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Socio-Economic Segregation in European Capital Cities. East meets West

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DOI 10.4324/9781315758879
Publisher: Routledge
Editors
Abstract
Growing inequalities in Europe are a major challenge threatening the sustainability of urban communities and the competiveness of European cities. While the levels of socio-economic segregation in European cities are still modest compared to some parts of the world, the poor are increasingly concentrating spatially within capital cities across Europe. An overlooked area of research, this book offers a systematic and representative account of the spatial dimension of rising inequalities in Europe. This book provides rigorous comparative evidence on socio-economic segregation from 13 European cities. Cities include Amsterdam, Athens, Budapest, London, Milan, Madrid, Oslo, Prague, Riga, Stockholm, Tallinn, Vienna and Vilnius. Comparing 2001 and 2011, this multi-factor approach links segregation to four underlying universal structural factors: social inequalities, global city status, welfare regimes and housing systems. Hypothetical segregation levels derived from those factors are compared to actual segregation levels in all cities. Each chapter provides an in-depth and context sensitive discussion of the unique features shaping inequalities and segregation in the case study cities. The main conclusion of the book is that the spatial gap between the poor and the rich is widening in capital cities across Europe, which threatens to harm the social stability of European cities. This book will be a key reference on increasing segregation and will provide valuable insights to students, researchers and policy makers who are interested in the spatial dimension of social inequality in European cities. A PDF version of the introduction and conclusion are available Open Access at www.tandfebooks.com. It has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license. © 2016 selection and editorial material, Tiit Tammaru, Szymon Marcińczak, Maarten van Ham, Sako Musterd; individual chapters, the contributors. All rights reserved.
Figures
1 A multi-factor approach to
understanding socio-economic
segregation in European
capital cities1
Tiit Tammaru, Sako Musterd, Maarten van
Ham and Szymon Marcińczak
Abstract
Growing inequalities in Europe, even in the most egalitarian countries, are a major
challenge threatening the sustainability of urban communities and the competive-
ness of European cities. Surprisingly, though, there is a lack of systematic and
representative research on the spatial dimension of rising inequalities. This gap
is filled by our book project Socio-Economic Segregation in European Capital
Cities: East Meets West, with empirical evidence from Amsterdam, Athens,
Budapest, London, Madrid, Milan, Oslo, Prague, Riga, Stockholm, Tallinn, Vienna
and Vilnius. This introductory chapter outlines the background to this interna-
tional comparative research and introduces a multi-factor approach to studying
socio-economic segregation. The chapter focuses on four underlying universal
structural factors: social inequalities, global city status, welfare regime and the
housing system. Based on these factors, we propose a hypothetical ranking of
segregation levels in the thirteen case study cities. As the conclusions of this book
show, the hypothetical ranking and the actual ranking of cities by segregation
levels only match partly; the explanation for this can be sought in context-specific
factors which will be discussed in-depth in each of the case study chapters.
Introduction
Although it is often claimed that socio-economic segregation is increasing in
European cities, there is no recent internationally comparative and systematic
research into changing levels of this form of segregation. Most research on segre-
gation focuses on ethnic rather than socio-economic segregation, and although the
two are related, the latter deserves more attention in light of increasing levels of
inequality in Europe, which is also likely to be expressed spatially. Governments
all over Europe fear that in the future such socio-economic segregation may lead
to social unrest, referring to recent riots in several European cities. Although
often openly ethno-religious, the deeper underpinnings of such urban unrest stem
from rising socio-economic inequalities that are also clustered into urban space
(Malmberg et al. 2013). This book will be the first to rigorously compare levels
2 Tiit Tammaru et al.
of socio-economic segregation for a large number of European cities, and to use a
multi-factor approach to understand segregation that combines structural factors
with a context sensitive presentation of each case study city. We ask whether ris-
ing inequalities in Europe lead to what Kesteloot (2005) suggests is a shift from a
city of social class divisions to a city of socio-spatial divisions, with top, middle
and low socio-economic groups being increasingly separated from each other in
urban space.
By socio-economic segregation we mean residential segregation of population
groups based on occupation and income. In the last three decades we have seen
some remarkable changes in advanced capitalist countries, characterised by a trans-
formation from industrial to post-industrial societies, accompanied by growing
levels of liberalisation and globalisation of capital and labour flows. These changes
have impacted on occupational structures and have led to wage inequalities even
in the most egalitarian European countries (Sachs 2012; European Commission
2010). The fall of the Iron Curtain in 1989 and the demise of the Soviet Union
in 1991 integrated many former communist countries in Central and Eastern
Europe (East Europe hereafter2) into ongoing globalisation and neoliberalisation
processes. The combination of these processes with transformations from cen-
trally planned to market economies brought about a rapid decline in real per capita
income between 1988 and 1993: minus 41 per cent in the Baltic states and minus
25 per cent in the Visegrad countries (Deacon 2000: 148). Before 1989/1991, wage
inequalities in East European countries were small but have grown dramatically
since (Słomczyński and Shabad 1996; Tsenkova 2006). In addition, in most East
European countries housing was supported by state patronage under socialism,
but during the 1990s more than 90 per cent of the housing stock was privatised as
states withdrew from the costly housing sector (Hegedüs 2013).
These fundamental changes were the inspiration for formulating the over-
arching goal of this book: to provide new insights into the spatial dimension
of growing socio-economic inequalities in Europe, in the light of the processes
of globalisation, neoliberalisation and welfare state retrenchment. The book
delivers a set of theoretically informed, methodologically sound, and policy and
planning relevant systematic comparative studies that provide new evidence of
the changing levels and patterns of socio-economic segregation across a diverse
set of European cities: Amsterdam, Athens, Budapest, London, Milan, Madrid,
Oslo, Prague, Riga, Stockholm, Tallinn, Vienna and Vilnius. We focus on these
economic and political capital cities because they have been the most influ-
enced by globalisation (Beaverstock et al. 2015). Especially in East Europe,
many important changes induced by globalisation occurred in the capital cities
first (Marcińczak et al. 2015; Stanilov 2007; Smith and Timár 2010). Although
the case study cities are diverse in size, they do share similar positions in their
countries as important locations for government services, as centres for educa-
tion and jobs, and for international investments.
The study is based on a multi-factor approach that focuses on key structural
indicators shaping socio-economic segregation: globalisation, social inequali-
ties, welfare regimes and housing systems, as well as the occupational structure
Understanding socio-economic segregation 3
of cities (Hamnett 1994; Kemeny 1995; Marcińczak et al. 2015; Musterd and
Ostendorf 1998; Sassen 1991). This approach is combined with more nuanced
institutional and contextual approaches that have found their way into studies
of residential segregation (Burgers and Musterd 2002; Kazepov 2005; Maloutas
2012; Van Kempen and Murie 2009). The latter approaches emphasise the role
of local institutional, morphological, historical and spatial contexts in mediat-
ing effects of more universal/generic structural factors on patterns of segregation.
Since such city-specific factors are very important, along with the more generic
ones for understanding segregation, each chapter of this book will deliver a
detailed account of the unique features of a given case study city.
The next section of this introductory chapter contains a literature review on
the key structural factors that shape socio-economic segregation. Because cities
in East Europe are still understudied with respect to inequalities and segregation
(cf. Van Kempen and Murie 2009), we will briefly elaborate on the urban experi-
ences in the formerly state socialist countries in a separate section. The main focus
here will be on the legacies of central planning and some distinct features of the
socialist city that are important for understanding socio-economic segregation.
The importance of context will be discussed in more detail in the next section.
Then we will develop a multi-factor analytical framework that guides the analyses
of the thirteen cities in the rest of the book. The operationalisation of this frame-
work leads to a hypothetical ranking of our case study cities by their expected
levels of socio-economic segregation. We will then present the data and methods
behind our analysis in the next section. In a separate section we introduce the rest
of the book chapters, which will each deliver a contextually sensitive and empiri-
cally detailed account of socio-economic segregation in one of the thirteen cities
in 2001 and 2011 (corresponding with the years of the census in many countries).
