential to create new economic growth in rural areas and to improve quality of life for their residents.

The primary economic challenge of rural regions is low density. Within a given area, there are fewer customers, investors, competitors, potential employees, potential employers, experts, service providers and so on. As a consequence, people and goods in rural areas have to travel longer distances, which leads to several disadvantages for firms located there. First, transport costs are high and market potential is low. This makes it difficult to compete against firms that can produce higher volumes at more strategic locations located closer to customers. Second, it is more difficult for firms to find specialised expertise, either by hiring new staff or by employing external experts. Third, the spread of new ideas and innovation that leads to agglomeration economies in cities typically takes place at a lower rate in rural areas.

Many new technologies that may emerge in the near future can help to alleviate these disadvantages. Two technologies in particular are likely to alleviate the disadvantage from long distances that are related to shipping goods. Autonomous vehicles will reduce transport costs and shipping times. Driverless trucks can run 24 hours a day and cover much larger distances than drivers who have to respect rest periods (see Chapter 3). They will not only be faster, but also cheaper than traditional trucks because of lower labour costs. Likewise, drones may soon ship small, but important, items such as spare parts or crucial components for just-in-time production (see Chapter 3). Just as driverless vehicles, this technology would increase delivery speeds and lower costs.

New communication technology is likely to overcome some of the challenges of rural areas. One of the earliest and most influential works of the Internet age was Death of Distance (Cairncross, 1997[68]). The key prediction of the book is the idea that the Internet and new communication technologies will lead to an economy in which location does not matter anymore. As is well-known by now, this prediction did not materialise. Even though better communication technology helped to overcome some of the effects of distance, it also increased the importance of knowledge-based clusters (Porter, 2000[69]). Arguably, the latter effect outweighed the distance-mitigating effects of new communication technology and led to an increased importance of location. However, further progress in communication technology offers the prospect to mitigate some of the effects of distance even if it is unlikely to completely reverse this picture. Emerging virtual reality technology could eventually be a close substitute for face-to-face business meetings (see Chapter 3). It also has the potential to further improve online education and distance learning.

3D printing can help small and medium-sized firms in rural areas that serve small markets. The technology has the potential to reduce economies of scale by making small-scale production more cost effective (see Chapter 3). Many mass production techniques require equipment such as moulds that can only be used to produce one specific type of good. Producing a different good in the same factory requires retooling, which can be slow and expensive. Thus, these production methods are only cheap if large volumes are produced. In contrast, 3D printers can produce many varieties of goods without the need for reconfiguration. They are especially beneficial to firms that produce small volumes, for example because they cater to small regional markets and are poorly placed to expand because of their geographic location.

Lastly, technology can make rural areas better places to live by improving service delivery (see Chapter 3). For example, autonomous school buses will make it easier for children to access schools. Telemedicine will improve the quality of medical service. Drone-based mail delivery might improve postal services. These developments will improve quality of life and can help to mitigate the population decline that many rural areas are facing (see Chapter 4)

Using opportunities will be crucial for the success of rural areas

The resulting picture for rural areas is mixed. The overarching trend to a knowledge-based service economy is likely to continue in the future. This will represent a challenge since knowledge-intensive services are predominantly located in urban areas. However, many emerging technologies have characteristics that make them especially valuable in rural contexts because they mitigate some of the disadvantages inherent to low densities and long distances.

Despite the advantages that new technologies offer, many regions are slow to take them up. This can be seen by the use of existing technologies. For example, ICT is used very effectively to provide remote schooling and telemedicine in some rural regions in the OECD (OECD, 2017[70]). Yet, although the underlying technologies are well-established, these methods are not used in many other regions where they could be highly beneficial. Thus, the constraining factor is not technological availability, but institutional factors such as awareness, administrative capacity and political will. For policy makers, the challenge is to ensure that the distance-mitigating possibilities of technology will be used. This will require large investments in technological infrastructure, but also in complementary policies such as education and skills training. Chapter 5 discusses how to finance these investments needs.

Megatrends will be shaped by policy

The discussion above has shown that few megatrends are unequivocally good or bad. Most offer opportunities, but also present risks. For example, automation has the potential to raise productivity and can make many jobs more pleasant by removing the need to do physically strenuous or repetitive tasks. At the same time, there is a risk that jobs will be destroyed more quickly through automation than can be replaced in other parts of the economy. As of today, it is unclear which effects will dominate and if new technologies will lead to widespread increases in prosperity or growing inequality. The broad range of possible scenarios for future trends is also reflected in polarised public opinion about many megatrends. A significant share of the public is anxious or very anxious about technological progress related to automation and artificial intelligence (McClure, 2018[71]). This contrasts with other population segments who eagerly await new technological developments.

It is not just the public that is split about the consequences of a megatrend. Expert opinions are often likewise polarised and tend to focus on either the positive or the negative dimension of a future megatrend. Some scholars expect automation to lead to jobless societies and high unemployment. Ford (2015[72]) is a prominent proponent of this theory. In contrast, others assume that technological progress and automation can drastically improve human welfare. A notable early example of such an optimistic view is Keynes (1930[73]), who argues that technological progress and automation would lead to 15-hour work weeks and a life free of material needs.

