Investigating Spatial Inequalities

Mobility, Housing and Employment in Scandinavia and South-East Europe

Peter Gladoić Håkansson, Helena Bohman, Peter Gladoić Håkansson, Helena Bohman

• Editore: Emerald Publishing Limited
• Anno: 2019
• ISBN:
  9781789739435
• Argomento: Diritto
• Categoria: Diritto nell'ambito dell'edilizia abitativa e dello sviluppo urbano

Seen as one explanation for the election of Trump, Brexit and the surge of a right-wing movement across Europe, spatial inequality has become an increasingly relevant topic. Offering in-depth perspectives on factors such as local labour markets, housing and mobility, this book investigates centralization tendencies in Scandinavia and South East Europe that help shape regional development and act as a catalyst to creating regional inequalities.
Joining scholars from four countries, this book provides a micro-examination of the development of regional inequalities in four geographically peripheral countries which represent different ends of the income spectrum, contain both EU and non-EU members and reflect differing levels of economic development. Divided into three sub-themes, the sections in turn discuss the topics of spatial divergence and labour market development, housing and institutional perspectives, and finally mobility, migration and commuting.

Chapter 1

Introduction: Spatial Inequalities in the Age of Rapid Technological Advances

Helena Bohman, Peter G. Håkansson and Inge Thorsen

Regional inequalities have long been a major concern of EU regional policy. Growing differences between urban and rural regions have recently been used to explain a source of political discontent underlying the election of Trump, Brexit, and the surge of extreme, right-wing, and xenophobic movements across Europe. Whereas many urban areas see their population and economies growing, other, often rural, areas suffer from an ageing and shrinking population – resulting in challenges associated with providing basic services of health, education, and mobility. Not surprisingly, urban populations often report an optimistic view on the future, whereas populations in more rural areas are more pessimistic and sceptic towards politicians and present politics. Understanding the spatial dynamics of economic development is therefore of vital interest. Income inequalities tend to be wider across regions within countries than across countries, but this differs. For example, disparities in income per capita are a lot wider across regions in the European Union than across states in the United States (Puga, 2002). This also applies for unemployment, which has become increasingly polarized in the European Union in the last decades (Puga, 2002). Important reasons for this are the wide-ranging changes that the world economy has experienced during the last decades. Digitalization, globalization, and educational upgrading have, for many, led to increased wages, less routine work, and overall increased wealth. However, there are also less desirable outcomes: divergence between urban and rural locations, labour market polarizations, and increased inequality within countries. One explanation is that regional labour markets have specialized in work that is not equally disposed to technological complexity – reflecting differences in industrial and occupational structures, the skill mix of the work force, organization of work, and the extent to which new technology is already present in the local economy (see, e.g., Berger & Frey, 2016).

Changes in the occupational structure caused by adopting new technologies seem to have reinforced existing spatial inequality. New jobs are created in cities with high concentration of highly skilled workers, while locations with low density of high-skilled workers experience job losses (Berger & Frey, 2016). When this structural change hits an area negatively, people may lose their jobs, but the overall negative development often also leads to decreased house prices. Hence, people in lagging areas may become ‘double losers’ when losing both income and the value of their house.

Accordingly, technology’s potential to substitute work is an important issue that largely influences different positions and perspectives of the regional labour markets today. Vast literature shows that recent technological change has been skill-, routine-, and capital-biased (see, e.g., Berger & Frey, 2016). Digitalization tends to substitute for workers engaged in routine tasks carried out by well-defined procedures. On the other hand, tasks that require intuition, creativity, complex social interaction, and higher levels of perception and manipulation are still difficult to automate. In many advanced economies, there have been significant expansions of employment at both ends of the skill spectrum, at the expense of employment in middle-skill occupations. The surge in low-skill service jobs can be explained by the fact that higher incomes increase the demand for services requiring low-skilled workers, and the manual, non-routine tasks that are prevalent in service occupations are not easily substitutable by computers (Autor & Dorn, 2013; Berger & Frey, 2016). However, rapid technological advances (artificial intelligence, use of big data, sophisticated algorithms, robotics, etc.) will probably soon enable automatization at an even wider scale, making low-skilled workers even more vulnerable.

Spatial Mobility and Inequality

Numerous studies in recent years have analysed local labour markets, for example, in relation to cultural diversity (Suedekum, Wolf, & Blien, 2014), mobility, over-education and educational mismatch (Croce & Ghignoni, 2015; Ramos & Sanromá, 2013), and trade and technology (Autor, Dorn, & Hanson, 2015). Even though spatial mobility in different forms is central in these studies, researchers often study mobility in isolation. Related issues concerning housing, transport, migration, and commuting decisions that may be central for the mobility of different groups of individuals are rarely analysed comprehensively.