Finally, we will reflect on the expected levels of segregation in our case study
cities. Taken together, this introductory chapter, the thirteen case studies and the
concluding chapter of this book will provide new perspectives and insights on
the evolution of socio-economic segregation and its contributory factors in major
European cities.
Literature review: structural factors shaping
socio-economic segregation
Cities are both the main drivers of innovation and economic growth, as well as
places where the biggest diversity and largest social inequalities can be found.
In this review, we focus on the key structural factors that link social and spatial
inequalities. Research on patterns of socio-economic residential segregation has
followed four important phases: the ecological approach; research on the relation-
ship between social and spatial inequalities inspired by a global city thesis; studies
that begin with the impact of welfare regimes on residential segregation; and,
most recently, studies that emphasise the importance of the contextual embedded-
ness of residential segregation (Maloutas 2012; Marcińczak et al. 2015; Musterd
and Ostendorf 1998; Van Kempen and Murie 2009).
4 Tiit Tammaru et al.
Charles Booth’s (1887) detailed social and spatial description of Tower
Hamlets in London could be considered as the beginning of a more systematic
research on segregation. Extending from description to explanation, scholars from
the Chicago School provided a human ecology framework of invasion and succes-
sion to explain the evolving segregation patterns in cities (Park et al. 1925). The
ecological approach explained the evolution of segregation by referring to natural
forces that are the same in all cities. Consequently, cities develop towards similar
spatial structures with different social and ethnic groups clustering into different
parts of the city (Häusserman and Haila 2005). The Chicago School developed an
important toolbox of segregation indices that are still used in segregation research
today (Massey and Denton 1988; Marcińczak et al. 2015; Peach 2009). Applying
these indices to real data typically reveals a U-shaped segregation pattern across
occupational groups, with the biggest spatial distance being between the highest
and lowest social categories or occupations (Duncan and Duncan 1955; Ladányi
1989; Morgan 1975; 1980). The ecological approach developed into a factorial
ecology during the post-war positivist research tradition (Berry and Kasarda
1977), and later into GIS-based studies of segregation and advanced spatial mod-
elling (Wong 2003).
Methodologically, these studies stressed that for a rigorous spatial analysis,
the units used should be internally homogeneous so that the variation of interest,
for example the distribution of socio-economic groups across the city, becomes
visible between the units as an ecological variation (Janson 1980). This indicated
the problem of how to define neighbourhoods (known as the modifiable area unit
problem) and raised the question of how the conceptualisation of neighbourhoods
affects segregation measures (Fotheringham and Wong 1991). Following the
research by Kish (1954) on within-unit and inter-unit variation of a given phe-
nomenon in and across neighbourhoods, Manley et al. (2016) make an important
methodological contribution to this book by extending the ecological tradition
into a multilevel research setting. It should also be noted that in parallel with the
advancements of the ecological approach, behavioural (Wolpert 1965) and insti-
tutional (Rex and Moore 1967) approaches towards studying segregation started
to emerge. These are beyond the scope of this book.
Income inequalities started to grow in advanced capitalist countries in the
1980s (Piketty 2013) and in Eastern Europe in the 1990s (Sztompka 1996), and
this reinvigorated the interest in relations between social inequalities and socio-
economic segregation. These relations play a major role in the social polarisation
versus professionalisation debate (Manley and Johnston 2014; Hamnett 1994;
Sassen 1991). The concentration of higher-order management and coordination
and service functions of multinational corporations into large cities is a direct
result of globalisation and economic restructuring. According to the global city
thesis this leads to social polarisation; a class of well-paid workers in the financial
and other higher-order services emerges on the one hand, and since they require
consumer services this provides jobs for many low-skilled workers on the other
hand (Sassen 1991). Others argue that professionalisation rather than polarisation
takes place in the global cities (Hamnett 1994; Préteceille 2000).
Understanding socio-economic segregation 5
The widespread pursuit of free market efficiency in the housing sector in Europe
in tandem with globalisation and economic restructuring since the 1980s, and the
retrenching of welfare states, implied significant cuts in universal housing
subsidies, privatisation of part of the social housing stock and the promotion of
home ownership (Arbaci 2007). Major changes in the housing sector started in
the UK with the right-to-buy policy in the 1980s, which led to an increase in
home ownership and a decline of the social housing sector, residualising this sec-
tor and increasing socio-housing divides (Kleinhans and van Ham 2013). This
trend of decreasing importance of social (or public) housing subsequently spread
across Western Europe (Jones and Murie 2006), while at the same time people
with a lower social status became increasingly overrepresented in social housing
(Van Kempen and Murie 2009; van Ham and Manley 2009; Manley et al. 2013).
Since social housing is often concentrated in certain parts of cities, the develop-
ments in the housing market combined with a growing social polarisation, ceteris
paribus, should lead to rising levels of residential segregation.3 Globalisation and
economic restructuring thus create socio-housing divisions that become the core
drivers of residential segregation (Figure 1.14). Further, since these changes in
the housing sector spread across most Western European countries in the 1980s,
it has been argued that this leads to increasing convergence of European housing
systems (Arbaci 2007).
European countries are still characterised by high levels of social protection
and income redistribution (although declining) that cushions the effects of glo-
balisation and economic restructuring (Musterd and Ostendorf 1998). This is
also the core argument why we find lower levels of residential segregation in
Figure 1.1 Globalisation and socio-economic segregation.
6 Tiit Tammaru et al.
Western European cities compared to USA cities (Musterd 2005; Van Kempen
and Murie 2009). Essentially, the welfare state helps to lower residential segrega-
tion through direct reductions of social inequality, or through housing policy, or
through both (Figure 1.2). The cutbacks in welfare and housing benefits across
Western Europe directly affect those who are dependent on such benefits, such as
unemployed or lower-income groups, increasingly pushing them into lower-cost
housing (Marcuse and Van Kempen 2000). Retrenchment of the welfare state, the
promotion of home ownership together with social and economic change (profes-
sionalisation) and spatial change (gentrification, suburbanisation) thus potentially
contribute to increasing levels of socio-economic segregation. The sorting of social
groups across housing and neighbourhood types increasingly emerges from market
transactions, favouring middle-income and high-income population groups over
low-income groups (Van Kempen and Murie 2009; Dewilde and Lancee 2013).
Despite converging tendencies, European countries still represent different types
of welfare regimes (social democratic, corporatist and liberal; see Esping-Andersen
1990) and housing systems (unitary, dual; see Kemeny 1995). The corporatist (and
its distinct Mediterranean variant) and social-democratic welfare types are char-
acterised by a relatively large social housing sector and unitary rental system in
which social and private renting are integrated into a single rental market, with
high levels of socio-tenure mix and low levels of residential segregation (Arbaci
2007). The liberal and Southern European welfare types are characterised by a dual-
ist rental system, in which the state controls and residualises the social rental sector
for vulnerable and low-income groups, leading to higher levels of socio-tenure seg-
mentation and residential segregation but often at a very micro scale (ibid.).
Figure 1.2 Social inequalities, welfare state and socio-economic segregation.
Understanding socio-economic segregation 7
Fenger (2007) found that East European countries are both collectively distinct
from the Western countries and different from each other. Namely, Visegrad countries
(including the Czech Republic and Hungary represented in this book) could be char-
acterised as post-socialist corporatist regimes, while Baltic countries Estonia, Latvia
and Lithuania could be characterised as post-socialist liberal welfare regimes. The
main differences between the two country groups relate to the social stratifica-
tion dimension, with higher social inequalities being found in the Baltic countries
compared to the Visegrad countries. Next, we will elaborate more on the specific
features of Eastern European cities that stem from their socialist legacy.