The one thing proponents of monochrome optimistic or pessimistic visions of coming megatrends often have in common is that they see them as deterministic developments that are guided by forces akin to laws of nature. According to this view, public policy can only intervene to mitigate the outcomes at the margins, but is unable to affect developments at a more fundamental level. However, this perspective tends to underestimate how external trends interact with institutions and how they can be shaped by public policies. Instead of operating in isolation, the functioning of markets fundamentally depends on the institutions and social norms that shape them (Polanyi, 1944[74]). How megatrends will play out will depend on how these institutions and social norms evolve and on the policies that are shaped by them.

In order to illustrate the practical implications of this theoretical argument, it is useful to analyse why Keynes (1930_[73]) and Ford (2015[72]) come to fundamentally different conclusions about the likely effect of automation. Despite writing 85 years apart from each other, they do not differ much in their assessment of technological progress. Both authors expect that automation will drastically increase the output of an economy. At the same time, both authors also expect that automation will lead to a decrease in labour demand because jobs lost to automation will outweigh the newly created jobs. As a consequence, they expect labour supply to exceed labour demand permanently.

Keynes’ (1930_[73]) and Ford’s (2015[72]) disagreement stems from their assessment of how labour market institutions will respond to this mismatch between labour demand and labour supply. While Keynes assumes that the remaining work will be distributed across workers, Ford argues that a lucky few will remain in full-time employment while the majority of workers will be unemployed. Yet, nothing in the nature of technological progress inherently predicts one or the other outcome. Ford admits that it is not a lack of skills that prevents a larger number of workers to participate in the labour market in his scenario. Instead, he argues that even if everybody was highly educated, only a few workers would have jobs.

The reasons for the presumed labour market responses are only briefly discussed by both authors even though they are a critical element in their predictions. Keynes (1930_[73]) argues that automation will lead to a world in which all material needs will be satisfied and “we shall endeavour to spread the […] work there is still to be done among” all workers.3 In contrast, (Ford, 2015, p. 252[72]) claims as justification for his winner-takes-all argument that “historically, the job market has always looked like a pyramid”.

Despite being considered a direct consequence of future automation, neither of the two contrasting outcomes is inevitable. Whether a world with shrinking labour demand due to automation will lead to a 15-hour work week or to a permanently unemployed underclass or to a completely different outcome depends on the collective choices made by a society. Policy choices, institutions and individual preferences are all important factors that determine the consequences of automation.

Policy makers play a central role shaping the responses to automation and other megatrends. Doing so will not always be easy. Some of the changes to the way that regional economies and societies operate will be enormous. Ensuring that technological progress will enhance overall well-being and does not lead to rising inequality will require policy responses that can seem radical. Today’s tax policies, regulatory policies and also governance arrangements have been designed with respect to how economies and societies have operated in the past. Some of them will need to be fundamentally overhauled to make them fit for the future.

For regional policy, this implies that many of its current pillars have to be reconsidered. This ranges from the economic development policy of countries to their system of multi‑level governance. In some instances, unprecedented decentralisation could be required to empower regions to respond adequately to the challenges that they are facing. In others, greater centralisation may be needed, for example to develop more effective fiscal equalisation mechanisms. Both types of decisions tend to be politically difficult and have far-reaching implications. Yet, policy makers should not shy away from them if they seek to ensure future prosperity in all regions.

Innovative governance to address megatrends

Long-term planning, projections and other foresight methods are important tools to future‑proofing regional policy making. However, they will fall short if the insights generated through them are not translated into policies. Adequate governance mechanisms are therefore important in order to design and implement policies in response to challenges that have been identified in foresight exercises. Such governance mechanisms have to evolve together with the challenges that they seek to address. This section presents pathways to adapt the governance of regional development policy to future megatrends.

As argued above, governments can play a leading role in addressing challenges associated with globalisation, climate change or disruptive technologies, rather than being side-lined by them. This requires the public sector to become more agile, experimental and innovative, especially at the regional and local levels. In this rapidly changing world, fixed rules of governance written by a hierarchical authority (e.g. “command and control” regulation) are quickly rendered obsolete on the ground. Policy makers need to act as front‑line actors to find joint solutions to common problems through experimental trial and error processes (Morgan, 2018, forthcoming[76]). Several tools can strengthen governance frameworks and make them fit to deal with future megatrends. They are discussed in more detail in Chapter 5.

Governance arrangements across countries, but also within countries, differ greatly from each other. Within many countries, subnational governments have varying degrees of autonomy, attributed responsibilities and administrative capacity. Many of the megatrends discussed above will further increase the need for differentiated governance arrangements. Yet, it is often not clear a priori which governance models are appropriate for local or regional circumstances.