The purpose of this book is to bring new insights into the patterns underlying inequalities across space, with a special focus on mobility and institutional rigidities that affect mobility. The idea that a lack of geographical mobility contributes to inequalities is far from new. Already Beveridge (1944) and Friedman (1968) emphasized the role of mobility. However, what is novel in this book is that it combines employment, mobility, and housing in a major comparative study across Europe and analyses the structural change of the so-called fourth industrial revolution (see, e.g., Schwab, 2016).

This book provides comparative analyses between four countries in Scandinavia and South East Europe (SEE) – Sweden, Norway, Serbia, and Croatia. The choice of countries may seem odd at a first glance; however, they are all countries in the more peripheral parts of Europe, and they all have a history of relatively extensive welfare states and egalitarian ambitions. In terms of differences, Sweden and Norway belong to the richest parts of Europe, whereas Serbia and Croatia belong to the poorer parts. This provides an opportunity to analyse the extent to which some differences may be the outcome of a country’s or region’s stage of economic development. We consider this a ‘most-different’ approach. Moreover, selecting two countries from each region allows us to investigate the role of economic structure within the two groups. For example, Sweden and Norway share many characteristics, but their geography and economic base are quite different. The most obvious example of economic difference is perhaps Norway’s revenue from oil, but the geographical differences also give quite different preconditions – for example, infrastructure investments. The same argument applies for Serbia and Croatia. Thus, the choice of countries enables analysis using both ‘most-different’ and ‘most-similar’ approaches.

Although Europe is quite strong in ICT development, these strengths decline as we move to the Southern and Eastern peripheries of the EU (Leontidou, Afouxenidis, Gialis, & Stringli, 2013). In addition, there are large differences in the share of high-tech employment between regions in Europe: while the regions in Western and Northern Europe generally have higher high-tech intensity, the share is much lower in some Southern and Eastern European regions (Goos, Konings, & Vandeweyer, 2015). Thus, spatial inequality is not only a question for local labour markets; it also concerns the inequality between the North West and the South East of Europe.

The slow pace of convergence reinforces internal migration. A growing literature argues that spatial labour mobility is one of the primary mechanisms through which metropolitan areas adjust to changes in local economic conditions (Blanchard, Katz, Hall, & Eichengreen, 1992; Gallin, 2004; Saks, 2008). In addition, since new technology jobs mostly cluster in high-skilled cities, low-skilled workers will inevitably have to follow – making economic activity increasingly geographically concentrated, increasing housing prices and other living costs, and resulting in increased inequality and risk of poverty in these cities despite the growing GDP per capita. Furthermore, only around 3 per cent of the world’s population – about 210 million people – are international migrants (Geddes & Korneev, 2015; World Bank, 2011), and most migratory flows happens within the country (Bell et al., 2015). Therefore, internal migration, which result from labour market polarization and regional development dynamics, is one of the most fundamental responses to technological transformations’ effects on employment.

Spatial labour mobility can occur through either migration or commute. The decision to move or commute is connected to the housing market. Both issues will be analysed and described in the book from a socio-economic and gender perspective. Theoretically, the relation between housing prices and distance commuted has well-known trade-offs. Extensive out-migration from a local market will probably also lead to decreasing house prices, which will affect the migration decision. The relationship between home ownership and unemployment rates, at the individual or regional level, has been extensively discussed (e.g., Blanchflower & Oswald, 2013; Coulson & Fisher, 2009; Dohmen, 2005; Lux & Sunega, 2012; Munch, Rosholm, & Svarer, 2006; Oswald, 1996). This literature was largely propelled by Oswald’s argument that reduced mobility associated with home ownership creates labour market inefficiency and higher unemployment rates. Empirical findings indicate that declining home prices are associated with reduced geographic mobility of homeowners (Chan, 2001; Ferreira, Gyourko, & Tracy, 2010). This causes people to become trapped in peripheral, low-income areas – becoming ‘double losers’ as the loss of the house value prohibits them from adapting and seeking higher income jobs. Similarly, research conducted on the Spanish labour market showed that increasing provincial house prices significantly decreases the probability of migration (Palomares-Linares & van Ham, 2016).