Eastern Europe: central planning and the socialist city
Cities in Western Europe are generally less segregated than in the USA, and the
literature provides some evidence that the communist rule and centrally planned
production and consumption policies in East European countries resulted in less
socio-economically segregated cities there in comparison to Western Europe
(Dangschat 1987; Ladanyi 1989; Musil 1987; Węcławowicz 1979). The socialist
states nationalised the means of production and had a central role in the pro-
duction and redistribution of goods, with housing being one of the most valued
(Kornai 1992; Verdery 1996). Urbanisation had a particular significance in the
formerly centrally planned countries: on the one hand, it facilitated the fulfilment
of ambitions for industrialisation and signified modernisation and progress, and
on the other hand it encouraged collective rather than individual identity with the
aim of creating a socially just society (Kährik and Tammaru 2010; Smith 1996).
A more uniform city with little economising of space and little urban diversity
emerged in countries under central planning (Gentile et al. 2012; Szelényi 1996).
The high degree of urban homogeneity was a function of the share of the urban
fabric constructed under socialism when the focus was on standardised mass con-
struction of high-rise housing estates. In the context of the pool of cities included
in this book, the imprint of the socialist legacy, for example in the form of socialist
housing estates, is larger in the Baltic capitals in comparison to Visegrad capitals
(Marcińczak et al. 2015).
When it comes to segregation, the administrative allocation of housing free of
charge (apart from cooperative housing that formed a minor part of the new hous-
ing construction) became an important tool for building the desired uniform and
egalitarian society. The production and administrative allocation of goods, which
were either free of charge or heavily subsidised, was broader than housing and
characterised many key aspects of society – ranging from subsidised food, trans-
port, guaranteed employment, adequate health and education (Deacon 2000). This
implies that state paternalistic welfare, sometimes called the totalitarian corporat-
ist welfare state (Sennett 2003), characterised countries under central planning
(Verdery 1996). With time, market elements were introduced in some domains
of the economy, first in the Visegrad countries and later, from the mid-1980s,
in the Soviet Union in the form of perestroika – the policy to restructure Soviet
political and economic systems – introduced by the Soviet leader Gorbachev.
8 Tiit Tammaru et al.
Budapest was the first to introduce market-relations in housing (Bodnár and
Böröcz 1998) and, interestingly, at the end of socialism it was the most segregated
East European capital city among the pool of cities included in this book (Kovács
2009; Marcińczak et al. 2015).
Although the mass construction of standardised housing was common in many
non-communist Western European countries in the 1960s through the 1980s, the
scale was much higher in East European cities (Gentile and Sjöberg 2013). In
other words, the redistributive effect of the state in the form of public housing
allocation (see Figure 1.2) in countries under central planning was stronger than
in the West. As a result of the construction of vast high-rise housing estates, East
European cities were characterised by less sprawl than Western cities (Bertaud
and Renaud 1997; Tammaru 2001). Moreover, the Iron Curtain sealed off Eastern
Europe from the effects of globalisation and economic restructuring, and manu-
facturing industry remained the main pillar of its economic base. The result was
a less segregated and spatially compact city with socially mixed neighbourhoods
(Marcińczak et al. 2015). In the literature, it has been labelled as a distinct city
type: the socialist city (Sýkora 2009). In comparison to many West European
large housing estates, which are often in deprived areas, similar estates in East
European cities show a much greater share of higher socio-economic groups
(Turkington et al. 2004). In the West such housing estates originally also included
middle-class families, and in some areas this is still the case, but in many places
downgrading or residualisation occurred. An important question then is whether
this downgrading of large housing estates will also occur in high-rise estates in
Eastern Europe, as some scholars already envisaged in the 1990s (Szelényi 1996).
Important changes to East European cities after the fall of the Iron Curtain
twenty-five years ago are related to the economising of space, increased social
inequality and increased urban diversity (Gentile et al. 2012; Hirt 2013; Szelényi
1996; Tsenkova and Nedović-Budić 2006). Large-scale privatisation and restitu-
tion of properties to pre-World War II owners were the two important specific
factors shaping housing markets in East European cities in the 1990s. The con-
crete spatial manifestations of such changes are under debate. Segregation could
be reduced further when those parts of the socialist city that were left in decay
under socialism – mainly higher-density, pre-World War II inner-city areas and
lower-density outer city areas – go through a process of socio-spatial upgrading,
while large housing estates start to lose the relatively high status they used to
enjoy under socialism. Eventually, this might cause these large housing estates
to take similar positions on the housing market as in the case in many Western
European cities. This in turn leads to the paradox of post-socialist transition:
despite increasing social inequalities, levels of segregation either decreased from
already low levels (Sýkora 2009; Marcińczak et al. 2013) or virtually did not
change (Marcińczak et al. 2014) in the 1990s.
This paradox, however, could be a temporary phase of urban socio-spatial
change; the evolutionary view predicts that, with time, new residential socio-economic
segregation patterns will emerge as the residential choices of managers and pro-
fessionals, as well as in situ demographic and social change (effects of changes on
Understanding socio-economic segregation 9
the labour market were more extensive compared to residential mobility in East
Europe in the 1990s), start to redefine the residential geographies of cities (Sýkora
and Bouzarovski 2012; Tammaru et al. 2016). The gentrification of inner cities
and suburbanisation of the middle class are the most eye-catching manifestations
of such spatial change that could ultimately lead to higher levels of segregation.
Such paradox is not unique to East European cities, though. Gentrification often
lowers levels of segregation in its initial phase as higher socio-economic groups
start to move into low-income inner city areas, and evidence of it can be found in
many parts of Europe (for example Leal and Sorando 2016; Musterd and Van Gent
2016). Likewise, in situ change in social and demographic structures can also con-
tribute to lowering the levels of segregation in a neighbourhood (Hochstenbach
et al. 2014), for example through professionalisation or increase of income of
people not moving or as a result of in situ demographic change. The latter can
occur as children achieve a higher occupational status than their parents but, later,
leave the parental home, move into a different dwelling in the same neighbour-
hood, or inherit housing from parents. Such social and demographic processes
within the neighbourhoods might lead to neighbourhood socio-economic mixing
in the initial phases of change. This is especially important in South European
countries where spatial proximity of family members is still very common (Leal
and Sorando 2016; Maloutas 2016; Petsimeris and Rimoldi 2016).
Comparative research on socio-economic residential segregation using recent
data is missing for East European countries, with the exception of a study by
Marcińczak et al. (2016) of five East European capital cities using data from
the 2000 census. The results of this study reveal low levels of socio-economic
segregation, reflecting the patterns of the late socialist period. Although social
inequalities rose rapidly in the 1990s, they were not directly translated into urban
space since residential mobility was still low at this time of institutional reforms
and housing privatisation (Hegedüs 2013). This book uses the most recent data
available from the 2011 census in many countries to investigate whether and how
social inequalities, created mainly in the 1990s and persisting today, start to trans-
late into urban space.
Contextual approach to residential segregation
Globalisation and economic restructuring, mediated by welfare regimes and
housing systems, affect levels of socio-economic segregation. Yet, the associa-
tion between structural factors and the level of segregation is not universal; it
hinges on cultural differences, and on the historically developed institutional
and spatial contexts of cities, where social divisions render spatial patterns in
unique ways (Burgers and Musterd 2002; Maloutas 2012). Abstract models and
universal factors simply fail to fully consider the rich reality of contemporary
cities, with all their different historical layers and contemporary diversity in their
key institutions – families, state and markets (Kazepov 2005; Häussermann and
Haila 2005). Hence, making deductions from global megatrends to exact spatial
patterns in the city should be done with great care.