Experimental governance can help to develop better models of governance through trial-and-error processes. By giving local and regional governments space to experiment, new solutions can be tested in a limited environment. If they turn out to be successful, they can be adopted more broadly. Ideally, such trials are accompanied by monitoring and evaluation processes that make it possible to identify the underlying causes for success or failure in order to maximise the learning potential from experiments.

Governance mechanisms that take behavioural insights into account are an important specific class of experimental governance. The use of behavioural insights for policy design is becoming more and more common in many OECD countries. Such policies integrate insights into how citizens behave in real-world settings, rather than relying solely on the predictions of traditional economic models based on assumptions of rational behaviour. Considering behavioural responses in policy design can help to bridge the gap between policy objectives and ultimate outcomes.

Behavioural insights can also be applied to the design of governance mechanisms and to the structure of organisations. For example, in the design of regional development policy frameworks, it is important to consider that many factors influence the actions of policy makers and policy implementers beyond what traditional models of rational behaviour can predict. Anticipating this and adjusting the framework accordingly can increase its effectiveness.

Innovative governance makes use of new technologies. New technologies provide the opportunity for governments to become more responsive, to use resources more efficiently and to streamline administrative processes. For example, the growing use of Geographical Information Systems (GIS) at the local level can help municipal governments to better allocate resources across their territory. By mapping maintenance needs or the incidence of crime, maintenance crews and police officers can be more effectively distributed throughout the territory.

A greater use of digital technology can also improve interactions between governments and citizens. Providing administrative services on line increases the convenience for citizens and can improve the efficiency of administrative processes. Furthermore, online tools allow citizens to provide immediate feedback to governments. For example, many cities have started to introduce smartphone applications that allow citizens to notify the local administration about irregularities in the public space, such as uncollected rubbish, broken street lighting or other defects in public infrastructure.

Digital technology can also enhance public participation in the decision-making process. For example, public consultations in the planning process can reach a broader audience if the possibility exists to provide feedback online instead of in hearings only. Governments also use online voting procedures to obtain feedback on proposals or prioritise spending through citizen budgeting processes.

New technologies will present further opportunities. Big data analysis will allow governments to use the vast amount of data that is constantly generated in cities. Currently, much of the data produced by cities are not analysed because the amounts of data are too vast to handle and often owned by private companies. While some of the required technology for real-time big data analysis already exists today, many applications still have to be developed.

New technologies will require reforms to subnational finance

The emergence of new technologies will affect the tax base of regional and local governments. The effects discussed above would change the distribution of labour income and affect the valuation of capital. If automation leads to growing wage inequality, income taxation will have to be adjusted to mitigate inequality and ensure sufficient revenue collection. Influential actors, such as Bill Gates, have even called for the introduction of a robot tax to slow down automation (Delaney, 2017[77]). Furthermore, many other taxes will be affected by technological change. For example, once electric vehicles become widespread, receipts from petrol taxes will decline. However, new sources of regional and local transport taxes may emerge due to the introduction of autonomous vehicles and the associated need to control traffic flows through new taxes.

If value creation becomes more concentrated in a few regions, tax revenues from business taxes and income taxes will likely undergo a similar concentration. Several steps can be taken to counteract this effect. Taxation can be shifted to tax bases that have less regional variation. Furthermore, vertical and horizontal equalisation mechanisms across regions will become increasingly important. These mechanisms can ensure that total per capita revenues of regional governments within the same country do not diverge too much from each other. They are discussed in more detail in Chapter 5.

In addition to reforms in tax policy, the financing of investment at all levels of government will have to be adopted. Enabling regions to benefit from new technologies will require large investments not only in infrastructure, but also in human capital. Further investments are needed to address other global megatrends. Combatting global warming will require investments to reduce new carbon emissions. It will also necessitate additional spending in infrastructure that is resilient to the effects of climate change.

Addressing the investment needs will require a reversal of the long-term trend of declining public investments in advanced economies that can be observed since the 1970s. Overall, public investment has fallen, from approximately 5% of GDP to approximately 3% of GDP in 2017 (OECD, 2018[50]) (IMF, 2015[78]). Since approximately 57% of all public investment within the OECD is undertaken by subnational governments, a large part of the financial burden of additional investment needs will fall on their shoulders. Financing this investment will require a better utilisation of existing funds combined with tapping new, potentially external, sources of funding, and developing new forms of financing, such as bond financing and pooled financing (see Chapter 5).

However, additional investment spending is only part of the solution. It is equally important to raise the quality of public investment. This requires improvements to the governance of public investments. For example, ex ante assessments of the benefits of an investment should be used even more routinely than they are now. Likewise, multi-year forecasting and scenario analyses should be integrated in the budgeting process for investment decisions.

How to future-proof regional policy making?

To anticipate these changes, governments have developed a variety of mechanisms to ensure that today’s policies are aligned with future developments. They include various instruments and are often, but not always, integrated in the regional planning system. Among the most important tools are forecasting and strategic foresight processes that identify trends, analyse scenarios and develop policy responses to them.