The digitalization of the economy is evidenced by internet use and access, e-governance, individuals’ digital skills, and enterprises use of ICT. In all these indicators, Sweden and Norway are ahead of Serbia and Croatia (Eurostat, 2017). When it comes to economic structure, close to 80 per cent of all employees in Sweden and Norway work in the service sector, while the rate is 64 per cent in Croatia and 56 per cent in Serbia (ILO, 2017). Differences in economic structure and wealth create incentives for international migration because of wage gains from emigration. This makes depopulation trends strong in SEE, as opposed to modern Scandinavia. However, Scandinavia experienced depopulation around 100 years ago in the emigration wave to North America. This experience may provide insights into how SEE countries can reverse the tide of emigration. There are also similarities between the two regions. For example, low population density and a positive attitude towards the welfare state.

What Is Inequality?

Inequality is becoming a central topic in economics after decades of, if not being neglected, at least being a peripheral topic. One reason for this previous disinterest is that neoclassical theories have predicted convergence between regions, since poorer countries are supposed to grow at a faster rate than richer countries (Solow, 1956). Although there has been an intense debate on convergence, the expectation that inequality is a temporary problem has minimized interest in the topic. Inequality seems to have increased in popularity again, which is illustrated by the successes of Piketty (2014) and Stiglitz (2012). Concerns about the negative effects of globalization, the uneven benefits of the transition of socialist economies, and the recent financial crisis have renewed the debate (Wei, 2015). However, while economists have paid less attention to individual inequality, regional and spatial inequality have been discussed among economic geographers and others (Wei, 2015). Accordingly, there is a large number of theories, sometimes contradicting, that can be analysed and applied.

But what is inequality? More specifically, what is spatial inequality? Often when we talk about inequality, we mean individual income inequality. However, income is a broad term and inherently difficult to measure. What do we mean by income? Piketty shows, for example, that the result changes if capital gains are included. Income statistics are also prone to different types of measurement errors, as depicted by the large literature on the topic (see, e.g., Deaton, 1997). When it comes to spatial inequality, the definitions become even more blurry since we have to deal both with the spatial dimension and with the unevenly distributed variable. In this book, we use employment, unemployment, population growth, and income (regional GDP) as the variables.

When investigating spatial inequality, it is usually necessary to use geographical aggregations. Aspects of spatial inequality may be subordinate to personal inequality and fairness, and authorities in some countries may not be very concerned about a development involving centralization and depopulation of peripheral rural areas. Centralization and agglomerations arguably promote increased efficiency to the benefit of the society as a whole. However, there are objections against increased centralization. One objection is related to the diseconomies of agglomeration – represented, for instance, by pollution, congestion, and costs of living. Another objection concerns interdependencies in location decisions of firms and households.

However, market incentives are one thing. Another is institutional and political decisions on location that may enhance or slow down agglomeration forces. As Puga (2002) puts it, when firms and households move, they do not take into account possible losses for those left behind. Such network externalities should be accounted for when designing an adequate regional policy. McArthur, Thorsen, and Ubøe (2014) demonstrate that ignoring the effect of negative network externalities may lead to the closure of schools, kindergartens, or the local grocery shop – thereby triggering a self-enforcing depopulation from rural areas. In a spatial general equilibrium framework, McArthur et al. (2014) identify the existence of multiple equilibria and demonstrate how a failure to account for interdependent location decisions may take the economy over a bifurcation point, into an equilibrium state where peripheral areas are more or less totally depopulated. In evaluating aspects of personal fairness, it is important to recognize how individual opportunity sets are influenced by collective decisions on, for instance, the spatial distribution of jobs and the construction of a satisfying transportation infrastructure.

Kanbur and Venables (2005) add other arguments in favour of a balanced regional economic development, stating that ‘spatial inequality is a dimension of overall inequality, but it has added significance when spatial and regional divisions align with political and ethnic tensions to undermine social and political stability’ (p. 3). In addition to representing an important dimension of individual inequalities, unbalanced regional development has also been fuelling ethnic tensions and forces of segregation – for example, in Catalonia (Spain) and Eastern Ukraine – as Lessmann and Seidel (2017) point out. This is an argument for striving for regional balance and equity in addition to being concerned with inequalities and fairness at an individual level.

According to Puga (2002), ‘European regions experienced a clear convergence in income per capita up until the late 1970s, when convergence came to a sudden stop’. He also acknowledges that income inequalities tended to be wider across regions within countries than across different countries. Objective 1 of the European Structural Funds is about ‘promoting the development and structural adjustment of regions whose development is lagging behind’ (Puga, 2002). A few decades ago, around two-thirds of the EU funding was allocated to this ...