10 Tiit Tammaru et al.
What are the contextual factors that are important in shaping residential seg-
regation, on top of and related to the welfare state and housing systems that we
have already discussed? Musterd and Kovács (2013) refer to specific historically
grown urban layers, while Maloutas (2012) refers to the spheres that characterise
them. The economic layer pertains to labour market changes; the state layer has to
do with the redistribution of public services; the social layer pertains to social and
family networks; and the fourth layer is about specific local socio-spatial realities
such as urban morphology. The institutional approach has made its way into the
contextual understanding of segregation patterns, too, since the local institutional
arrangements are much more complex and nuanced than the broad welfare regime
types or housing systems, including various governance arrangements and other
dimensions (Van Kempen and Murie 2009).
The local realities relate, in the first place, to historical pathways that city
regions have taken and which have led to the contemporary diversity of urban
morphology, housing stock, social class structures, etc. (Figure 1.3). Through
most of the chapters in this book, the importance of historic pathways of city
development in understanding contemporary segregation patterns is repeated.
The historic layers are further coloured by the cultural diversity of Europe. For
example, the family dimension is very important in the social and spatial rela-
tions of Southern European cities (Maloutas 2016; Leal and Sorando 2016;
Petsimeris and Rimoldi 2016). Housing in Southern European cities is often
more distant from market processes and embedded in family relations (Arbaci
2007). Even then, important differences emerge also between Southern European
cities, for example when it comes to the effects of the last cycle of housing boom
and bust and their spatial implications in the cities of Athens, Madrid and Milan
as included in this book.
Figure 1.3 Contextual approach to socio-economic segregation.
Understanding socio-economic segregation 11
The unique characteristics of place (the genius loci) are an important part of
the historic development of the city. It includes, but is not restricted to, differ-
ences in attracting various economic activities, the functioning of the welfare
regime and housing market, the match between demand and supply, quality of
the built environment, the architecture, the urban layout and the state of the urban
condition in general. These are all factors which are increasingly important assets
that make a difference between cities (Musterd and Kovács 2013). All of these
factors shift the attention away from generalised representation of national sys-
tems towards the unevenness of policy and capability associated with each city
administration (Van Kempen and Murie 2009). The very same characteristics
are also important at the intra-urban scale. The more diverse the historic layers
of the city are, the greater is the housing and neighbourhood diversity, and the
more ‘opportunities’ exist for different population groups to isolate themselves
from other groups. Furthermore, the new urban reality, with less public involve-
ment, could be characterised as a shift from government to governance. Various
governance levels (state, region, city, neighbourhood) and other actors (private
firms, inhabitants) work together in shaping the dynamics of the city and the
milieus of the neighbourhoods in increasingly complex and diverse ways (Van
Kempen and Murie 2009).
The advancement of the contextual approach to residential segregation thus
allows us to pay due attention to the historic development and urbanisation path-
ways, city-specific policies, governance, planning practices and other factors
unique to each city. Two of the four contributory factors discussed in previous
sections – welfare and housing regimes – have more to do with the contextual
factors than the more generic factors of globalisation and social inequalities.
Therefore, each of the following chapters includes a section that discusses the
context of the city under study. Still, by contrasting the evidence of thirteen cit-
ies across Europe, we want to seize the unique opportunity to shed some light
on how the key structural factors potentially shape residential segregation. We
therefore have developed a multi-factor framework for studying socio-economic
segregation in European capital cities.
Multi-factor framework of the study
Our analytical framework for understanding social segregation in European capi-
tal cities includes four key contributory factors outlined above – socio-economic
inequalities, welfare regime, housing regime and the global position of the city
in the world economy – combined with a unique profile of each city. Using these
factors as a starting point, we develop a theoretical model of levels of social seg-
regation that allows us to predict segregation in each city under study. Following
Marcińczak et al. (2016), and based on the literature review in the previous sec-
tion, for each city we quantify the theoretical level of segregation by attaching
one to three points to each factor that contributes to segregation (globalisation,
Gini index, welfare regime, housing regime, share of higher occupations). This
exercise may be overly simplistic, but it operationalises the conceptual framework
12 Tiit Tammaru et al.
underlying this book and, when viewed in a less deterministic way, allows us to
advance the debate on how the various structural factors could be related to levels
of socio-economic segregation.
The first factor that potentially has an important impact on levels of socio-eco-
nomic segregation is globalisation. The best-known classification system groups
cities into three main categories of Alpha, Beta and Gamma cities (Beaverstock
et al. 2015). Alpha cities are the most important command-and-control centres
in the global economy, and we attach the value 3 to them, indicating the highest
expected level of segregation. The least global cities are Gamma cities and we
attach the value of 1 to these, indicating the lowest expected segregation level
(Table 1.1). Since we are studying capital cities, seven of our case study cities
belong to the Alpha category (London, Amsterdam, Madrid, Milan, Prague, and
Vienna) and only two to the Gamma category (Tallinn and Vilnius).
The second factor potentially shaping levels of socio-economic segrega-
tion pertains to socio-economic inequalities. At this point it is important to
acknowledge that the five factors we use are not completely independent from
each other. The first factor – globalisation – is an important source of socio-
economic inequalities (Marcuse and Van Kempen 2000), but other factors
shape inequalities, too, including the type of welfare regime, economic struc-
ture and many others. Thus, there is no one-to-one correspondence between
the factors we use, and all of them uniquely contribute to segregation levels,
as shown in the literature review in the previous sections. The most common
indicator for characterising income-based socio-economic inequalities is the
Gini index. The values of the index as used in Table 1.1 are obtained from
around the year 2010, as is the case with the other variables (Eurostat 2015).
The literature does not provide any guidance on which values of the Gini
index could relate to high or low levels of segregation. Instead of focusing on
the values of the index we therefore opt for a relative approach that contrasts
our research cities with each other. We attach the value 3 to those cities where
the Gini index is one standard deviation above the average of our thirteen
cities, and we attach the value 1 to those cities where the value of the Gini
index is one standard deviation below the average. All other cities get the
value 2. This way, we classify London, Riga, Madrid and Athens as the most
unequal cities, and Stockholm, Milan and Prague as the most equal cities
(Table 1.1).
The link between socio-economic inequalities and socio-economic segrega-
tion is moderated by the type of the welfare regime. We use three main types of
welfare regime (Esping-Andersen 1990), and we distinguish the South European
or Mediterranean regime within the corporatist type. Arbaci (2007) shows that the
liberal welfare regime tends to correlate with higher levels of residential segrega-
tion, while corporatist and social-democratic welfare types tend to relate to lower
levels of segregation. Interestingly, she further argues that a corporatist (as well as
Mediterranean) welfare regime could lead to a more mosaic type of urban spatial
structure because of the large number and diversity of urban developers compared
to the social-democratic welfare regime. As a consequence, Arbaci argues that the
Table 1.1 Key structural indicators that shape socio-economic segregation and their corresponding values
Indicators: Globalisation Gini index Welfare regime Housing
regime
Higher
occupations*Lower
occupations**
London Alpha++ 38 Liberal Dual 33 10
Riga Beta-35 Liberal-PS*** Dual-PS 33 10
Vilnius Gamma 34 Liberal-PS Dual-PS 50 7
Madrid Alpha 36 Mediterranean Mediterranean 29 14
Tallinn Gamma 32 Liberal-PS Dual-PS 34 8
Milan Alpha 21 Mediterranean Mediterranean 32 15
Amsterdam Alpha 30 Corporatist Unitary 46 5
Budapest Beta+29 Corporatist-PS Dual-PS 34 7
Oslo Beta 27 Social Democratic Dual 37 4
Athens Beta+35 Mediterranean Mediterranean 26 9
Stockholm Alpha-24 Social Democratic Unitary 33 6
Prague Alpha-27 Corporatist-PS Unitary-PS 26 5
Vienna Alpha-28 Corporatist Unitary 28 9
Indicator
values:
Globalisation Gini index Welfare regime Housing
regime
Higher
occupations*Lower
occupations** Sum
London 3 3 3 3 2 2 16
Riga 2 3 3 3 2 2 15
Madrid 3 3 2 2 1 3 14
Vilnius 1 2 3 3 3 2 14
Milan 3 1 2 2 2 3 13
Tallinn 1 2 3 3 2 2 13
Amsterdam 3 2 1 1 3 2 12
Athens 2 3 1 2 1 2 11
Budapest 2 2 1 3 2 1 11
Oslo 2 1 2 3 2 1 11
Stockholm 3 1 2 1 2 2 11
Prague 3 1 1 1 1 2 9
Vienna 3 2 1 1 1 1 9
Notes:
* Share of managers and professionals in the workforce.