Forecasting is a data-driven activity that uses and extrapolates existing data to anticipate the future. Forecasting usually assumes that the future will follow a pattern similar to that observed in recent data. Potential breaks in the factors that drive trends are not considered. Forecasting processes, therefore, produce one main scenario about the future (with possible lower- and higher-bound estimates), where the emphasis is on the predictability, accuracy, reliability and precision of outcomes (Wilkinson, 2017[79]). However, forecasting cannot be relied on for long-term decision making under unpredictable and uncertain conditions. Given that forecasting is essentially an extrapolation of current trends, it cannot take fundamentally new trends into account. In contexts where new trends are likely to emerge, strategic foresight can help policy makers to better anticipate and prepare for different futures that are all possible and plausible, as will be discussed below (Van Duijne and Bishop, 2018[80]).

Strategic foresight to better prepare for an uncertain future

Strategic foresight is a thought-driven, planning-oriented process for looking beyond the expected future to inform decision making. It aims at redirecting attention from knowing about the past to exercising prospective judgement about events that have not yet happened (Wilkinson, 2017[79]). For example, strategic foresight does not claim predictive power but maintains that the future is open to human influence and creativity, with an emphasis – during the thinking and preparation process – on the existence of different alternative possible futures (Wilkinson, 2017[79]). This generates an explicit, contestable and flexible sense of the future, where insight about different possible futures allows the identification of new policy challenges and opportunities, and the development of strategies that are robust in face of change (Cass-Beggs, 2018[81]).

In a strategic foresight process, a manageable and memorable number of plausible stories about the future are developed, shared and contrasted in different forms – narratives, numbers and images (Wilkinson, 2017[79]). The “users” engage in regular or ongoing strategic group conversations, iterating between different ways of envisioning the future. Strategic foresight starts with defining the domain of what is being studied, the time horizon, the key issues, the stakeholders involved and the current conditions through quantitative and qualitative information (Van Duijne and Bishop, 2018[80]). As uncertainties associated with these driving forces of change come up, alternative scenarios about the future are formulated, according to three main types: 1) possible scenarios; 2) plausible scenarios; and 3) preferable or normative scenarios.

The first type of scenarios, possible scenarios, seeks to determine what is constant, what may change and what is constantly changing during the analysed time period (Wilkinson, 2017[79]). Collecting information from different sources, such scenarios are based on a systematic “horizon scan” of emerging trends, early signs of new or different possible futures, and disruptive developments that might affect their external environment. This can take the form of an open search, an expert-led scan or a data-mining meta-scan, where outputs can be presented as quantitative trends, visual maps of qualitative themes or discourse analyses. Possible scenarios, thus, help to anticipate, detect and prepare for early signals of transformations (ibid.).

Box 2.3. Horizon scanning in Canada

A possible-scenarios assessment (MetaScan 3: Emerging Technologies) was used by the Canadian government in 2013 to explore how emerging technologies will shape the economy and society, and the challenges and opportunities they will create (Padbury and Christensen, 2013[82]). The study was conducted through wide research, consultations and interviews with more than 90 experts. The key findings include some of the following policy challenges (ibid.), i.e. if the assumed possible futures materialise:

• The next decade could be a period of jobless growth, as new technologies increase productivity with fewer workers.

• All economic sectors will be under pressure to adapt or exploit new technologies, where the main characteristics of change include greater customisation, localisation and intelligence built into production and delivery. Having workers with the right skills, therefore, will be essential.

• New technologies are likely to significantly alter infrastructures for health, transportation, security and energy systems. Governments will have to decide whether to maintain old infrastructures or switch and invest in new, more efficient ones.

The second type of scenarios, plausible scenarios, construct a set of two to three plausible futures that cannot be influenced by policy makers (Wilkinson, 2017[79]). Such scenarios reflect the possible causal logics and behaviour of the wider, underlying sociological, technological and ecological megatrends, relevant to a new situation of concern. The scenarios are created via an iterative process of strategic conversation about plausibility as the guide to attention to the future (ibid.). Hence, plausible scenarios are about engaging in different perspectives, reframing and re-perceiving the policy maker’s assumptions about the future, so to consider more and better strategies that are robust in the face of change.

The third type of scenarios, preferable or normative scenarios, construct a preferred future state to determine pathways for progress (Wilkinson, 2017[79]). Transforming a vision into concrete policies is then achieved through a process of backcasting from future to present to identify the strategic priorities, goals and indicators that are relevant to attain the preferred future. Normative scenarios, thus, create a shared understanding and explicit description of the preferred future and a medium-term guideline detailing the specific policies for making progress towards the initial vision (ibid.). Within such scenarios, goal‑oriented scenarios can be used to imagine and describe the role of an organisation in a changed future world (Van Duijne and Bishop, 2018[80]).