** Share of elementary occupations in the workforce.
*** Post-socialist.
14 Tiit Tammaru et al.
lowest levels of segregation evolve under corporatist welfare regimes. We therefore
expect that the welfare regime contributes to higher levels of segregation in London,
Riga, Tallinn and Vilnius, and to lower levels of segregation in Amsterdam, Athens,
Budapest, Milan, Madrid, Prague and Vienna.
The classification of the welfare regimes does not take into account the housing
systems – a key element influencing segregation (Maloutas 2012). Socio-economic
residential sorting is firmly shaped by the extent of income-based housing mar-
kets and policies (Reardon and Bischoff 2011). Although strongly linked, welfare
regimes and housing regimes do not necessarily correspond with each other. The
best examples in our pool of cities include Oslo and Stockholm. Although both rep-
resent a social-democratic welfare regime, the housing sector in Oslo (Wessel 2016)
is much more market based compared to Stockholm (Andersson and Kährik 2016).
Therefore, as the fourth key contributory factor, we introduce the housing regime
into our analytical framework by assuming that marketisation of housing produces
a stronger spatial separation of socio-economic groups (Marcińczak et al. 2015).
As shown in the literature, the most important division line runs between dual and
unitary housing systems (Kemeny 1995). Market-based residential sorting in the
dual system can be expected to lead to stronger income-based residential segrega-
tion in cities such as Budapest, London, Oslo, Riga, Tallinn and Vilnius (Table 1.1).
A lower level of marketisation and a tenure-neutral housing policy under the unitary
housing system would lead to weaker income-based residential segregation in cities
such as Amsterdam, Prague, Stockholm and Vienna. As with the welfare regimes,
we distinguish a South European housing regime which sits in-between the two
main types. Athens, Madrid and Milan represent such cities in our study.
The level of socio-economic segregation is also a function of the occupational
structure of the city. The more socially polarised a city is, the higher the levels of
expected segregation (Mollenkopf and Castells 1991) since both the higher-status
and lower-status groups tend to be more segregated compared to middle-status
groups (Duncan and Duncan 1955; Morgan 1980; Marcińczak et al. 2015). The
share of managers and professionals has increased remarkably while the share of
people working in elementary occupations (unskilled workers) has decreased to 15
per cent of the workforce or less in our research cities. We use both the share of
managers and professionals, as well as the share of people working in elementary
occupations to account for the effect of the occupational structure (Table 1.1). We do
not prefer one group to another since in some of our case study cities, managers and
professionals are the most segregated group while in other cities unskilled workers
form the most segregated group. In Amsterdam and Vilnius, the share of managers
and professionals is more than one standard deviation above the average, and we
expect that this has a strong effect on segregation in those two cities. In the case of
unskilled workers, this effect is strongest in Madrid and Milan. In Athens, Prague
and Vienna, the share of managers and professionals is one standard deviation below
the average, and we expect that this leads to the lowest levels of segregation. In the
case of unskilled workers, this is true for Budapest, Oslo and Vienna.
As a final step, we calculate the sum of the indicator scores for each city in
Table 1.1 (last column of the lower half of the table). We do not weight the various
Understanding socio-economic segregation 15
scores since the literature provides no guidance on how to weight each of them.
The result of this exercise is a theoretical ranking of our thirteen case study cit-
ies based on the levels of socio-economic segregation. This ranking is obviously
sensitive both to the number of factors/indicators included in the analytical frame-
work, as well as to the scores attached to them. However, this is a first attempt
to systematically contrast theoretical segregation levels across European capital
cities and empirically test them (Marcińczak et al. 2016). The summation itself
provides the following hypothetical ranking of cities included in this study (from
most to least segregated): London (16 out of 18); Riga (15); Madrid and Vilnius
(14); Milan and Tallinn (13); Amsterdam and Athens (12); Budapest, Oslo and
Stockholm (11); Prague and Vienna (9). In order to facilitate our comparative
study we next discuss data and methods that guide the analysis of the chapters
included in this book.
Data and methods
In order to undertake a rigorous comparative analysis of socio-economic seg-
regation in thirteen cities, the first task is to produce comparable datasets. This
is not easy because of two major obstacles. First, different countries use differ-
ent key indicators for socio-economic status. Second, different countries have
different data policies when it comes to releasing data for small geographic
areas, and use different aggregation levels of either socio-economic groups or
spatial units/neighbourhoods. These obstacles are beyond our control, but we
aimed at a high level of comparability and analytical detail within these two
sets of limits.
The first data-related challenge pertains to the indicators of socio-economic sta-
tus. We had to use three different variables for measuring socio-economic status
in different cities: occupation, income and education. Occupation, but not income,
is available at a detailed geographic resolution in those countries that undertake
censuses. The analysis of Athens, Budapest, Madrid, Milan, London, Prague, Riga,
Tallinn and Vilnius is thus based on occupational data. Income, but not occupation,
is available at a detailed geographic resolution for cities in register-based countries:
Amsterdam, Oslo and Stockholm. Finally, the study of Vienna is based on educa-
tion as Austria switched from census to registers in 2010 with the unfortunate side
effect that neither income nor occupation data is available at a detailed geographic
resolution in this country for the 2001–2011 period.
We use the International Standard Classification of Occupations (ISCO) for
defining the major occupational groups in chapters based on occupational data
(ILO 2015), and income quintiles are used in chapters based on income data. We
excluded two small occupational groups, agricultural workers and armed forces,
from the ten major categories in the ISCO classification used in our analysis.
This leaves us with the occupational ladder of eight major categories: managers,
senior officials and legislators; professionals; technicians and associate profes-
sionals; clerks; service and sales workers; craft and related trades workers; plant
and machine operators, and assemblers; and elementary occupations.
16 Tiit Tammaru et al.
The ISCO classification is generally internationally comparable, although
it should be noted that countries do make some minor modifications to the
classifications. What is more important is that the classification itself changed
between the 2001 and 2011 census rounds. ISCO-08 replaced the ISCO-88 for
several reasons, mainly because the latter was seen to be seriously outdated,
most notably due to developments in the field technology sectors (ILO 2007).
Also, there was a need to better aggregate managerial occupations (ILO 2012).
Most of the changes took place within the ten major categories, but some jobs
were also shifted from one major category to another (detailed ISCO-88 and
ISCO-08 correspondence tables can be found at ILO 2015). Most importantly,
managers of small organisations without any sophisticated hierarchical struc-
ture, such as small shops, restaurants, cafes and similar establishments, were
shifted from the first major group of managers to the fifth major group, service
and sales workers. This had an especially important effect on cities with a large
number of such small establishments, such as Southern European cities.
Occupation and income are strongly related to each other: the higher the job in
the occupational ladder the higher the income, with managers and professionals
earning the most (Table 1.2). The relationship is not directly linear, though. For
example, service sector workers have the lowest income together with the elemen-
tary workers. Another general issue to consider is that the extent of the differences
between occupational groups varies city by city. For example, differences in the
wages between occupational groups are the smallest in Athens (for example, man-
agers earn 2.2 times the salary of elementary workers) and Stockholm, and the
largest in Prague, Budapest and Madrid. Despite those differences, it is still safe to
Table 1.2 Income of occupational groups relative to people working in elementary
occupations (data from around 2010)
Managers
Professionals
Technicians
Clerks
Service
workers
Craft
workers
Operators
Elementary
workers
Amsterdam 3.3 2.5 2.3 1.5 1.1 1.6 1.5 1.0
Athens 2.2 2.3 2.0 1.6 1.4 1.3 1.6 1.0
Budapest 5.1 3.2 2.2 1.7 1.1 1.5 1.5 1.0
London 2.7 3.0 3.0 1.7 1.0 1.2 2.0 1.0
Madrid 4.6 2.6 2.2 1.5 1.2 1.6 1.6 1.0
Milan 3.8 1.9 1.6 1.4 1.1 1.1 1.2 1.0
Oslo 3.0 2.1 1.9 1.4 1.0 1.5 1.5 1.0
Prague 5.3 3.0 2.3 1.7 1.3 1.6 1.5 1.0
Riga 2.9 2.4 2.0 1.6 1.1 1.4 1.5 1.0
Stockholm 2.3 1.7 1.4 1.2 1.1 1.2 1.2 1.0
Tallinn 3.5 2.3 1.9 1.4 1.3 1.9 1.6 1.0
Vienna 3.0 2.1 1.8 1.5 1.1 1.5 1.4 1.0
Vilnius 3.1 2.3 1.8 1.5 1.1 1.4 1.5 1.0
Source: Statistical Offices of the countries.
Understanding socio-economic segregation 17
argue that the two variables used in different case studies in this book – income and
occupation – are clearly positively related to each other, allowing us to make broad
comparisons between the findings. In Vienna, as noted, education has been used as
a proxy for socio-economic status. This variable also correlates with income and
professional categories, but again this is not a one-to-one relationship. Some cau-
tion is required when comparing this city with the others.
We used data from the 2000 and 2010 (or any other years close to these) census
rounds. Some chapters also use data from the 1990 census round. We define cit-
ies as a continuous built-up area that forms a common housing market. In other
words, the analysis is not confined to administrative city boundaries. Within this
broad definition of a common housing market area, authors had to use different
definitions. For example, cities that are more sprawling (many Western European
cities without a communist past) adopt an extensive delimitation strategy of the
city, while cities that are less sprawling (many East European cities) adopt a
more restrictive delimitation strategy. Likewise, in some cities, data was avail-
able for the administrative unit only. Again, all these data limitations are beyond
our control. Within the cities, the suggested smallest spatial unit of analysis is a
neighbourhood of around a thousand inhabitants.
We apply the most well-known indices of segregation that measure the resi-
dential separation of population subgroups from each other, focusing either on
their distributions across the neighbourhoods (evenness dimension) or on the
potential to meet each other within each neighbourhood (exposure dimension).
While studies of ethnic segregation tend to have a stronger interest towards the
exposure dimension of segregation that characterises the potential for interaction
between ethnic groups, in this book, our main focus is on the evenness dimension
that allows us to understand how equally socio-economic groups are distributed
across the neighbourhoods of European capital cities. Yet, we present both.
For analysing the evenness dimension, we calculate a dissimilarity index5 (D)
that compares the spatial distance between each occupational/income group, and
the index of segregation6 (IS) that compares the spatial distance of a given group
from the rest of the workforce. Our main focus is on D. The values of the D range
from 0 to 100, indicating what percentage of either one or the other group needs
to change address across the neighbourhoods in order to achieve a similar residen-
tial distribution to the reference group. The D value 0 represents the completely
equal distribution of the two groups across the neighbourhoods, while the D value
100 represents complete separation, with some neighbourhoods providing shelter
to the members of a given group and other neighbourhoods to the other group.
If the D value for the managers compared to professionals is 20, it means that
either 20 per cent of managers or professionals have to redistribute in the city in
order to achieve their equal distribution across neighbourhoods. In the context
of ethnic segregation, index values of D below 30 are interpreted as low and D
values above 60 are interpreted as high (Massey and Denton 1993). Since levels
of socio-economic segregation tend to be lower than levels of ethnic segregation,
Marcińczak et al. (2015) suggest that D values below 20 can be interpreted as low
and D values above 40 can be interpreted as high.
18 Tiit Tammaru et al.
Next, we capture the exposure dimension of segregation by calculating the index
of isolation (II) and the modified index of isolation7 (MII). The meeting potential
it captures is important since previous literature argues that when lower socio-
economic groups get isolated, they are cut off from important social networks and
positive role models, thereby starting to hamper their other life careers (van Ham
and Manley 2012). The II is sensitive to the relative sizes of the groups. If the given
group is large in size, its probability for meeting members of the other groups in
residential neighbourhoods is small, and if the group is small in size, its probabil-
ity for meeting members of the other group in residential neighbourhoods is large,
regardless of their even distribution across the neighbourhoods. Think of a group A
of a thousand people and a group B of a hundred people who have a similar distri-
bution across three neighbourhoods of a city, i.e. 50 per cent, 30 per cent and 20 per
cent of the members of both groups live in neighbourhoods X, Y and Z, respectively.
Because of differences in size, in each of these neighbourhoods X, Y and Z mem-
bers of group A have a high potential to meet own-group members, while members
of group B have a small potential to meet own-group members. When the group
A increases in size, its isolation from the group B increases further, even when the
distribution of both groups across the neighbourhoods remains the same.
This is exactly the case in cities that are undergoing a process of profes-
sionalisation – the share of managers and professionals increases in the urban
workforce – which is typical for most of our research cities. Indices that measure
the exposure dimension of segregation can thus change without changes taking
place in indices that measure the evenness dimension of segregation. In order
to also take into account the group size and its change, the modified index of
isolation (MII) is used by subtracting the city-wide share of the group from II.
We use both the II and MII for analysing the potential to meet members of the
other socio-economic categories in their residential neighbourhood. The smaller
the MII value, the less isolated the given group is from the rest of workforce and
vice versa. For example, if the MII value for the managers is 20, it means that 20
out of the hundred people this person potentially meets within the neighbourhood
of residence are also managers. In the context of ethnic segregation, the index
values of MII below 30 are interpreted as low and MII values above 60 are inter-
preted as high (Massey and Denton 1993). Again, since levels of socio-economic
segregation tend to be lower than levels of ethnic segregation, we interpret MII
values below 20 as low and MII values above 40 as high.
This book compares residential distributions of all eight major occupational
groups or five income groups to each other to find out how strongly socio-
economic distance is related to spatial distance. All indices presented above
are sensitive to the aggregation of workers into occupational/income groups,
and the aggregation of addresses into residential neighbourhoods. As a rule of
thumb, the larger the groups/neighbourhoods used are, the lower are the values
of the dissimilarity index. When it comes aggregating addresses into neighbour-
hoods, we face zoning and scaling problems (see Flowerdew et al. 2008 on the
modifiable area unit problem). The most common way of overcoming these is
to define neighbourhoods as small and homogenous spatial units. Obviously, the
Understanding socio-economic segregation 19
aggregation of jobs into major occupational categories faces a similar problem.
Here we follow the most common approach as explained above by using the
standard major ISCO categories developed by the ILO (2015).
Finally, in order to also learn about the local geographies of segregation, we use
location quotient8 (LQ) maps that visualise the relative spatial concentration or dis-
persion of income/occupational groups in the neighbourhoods of the city. In essence,
the LQ is a ratio between the share of a given group, such as managers, in a given
spatial unit, and the city-wide share of this group. If the ratio is less than 1, the group
is underrepresented in the given neighbourhood, and if it is more than 1, the group is
overrepresented in the given neighbourhood.
These simple but well-known and widely used measures (D, IS, II, MII, LQ)
are applied in all cities to 2001 and 2011 (census) data. In addition to this har-
monised analysis, each chapter elaborates on the topics which are most relevant
in the given city context for the understanding of changing levels and patterns of
socio-economic segregation.