Box 2.4. Megatrends analysis and scenario planning in the United Kingdom

A plausible scenarios-led foresight assessment (Futures of Cities) was launched by the UK Government Office for Science in 2013 to develop an evidence base on the future of UK cities (challenges and opportunities towards 2065), to inform national- and city-level policy makers (UK Government Office for Science, 2016[83]). The study was conducted through the commissioning of working papers and essays, and interactive workshops, with over 25 UK cities participating (ibid.). By combining megatrends analysis and scenarios planning, for instance, the study “produced” a plausible future consisting of considerable climate shocks presenting key urban challenges by 2065 – e.g. drier summers and heatwaves affecting the United Kingdom’s southern cities, and high levels of precipitations affecting western cities during the winter. The study, thus, suggested the importance to adapt, and develop localised ecosystem services such as green infrastructure to mitigate flood risk or have greater resilience on local energy production (UK Government Office for Science, 2016[83]).

Across the OECD, governments use several of the above forecasting and strategic foresight instruments to future-proof regional policy. The following section provides a descriptive analysis of how national and regional OECD governments prepare for future economic, technological, demographic and environmental megatrends. It is based on responses to an OECD survey provided by delegates from national governments to the OECD Regional Development Policy Committee.4

How countries use forecast and foresight

As of 2018, more than two-thirds of countries had a national long-term planning or strategic foresight unit at the centre of government (Figure 2.2). In most cases, such units provide a long-term framework, vision or strategic development plan for the country; conduct foresight activities; and co-ordinate the government’s long-term plans across different levels of government. Moreover, although they are devoted to planning at the national level, more than 90% of such units also consider regional elements and dimensions, such as divergent policy impacts across regions and different regional competitive advantages.

Figure 2.2. National long-term planning or strategic foresight unit at the centre of government

Note: Centre of government refers to a body that provides support and advice to the head of government and Council of Ministers, for example: head of the prime minister’s office, cabinet secretaries and/or secretary general of government.

Source: Calculations based on 35 country responses to the OECD Regional Outlook Survey.

Statlink

Table 2.1 shows the main tools used in forecast and strategic foresight processes. Monitoring and evaluation and SWOT (strengths, weaknesses, opportunities and threats) analysis are included, although they are not primarily forward-looking tools. They are used to obtain a better understanding of the current state of the world and to learn about the effectiveness of policies. Given that accurate forecasts require a good understanding of the present, monitoring and evaluation and SWOT analyses are essential also for forward-looking activities as they establish links between past, present and future actions.

Nearly two-thirds of the countries in the sample use both forecasting and strategic foresight in regional planning processes (Figure 2.3, left panel). The remaining third only uses forecasting. Strategic foresight is almost never used as a stand-alone planning process.5 Correspondingly, data-driven tools are more frequently used than thought-driven tools in planning processes (Figure 2.3, right panel). Trend analysis, for instance, was applied at least once in 28 of the total 35 countries. In contrast, the use of strategic foresight planning-oriented activities – due to their less clearly defined nature – varies strongly across countries. In particular, some countries have implemented large-scale strategic foresight exercises while others only use them as preparatory processes for data-driven forecasts or not at all.

Examples of countries having comprehensively applied strategic foresight processes include Canada (Box 2.3), the United Kingdom (Box 2.4) and Switzerland, which developed its report “Perspectives 2030” combining megatrends analysis and scenario planning (Box 2.5).

Table 2.1. Forecasting and strategic foresight tools

Type of planning-oriented process	Planning-oriented process tool	Definition
Monitoring and evaluation	Monitoring	Monitoring is a continuous assessment that aims primarily to provide the management and main stakeholders with indications of progress, or lack thereof, in the achievement of results (UNDP Evaluation Office, 2002[84]).
	Evaluation	Evaluation is the systematic and objective assessment of the design, implementation process and results of an ongoing or completed project, programme or policy. The aim is to determine the relevance and fulfilment of objectives, efficiency, effectiveness, impact and sustainability (UNDP Evaluation Office, 2002[84]).
Situation analysis	SWOT analysis	SWOT analysis is a framework used to evaluate a body’s internal and external environment to identify its present and future strengths, weaknesses, opportunities and threats (SWOT), before taking action (American Marketing Association, 2017[85]).
Forecasting	Trend analysis	Trend analysis is a method for understanding how and why specific things have changed, or will change, over time. To do that, it collates past and recently observed data to discover patterns or trends (Rae, 2014[86]).
	Model-based projections	Model-based projections use available historical data as inputs in statistical models to make informed estimates that are predictive in determining a future state.
Strategic foresight	Horizon scanning	Horizon scanning is an ongoing systematic process aimed at detecting early signs of new and different futures and disruptive developments (Wilkinson, 2017, pp. 15-17[79]).
	Megatrends analysis	Megatrends analysis provides a conceptual framework to think and prepare for inevitable pattern shifts that will occur in a decadal time frame, where causal logics are complex and cannot be fully known ahead of time (Wilkinson, 2017, pp. 17-19[79]).
	Scenario planning	Scenario planning involves building and using a set of plausible, alternative stories that can be used to reframe the present situation (Wilkinson, 2017, pp. 20-24[79]).