Main findings: introduction to the book chapters
The 13 cities brought together into this book represent different regions of
Europe, as well as different degrees of globalisation, inequality, welfare and
housing regimes, and occupational profiles (Table 1.1). The multi-factor approach
resulted in the following theoretical ranking of cities with regard to their expected
level of socio-economic segregation: London; Riga; Madrid and Vilnius; Milan
and Tallinn; Amsterdam and Athens; Budapest, Oslo and Stockholm; and finally
Prague and Vienna. The case studies presented in this book revealed a some-
what different ranking based on real data: Madrid and Milan; Tallinn; London;
Stockholm; Vienna; Athens; Amsterdam; Budapest; Riga; Vilnius; Prague; and
Oslo. Most importantly, Riga and Vilnius are actually much less segregated, while
Stockholm and Vienna are much more segregated compared to what we predicted.
We will elaborate further on the differences between the theoretical and actual
rankings of cities in the conclusion of this book (Marcińczak et al. 2016). Next,
we will briefly introduce each city and highlight the main findings from the con-
tributions that follow in the rest of the book.
London, Amsterdam and Vienna represent West European cities. Based on
factors outlined in Table 1.1, we hypothesised London to be highly segregated,
Amsterdam moderately segregated and Vienna weakly segregated. The main find-
ings confirm the hypothesis for London and Amsterdam but not for Vienna, which
is highly segregated too. More specifically, the D value between top and bot-
tom socio-economic groups is 42, 33 and 39 in these cities respectively. Also, by
applying innovative multivariate extensions of traditional segregation indices for
the first time, Manley et al. (2016) modelled occupational segregation in London.
They found that although overall segregation decreased slightly in the 2000s, it
was not statistically significant and there are still sharp divisions within the city
landscape, with growing spatial distance between the top and bottom socio-economic
groups. Amsterdam is the only one within our pool of cities where segregation
20 Tiit Tammaru et al.
between the top and bottom socio-economic groups decreased in the 2000s. This
has happened at times of strong neo-liberalisation tendencies that included an
increase in owner occupation and residualisation of social housing that contribute
to forces that would increase segregation. Musterd and Van Gent (2016) argue that
the still low level of segregation is probably related to the long tradition of a fairly
equal income distribution in the Netherlands, but that the recent decrease must
be attributed to the 2008 financial and economic crisis, which reduced residen-
tial mobility, and a temporary effect of new gentrification processes that initially
causes more social mix and thus a decreasing level of segregation. Vienna has
long been focused on policies of social equality, with the city constantly working
to develop measures aimed at reducing social disparities. Contrary to Amsterdam,
Vienna experienced a significant increase in socio-economic segregation in the
2000s. Hatz et al. (2016) link high levels of socio-economic segregation in Vienna
with new immigration. There is also evidence that lower socio-economic groups
have become more confined to public housing neighbourhoods.
Stockholm and Oslo represent North European cities. Based on the factors
outlined in Table 1.1, we hypothesised Stockholm to be weakly and Oslo to be
modestly segregated. The main findings do not confirm these hypotheses. More
specifically, the D value between top and bottom socio-economic groups is as low
as 24 in Oslo and as high as 40 in Stockholm. Quite unexpectedly, thus, the two
cities with similarly low levels of social inequality are among the most and least
segregated cities among our thirteen cities. This can partly be due to differences
in measurement: the study of Stockholm is restricted to working age population
while no such restriction was applied in Oslo. Still, the high level of segrega-
tion in Stockholm comes as a surprise. Andersson and Kährik (2016) argue that
despite the long tradition of elaborate public policies in Sweden that have aimed
towards neighbourhood social mix, the public sector started to cut back on hous-
ing subsidies in the 1990s. Wessel (2016) argues that the emerging pattern of
socio-economic segregation in Oslo is a ‘contingent outcome’ of many structural
factors rather than a simple reflection of economic transformation and globalisa-
tion. An especially generous welfare system due to the Norwegian revenues from
natural resources is an important characteristic of Oslo above and beyond the
strongly market-based housing system that allows the sustaining of relatively high
levels of equality, at least in a comparative perspective.
Athens, Milan and Madrid represent South European cities. Based on the fac-
tors outlined in Table 1.1, we hypothesised Madrid to be the most segregated,
followed by Milan and Athens. The main findings partly confirm these hypoth-
eses. More specifically, the D value between top and bottom socio-economic
groups is 49 in Madrid and Milan, and 35 in Athens. Madrid and Milan are also
the most segregated cities in our pool of European capitals. Despite high lev-
els of social inequality we find not only moderate levels and stable patterns of
socio-economic segregation in Athens but even desegregation between some
occupational groups. Maloutas (2016) explains this as a combination of stability
in occupational structures, reduced immigration, high rates of homeownership and
low levels of residential mobility at time of crisis, as well as vertical segregation
Understanding socio-economic segregation 21
within buildings. According to Leal and Sorando (2016), both professionalisation
and residential entrapment of lower socio-economic groups are behind the dra-
matic growth in levels of segregation in Madrid. Furthermore, the authors argue
that the invasion of professionals into new parts of the city, for example as a
result of gentrification, as well as in situ inter-generational social mobility both
actually exerted lowering effects on levels of segregation. In Milan, Petsimeris
and Rimoldi (2016) indicate that some of the important mechanisms behind
segregation include self-segregation of business owners into the most exclusive
residential areas of the city; in situ intra- and inter-generational social mobility;
and the purchase of apartments by working-class households under the right-to-
buy schemes and a later selling of their properties to more affluent social groups.
In East Europe, we make a distinction between Visegrad and Baltic capitals;
Budapest and Prague represent Visegrad cities. Based on the factors outlined in
Table 1.1, we hypothesised Budapest to be moderately and Prague to be modestly
segregated. The main findings confirm these hypotheses. More specifically, the D
value between top and bottom socio-economic groups is as low as 26 in Prague
and 32 in Budapest, with a slightly increasing trend in both cities. Budapest used
to be the most segregated city in East Europe, but this is not the case anymore.
Furthermore, instead of higher socio-economic groups, lower socio-economic
groups became most segregated in the 2000s. Kovács and Szabó (2016) think that
the most plausible reason for the decreasing segregation level of professionals is
their more even distribution across neighbourhoods, triggered by new housing
projects developed in the inner city and other areas with previously a high share
of lower socio-economic groups. This is quite similar to what is taking place in
Prague. Ouředníček et al. (2016) agree that low levels of socio-economic segrega-
tion are mainly a consequence of the location of new housing and in-migration of
higher socio-economic groups into the formerly poorer neighbourhoods, often in
the inner city of Prague. Such changes are common to many other cities included
into our analysis beyond Eastern Europe. In other words, the increase of social
inequalities often goes hand-in-hand with gentrification, which, at least initially,
brings down levels of segregation as different social groups begin to mix in the
inner city.
Riga, Vilnius and Tallinn represent Baltic cities in East Europe. They share
a Soviet past and nation-building since regaining independence in 1991. Based
on the factors outlined in Table 1.1, we hypothesised all of them to be highly
segregated. The main findings confirm the hypothesis only for Tallinn. More spe-
cifically, the D value between top and bottom socio-economic groups is 31 in
Riga and Vilnius, and 48 in Tallinn. Tallinn also witnessed the highest growth
in socio-economic segregation among our case study cities in the 2000s, match-
ing high social inequalities with high spatial inequalities, and becoming the most
segregated East European city within the pool of our research cities. Neither in
Riga nor in Vilnius has the combination of large social inequalities and a liberal
society led to marked socio-economic divisions in urban space. Both Krišjāne
et al. (2016) and Valatka et al. (2016) explain that gentrification has led to the
increase of mixed neighbourhoods in the inner city of Vilnius similar to what has
22 Tiit Tammaru et al.
happened in Budapest and Prague. Krisjane et al. further argue that the ethnic
dimension is more important than the socio-economic dimension in generating
segregation in Riga, where the Russian-speaking population forms more than
half of the city’s population. Mixed neighbourhoods also characterised Tallinn
in 2000. Tammaru et al. (2016) show that the residential relocation of higher
socio-economic groups both to the inner city and outer city low-density areas is
responsible for the increase in socio-economic segregation in the 2000s, replac-
ing many earlier mixed neighbourhoods with more homogenous neighbourhoods.