Figure 2.3. Type and tools of planning-oriented processes

Notes: Countries can use several forecasting and strategic foresight tools, for example in different reports, development plans or planning activities. Each tool was only counted once for each country if it figured in several documents.

Source: Calculations based on 35 country responses to the OECD Regional Outlook Survey.

Statlink

Box 2.5. Megatrends analysis and scenario planning in Switzerland

The first step of the “Perspective 2030” report sought to identify influencing factors, changing trends and megatrends that will impact Switzerland in the next 15 years, through online questionnaires submitted to experts and think tanks. During the second step, the surveyed experts assessed the influencing factors and trends by assigning them a value between 1 (low impact/low degree of uncertainty) and 10 (high impact/high degree of uncertainty). These assessments, therefore, identified influencing factors with a high degree of uncertainty or impact, i.e. which might require attention from policy makers. Thirdly, the assessed influencing factors and trends for Switzerland were integrated into four different plausible world scenarios, where the interaction between the Swiss and international influencing factors as well as the resulting potential “winners” and “losers” were analysed for each scenario (Swiss Federal Chancellery, 2014[87]).

For example, the “Pleins gaz” scenario supposes a world characterised by multilateralism, globalisation and economic interdependence where the world economy, under the stimulus of free trade, continues to grow. As a result of the removal of trade barriers, under the World Trade Organization’s authority, Switzerland is highly economically interconnected and stands out as a world-class research and production hub. The overall economic and technological dynamism, however, requires great efforts of adaptation by state institutions, the Swiss export economy and the population. A strong agreement clarifies Switzerland’s relations with the European Union. Differently, the “Attention, bouchon” scenario assumes a world characterised by dazzling technological progress, against the backdrop of rivalry between the United States and the People’s Republic of China (hereafter “China”). The struggle for raw materials leads to a technological competition. Transatlantic links between the United States and the EU are strengthening; at the same time, China and the Russian Federation are getting closer. Switzerland is struggling to assert its political and economic position in the world. New technologies, however, partly offset the negative effects of the decline in foreign trade, and energy consumption can be largely reduced thanks to technical progress and effective regulation (Swiss Federal Chancellery, 2014[87]).

Furthermore, in Switzerland, the Council for the Territorial Organisation (COTER), which was set up in 1997, and brings together experts, evaluates territorial developments with a view to contributing to the design and the development of policies with territorial impacts. COTER performs as a think tank for policy-makers. It relies on the Swiss Territory Project to perform the following tasks: early detection of significant changes from the territorial point of view and their influencing factors; identification of ‘blind spots’; co-ordination between public authorities and the scientific community; consideration of appropriate options for action; development of new strategies for territorial organisation; and formulation of recommendations for the implementation of the strategies. In the third year of each legislature, COTER submits a report on global megatrends to the Federal Council. The 2018 version of this report, to be published mid-2019, was entitled “What influence do megatrends have on the Spatial Development Switzerland?”

In their forecast and foresight exercises, countries frequently adopt short- to medium-term time horizons – where shorter time horizons usually correspond to forecasts, while longer ones correspond to strategic foresight exercises. In fact, more than two-thirds of countries adopted a time horizon of 1-5 years, less than half a time horizon of 11-15 years, which further declines to less than one-fifth of countries for a time horizon of 30 years or more.

Using and combining a variety of data-driven and thought-driven approaches is useful to look beyond the expected future in a more comprehensive way, to plan and prepare for different possible scenarios, and to build strategies that are robust in the face of change. Such planning-oriented forecasts and strategic foresight processes, in fact, have two core objectives. First, they help to avoid costly policy mistakes today, such as investment in infrastructure that will soon become obsolete. Second, they improve the preparedness for future challenges and help policy makers to respond when these challenges arise.

Preparing for future megatrends can have various implications. In some cases, it requires to start acting today. For example, it is still uncertain by how many degrees temperatures will rise due to climate change and how this will affect regional climate and weather conditions. However, it is a fact that climate change is occurring. As Chapter 4 points out, regions are facing the consequences of it already today and the severity of the challenge will only get worse over the coming decades. Thus, regions need to analyse the dangers that climate change poses for them and implement policies to adapt to it as soon as possible.

In other instances, preparedness means being ready to act when necessary. For example, Chapter 3 points out that many experts expect autonomous vehicles to emerge before the end of the next decade, but it is still uncertain when the technology will be ready, how quickly it will be adopted by a majority of the population and how it will change mobility patterns. Thus, it is too early to implement major changes to spatial planning policies today. Nevertheless, there is a non-negligible possibility that autonomous vehicles will become available within a few years and will quickly replace traditional cars on the road. In such a scenario, policy makers would have to respond quickly to avoid negative consequences. To be able to do so, governments should have strategies prepared that outline the most important policy responses.

And, preparedness can also mean to be aware of the latest developments without taking action today. Some of the technologies discussed in this report are still speculative at this point in time. It is difficult to predict their impact on regional economies and societies let alone the time by when they will be widespread. For example, it is unclear if and to what extent virtual reality will be able to replace face-to-face contact in daily business life as discussed in Chapter 3. However, it is clear that the technology – should it become reality – would offer profound opportunities for rural regions to attract businesses that currently locate in cities. Given the significant uncertainty around virtual reality, it is too early for rural regions to prepare regional development strategies that are based on the technology. However, policy makers should follow the developments closely to start the preparation of concrete strategies once a timeline for the introduction of it becomes clear.

Lastly, as this chapter has shown, most megatrends will have strong region-specific implications. Routinely taking into account regional elements and dimensions in the above planning-oriented processes, thus, will be critical for policy makers to develop place-based and effective responses.

References

[49] American Marketing Association (2017), Dictionary of Marketing terms, American Marketing Association, https://www.ama.org/resources/Pages/Dictionary.aspx?dLetter=S&dLetter=S (accessed on 10 December 2018).

[25] Barton, B. (2016), “Some Early Thoughts on Liability Standards for Online Providers of Legal Services”, Hofstra Law Review, Vol. 45.

[5] Global, I. (ed.) (2018), Optimizing regional development through transformative urbanization, AEGDDRD Book Series.

[9] Brynjolfsson, E., D. Rock and C. Syverson (2017), “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”, NBER Working Paper, No. No. 24001.

[33] Cairncross, F. (1997), The death of distance : how the communications revolution will change our lives, Orion Business Books.

[45] Cass-Beggs, D. (2018), Strategic Foresight for Better Policies, OECD Presentation to ETUI-ETUC Conference.

[31] Chetty, R. and N. Hendren (2018), “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects*”, The Quarterly Journal of Economics, Vol. 133/3, pp. 1107-1162, http://dx.doi.org/10.1093/qje/qjy007.

[7] Cohen, B. (2018), “These are the world's top 10 blockchain cities”, Breaker, https://breakermag.com/these-are-the-top-10-blockchain-cities/.

[42] Delaney, K. (2017), Bill Gates: the robot that takes your job should pay taxes — Quartz, https://qz.com/911968/bill-gates-the-robot-that-takes-your-job-should-pay-taxes/ (accessed on 22 October 2018).

[12] Eurostat (2016), Statistics on regional population projections.

[37] Ford, M. (2015), Rise of the robots : technology and the threat of a jobless future.

[17] Frey, C. and M. Osborne (2013), “The future of employment: How susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114/C, pp. 254-280, https://econpapers.repec.org/article/eeetefoso/v_3a114_3ay_3a2017_3ai_3ac_3ap_3a254-280.htm (accessed on 28 February 2018).

[21] Furman, J. and R. Seamans (2018), “AI and the Economy”, NBER Working Paper, No. No. 24689, http://www.nber.org/papers/w24689.pdf.

[4] Commission, E. (ed.) (2017), Blockchain in education, JRC Science for Policy Report.

[13] IPCC (2014), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, http://dx.doi.org/10.1017/CBO9781107415379.

[38] Keynes, J. (1930), “Economic Possibilities for Our Grandchildren”, in Essays in Persuasion, Palgrave Macmillan UK, London, http://dx.doi.org/10.1007/978-1-349-59072-8_25.

[22] Klinger, J., J. Mateos-Garcia and K. Stathoulopoulos (2018), Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology.

[2] Long, Y. and K. Wu (2016), “Shrinking cities in a rapidly urbanizing China”, Environment and Planning A, Vol. 48/2, pp. 220-222, http://dx.doi.org/10.1177/0308518x15621631.

[24] Marcus, G. (2018), “Deep Learning: A Critical Appraisal”, http://arxiv.org/abs/1801.00631.

[11] Martin, J. et al. (2018), “Births: Final Data for 2016”, National Vital Statistics Reports, Vol. 67/1.

[36] McClure, P. (2018), “You're Fired Says the Robot: The Rise of Automation in the Workplace, Technophobes, and Fears of Unemployment”, Social Science Computer Review, Vol. 36/2, pp. 139-156, http://dx.doi.org/10.1177/0894439317698637.

[41] Morgan, K. (2018, forthcoming), Experimental Governance and Territorial Development.

[18] Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris, http://dx.doi.org/10.1787/2e2f4eea-en.

[30] OECD (2018), Divided Cities: Understanding Intra-urban Inequalities, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264300385-en.

[1] OECD (2018), Job Creation and Local Economic Development 2018, OECD Publishing, Paris, http://dx.doi.org/10.1787/26174979.

[32] OECD (2018), “Metropolitan areas”, OECD Regional Statistics (database), http://dx.doi.org/10.1787/data-00531-en. (accessed on 2 October 2018)

[19] OECD (2018), OECD Employment Outlook 2018, OECD Publishing, https://www.oecd-ilibrary.org/docserver/empl_outlook-2018-en.pdf?expires=1531304696&id=id&accname=ocid84004878&checksum=CDE55CB66AC595E526D532736CDAF950 (accessed on 11 July 2018).