Thus it seems that gentrification processes have proceeded at a very rapid pace
in Tallinn once the institutional transformations of the 1990s were completed.
Privatisation in Tallinn was faster and the city is slightly wealthier compared to
Riga and Vilnius (Tsenkova 2006), and less regulated and more unequal compared
to Budapest and Prague. It remains to be seen whether other Baltic capitals that
are as unequal but less segregated follow Tallinn.
Conclusions
This introductory chapter has outlined the multi-factor approach for the study of
socio-economic segregation in thirteen European political and economic capital
cities. Based on a literature review, we distinguished four important structural fac-
tors that could help us understand levels of socio-economic segregation: degree of
globalisation, level of social inequalities, welfare regimes and housing regimes.
As socio-economic segregation is closely related to the occupational structure
of cities, a fifth additional factor is the share of managers and professionals. Our
main findings show that socio-economic segregation grew across Europe in the
2000s (with the exception of Amsterdam). This provides evidence for a shift from
a city of social classes to a city of socio-spatial groups as suggested by Kesteloot
(2005), especially when it comes to the residential separation between the top
and low socio-economic groups who are increasingly isolated from each other in
urban space. There is no simple correlation between the main contributory factors
to segregation and the actual levels of segregation; the levels of social inequalities
and the levels of socio-economic segregation do not necessarily match up (com-
pare Madrid and Milan). A unitary housing system along with elaborate public
policies, as is the case in Stockholm and Vienna, is not a sufficient precondition
for low levels of socio-economic segregation. Marketisation of housing does not
necessarily lead to high levels of segregation as the case of Oslo shows – if placed
into an otherwise generous welfare context.
The most common factor characterising highly segregated cities is the level of
globalisation. In Western Europe, the most global city, London, is more segregated
than Amsterdam and Vienna. In the South of Europe the more global cities of Madrid
and Milan are more segregated than Athens despite the fact that social inequalities
are much higher in Athens compared to Milan. The only distinct case is Tallinn,
which is one of the least global but one of the most segregated cities in our pool of
cities. All this leads to a clear message: universal structural factors, especially globalisa-
tion, are very important, but they also need to be combined with city-specific factors
Understanding socio-economic segregation 23
to fully understand the variations in the levels and patterns of socio-economic segre-
gation in European capital cities. What follows in the rest of the book, therefore, is a
detailed and context-sensitive analysis of each city, which, ultimately leads to a com-
prehensive synthetic account of our findings in the concluding chapter of the book.
Notes
1 We are very grateful to all the co-authors for their contribution to the book. Wouter Van
Gent from the University of Amsterdam and Michael Gentile from the University of
Helsinki provided invaluable comments to this introductory chapter. The research lead-
ing to the results presented in this chapter has received funding from the Estonian
Research Council (Institutional Research Grant IUT no. 2–17 on Spatial Population
Mobility and Geographical Changes in Urban Regions); European Research Council
under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC
Grant Agreement no. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-
spatial Inequality, Deprived Neighbourhoods, and Neighbourhood Effects); and from
the Marie Curie programme under the European Union’s Seventh Framework Programme
(FP/2007-2013) / Career Integration Grant no. PCIG10-GA-2011-303728 (CIG Grant
NBHCHOICE, Neighbourhood Choice, Neighbourhood Sorting, and Neighbourhood
Effects).
2 We use the term East Europe for countries that used to be part of the state socialist, cen-
trally planned bloc of countries for the five decades after World War II (Czech Republic,
Estonia, Hungary, Latvia and Lihtuania in this book), and West Europe for the rest of
Europe (Austria, Greece, Italy, Netherlands, Norway, Spain, Sweden, and the United
Kingdom in this book).
3 We should also recognise that besides polarisation processes, spatial and social
mismatch processes may also occur and contribute to inequality and segregation
(Wilson 1987). Mismatches between jobs supplied and jobs demanded will create
unemployment (over the past decades more in former local economies dominated by
manufacturing industries, and less in service oriented economies). This produces
cleavages between employed and unemployed that will eventually also be reflected
in segregation between these categories.
4 Figures 1.1 through 1.3 are inspired by work from Maloutas 2012.
5 We first use the index of dissimilarity (Duncan and Duncan 1955), which is calculated
as follows:
Dh
H
l
L
i
T
i
T
i
n
=−
=
1
21
,
where n is the number of spatial units/neighbourhoods; hi is the number of members of
one group (for example, highest socio-economic group) in neighbourhood i; HT is the
total number of this group members in the city; li is the number of the other group (for
example. lowest socio-economic group) in neighbourhood i; and LT is the total number
of this group members in the city.
6 The index of segregation is a variant of the dissimilarity index and it is calculated as
follows:
IS x
X
tx
TX
i
T
ii
i
n
=−
=1
,
where n is the number of neighbourhoods; xi is the number of people in the given group
(for example highest socio-economic group) living in neighbourhood i; XT is the total
24 Tiit Tammaru et al.
number of this group in the city; ti is the number of all other groups’ members (for
example. the rest of the workforce) in the neighbourhood i; and TT is the total number
of them in the city. In other words, IS indicates how much the residential distribution
of the given group, such as the highest socio-economic group, differs from residential
distribution of the rest of the workforce across the neighbourhoods of a city.
7 The MII is calculated as follows:
MII x
x
x
t
x
t
x
T
ii
i
i
i
i
n
=11
where n is the number of neighbourhoods; xi is the number of people of a given group
living in a neighbourhood i; X is the total number of people in this category; ti is the total
number of people living in a given neighbourhood i; and T is the total number of people
living in the city. The equation reduces to II when we remove the group size correction
from the MII equation.
8 The LQ is calculated as follows:
LQ xt
ii
=
// /,
where xi is the number of people of a given socio-economic group in the neighbourhood i;
ti is the total population of this neighbourhood i; X is the total number of the given socio-
economic group in the city, and is the total population T (workforce) of a city. If the ratio
is 1, the share of the given group in the given neighbourhood is exactly the same as her
city-wide average.
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  • ... Previous research has identified that changes in the global economy, structural changes in the labour market and occupational structure of the city, the type of welfare state and provision of public services, investment in social housing and its spatial distribution, and income inequality are all important factors that affect socioeconomic segregation in cities. However, Tammaru et al. (2016) note that there is no simple correlation between the principal contributing factors and levels of segregation. For example, the relationship between income inequality and socioeconomic segregation is mediated by the institutional set-up, demographic developments and various policies at local and national level, while they also interact with ethnic segregation. ...
  • ... In order to simulate different initial wealth configurations, we draw wealths from two log-normal distributions—LN (μ = 0, σ = 0.25) and LN (μ = 0, σ = 0.5). Additionally, given the fact that cities around the world are economically segregated to different extents[30][31], we create initial wealth configurations representing different levels of segregation. We do this by ordering the realized log-normal wealth distribution in ascending order, and creating M P-sized blocks of sorted agent wealths. ...
  • ... Queste esperienze dal basso raccontano una relazione pubblico-privato che sta ricostituendosi in epoca di crisi e di contrazione della spesa pubblica, ma anche come conseguenza di " competenze nuove " che dal basso si innestano producendo nuove narrazioni, bisogni e di conseguenza interventi di policy. Queste sperimentazioni appaiono particolarmente interessanti in un contesto come quello italiano dove i processi di partecipazione sembrano essersi svuotati di senso a seguito di numerose occasioni mancate o utilizzati come strumenti ridotti, per buona parte, alla costruzione del consenso[Savoldi, 2006;Gelli, 2005]. Rispetto a questo scenario, emerge invece un nuovo coinvolgimento dei cittadini all'interno di processi di rigenerazione urbana dal basso. ...
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