[26] OECD (2018), Productivity and Jobs in a Globalised World: (How) Can All Regions Benefit?, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264293137-en.

[15] OECD (2018), Subnational governments in OECD countries: Key data (brochure), OECD Publishing, http://dx.doi.org/10.1787/region-data-en.

[20] OECD (2017), “Big Data: Bringing Competition Policy to the Digital Era”, Unclassified report: DAF/COMP/M(2016)2/ANN4/FINAL.

[10] OECD (2017), Health at a Glance 2017: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/health_glance-2017-en.

[16] OECD (2017), Investing in Climate, Investing in Growth, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264273528-en.

[35] OECD (2017), OECD Territorial Reviews: Northern Sparsely Populated Areas, OECD Territorial Reviews, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264268234-en.

[28] OECD (2016), Making Cities Work for All: Data and Actions for Inclusive Growth, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264263260-en.

[3] OECD (2016), OECD Science, Technology and Innovation Outlook 2016, OECD Publishing, Paris, http://dx.doi.org/10.1787/sti_in_outlook-2016-en.

[27] OECD (2015), The Metropolitan Century: Understanding Urbanisation and its Consequences, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264228733-en.

[14] OECD (2012), OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264122246-en.

[23] OECD (2010), The Impacts of Nanotechnology on Companies: Policy Insights from Case Studies, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264094635-en.

[46] Padbury, P. and S. Christensen (2013), MetaScan 3: Emerging Technologies, Policy Horizons Canada, http://www.horizons.gc.ca/en/content/metascan-3-emerging-technologies-0.

[29] Paragon Real Estate (2018), San Francisco Early Autumn Report, https://www.bayareamarketreports.com/trend/san-francisco-home-prices-market-trends-news (accessed on 12 October 2018).

[39] Polanyi, K. (1944), The great transformation : the political and economic origins of our time, Farrar & Rinehart, New York.

[34] Porter, M. (2000), “Location, Competition, and Economic Development: Local Clusters in a Global Economy”, Economic Development Quarterly, Vol. 14/1, pp. 15-34, http://dx.doi.org/10.1177/089124240001400105.

[50] Michalos A.C. (ed.) (2014), Encyclopedia of Quality of Life and Well-Being Research, Springer, Dordrecht, https://doi.org/10.1007/978-94-007-0753-5_3062.

[8] Rodrigo, D., L. Allio and P. Andres-Amo (2009), “Multi-Level Regulatory Governance: Policies, Institutions and Tools for Regulatory Quality and Policy Coherence”, OECD Working Papers on Public Governance, No. 13, OECD Publishing, Paris, http://dx.doi.org/10.1787/224074617147.

[6] Smart Dubai (2018), Dubai Blockchain Strategy, https://smartdubai.ae/en/Initiatives/Pages/DubaiBlockchainStrategy.aspx.

[51] Swiss Federal Chancellery (2014), Perspectives 2030.

[47] UK Government Office for Science (2016), Future of Cities: Foresight for Cities, https://www.gov.uk/government/collections/future-of-cities.

[48] UNDP Evaluation Office (2002), Handbook on Monitoring and Evaluating for Results, http://web.undp.org/evaluation/documents/handbook/me-handbook.pdf.

[44] Van Duijne, F. and P. Bishop (2018), Introduction to Strategic Foresight, http://dx.doi.org/Future Motions.

[43] Wilkinson, A. (2017), Strategic Foresight Primer, European Political Strategy Centre.

Notes

← 1. This was partly due to the increase in migration over the time period.

← 2. LIDAR is an imaging method that uses laser to create a three-dimensional image of its surroundings.

← 3. In this statement and the related discussion, Keynes acknowledges that the emergence of the 15-hour work week is not guaranteed. However, he assumes that it will emerge because of changing human preferences once material needs are satisfied.

← 4. The analysis is based on the information directly provided by delegates from national governments, and on the information collected from the documents and websites mentioned in the surveys. Any information beyond the provided text and the documents and websites mentioned by delegates in the surveys was not taken into account so to minimise any “ease of access to information” country differences or language-related biases. To describe countries’ general approach to prepare for global megatrends, such as the level of government undertaking regional long-term planning and strategic foresight, or the type, tools and time horizon adopted in planning-oriented processes, all items of information of surveys were quantified and categorised ad hoc. Such items of information were collected systematically when they were explicitly named in the surveys and related documents and websites, or when the description of a certain process allowed their precise identification and categorisation. Reports written in languages other than English, German, Italian or French were fully translated using Google Translator.

The analysis considers the following 35 countries: Australia, Belgium, Canada, Colombia, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States.

← 5. Each country in the sample was categorised as using forecasting, strategic foresight or both, according to the tools applied in their planning processes as described in the surveys and related documents and websites – in line with endnote 4 above.