SEGREGATION AND RESIDENTIAL PATTERNS OF UPPER
CLASSES IN THE BARCELONA AND MADRID METROPOLITAN
AREAS 2001 - 2011
RUBIALES PÉREZ, MIGUEL
Departamento de Geografía Humana, Universidad de Barcelona
[email protected]
ISABEL PUJADASRÚBIES
Departamento de Geografía Humana, Universidad de Barcelona
[email protected]
JORDI BAYONA I CARRASCO
Departamento de Geografía Humana, Universidad de Barcelona
[email protected]
Abstract
This paper studies the residential patterns of upper classes. The data used are the
population census (2001 and 2011) considering: nationality, employment category, housing,
unemployment and occupational field and the administrative unit of analysis is the census
tract. The upper class in Madrid and Barcelona has pioneered the process of deconcentration
of metropolitan areas and the colonization of suburban enclaves. This process has occurred in
the context of the convergence of the structures and internal composition of larger cities. In
this paper, we compare the patterns, morphology and dynamics of the segregation of that class
in the metropolitan areas of Madrid and Barcelona. Despite their uneven scale, the patterns in
both regions are similar: the upper class congregates in the “ensanches” and the new-built
areas of the capital, and expands along axes in low-density areas that connect those
“ensanches” with upper-class municipalities. Besides it, the upper class is involved in the
gentrification’s process of the historical city and other neighborhoods that were traditionally
popular.
Key words: Residential segregation, gentrification, socio spatial classes, elites, metropolitan
regions.
1. Introduction
The scholarly focus on social classes and their spatial distribution in Spain has had a
secondary place in geographical, urban and sociological research in the last years (Del
Castillo y Casado, 1998; Moreno et al. 2003; Martori, 2004; Leal, 2003). Until 2000, a
broader research program was developed with studies on urban inequality (Leal 1994; Alabart
y García, 1996), on ghettoes and their origins (Arias y Nicolás, 2000), on social change in
specific areas of the city (Álvarez, 1995; García y Pujadas, 2009), etc. Moreover extensive
applied research was carried on about Spanish social class methodological issues (Carabaña
et. al., 1992; Wright, 1997). After 2001, Spain became a destination for international
migration flows. The growing numbers of foreign populations attracted the attention of policy
makers and researchers towards segregation based on ethnic criteria. Thus, concern about
urban inequity changed towards ethnic segregation and integration policies (Bayona, 2007;
Arbaci, 2008; Pozo y García, 2009 among others). There is a growing trend in urban research
towards the combination between ethnic and socioeconomic criteria (Malheiros, 2002;
Wacquant, 2007 o Petsimeris, 2010).
Academic attention on the spatial distribution of the elite is rare, although it can be a
key factor to understand territorial configuration, mobility policies or the political feasibility
of public services (Atkinson y Flint, 2004). The main goal of this presentation is to account
for high class spatial distribution patterns in metropolitan areas. Especially, since Spanish
metropolitan regions have suffered fast social and population changes. The comparison
between those big urban regions (Madrid and Barcelona) can show to what extent spatial
patterns have local traits or are shaped by the influence of large scale factors.
2. Characteristics and changes in the metropolitan regions of Madrid and Barcelona.
The metropolitan regions of Madrid and Barcelona are involved in global network
economy, they make up 24,4% of the whole country’s population, they produce 31% of the
GDP (INE 2008, provisional data) and are the main places of residence of the elite and high
class populations. After the 1980’s, the population in Madrid, Barcelona and their
metropolitan areas has undergone a decentralization process (Pujadas y Bayona, en prensa).
This process of redistribution is based on internal migration that was exceptionally intense
between 2001 and 2007, when even the main cities (Madrid and Barcelona) where growing.
The cycle of growth stopped between 2008 and 2011, with the outbreak of the economic
crisis.
The two regions of study present major differences. One of them has to do with their
boundaries: since the Metropolitan Region of Barcelona (RMB) is the territory reached by
public transports managed by the authority of transport, there is no equivalent institution for
the functional area of Madrid. It is common to assimilate the regional boundaries of Madrid to
the municipalities administrated by the Autonomous Community of Madrid (CAM). The
RMB covers an area of 3.200 km2 and 164 municipalities (about half of the province of
Barcelona). The CAM includes the whole province of Madrid: 8.038 km2 and 178
municipalities. Population sizes are uneven: the CAM had 1.460.000 more inhabitants than
the RMB (according to the continuous register 2011). Finally, both cities have a different
relationship within the cities of their setting: Madrid has a greater prominence among its
middle towns than Barcelona. Middle cities in RMB have been historically more developed
than the CAM middle cities, functioning, sometimes, as labour and residential subcenters.
The CAM’s population grew quickly from 5.805.927 in 2001 to 6.489680 in 2011,
which amounts to a million new inhabitants and a rise by 20% of its initial population.
Similarly, in the province of Barcelona the population went up from 4.805.927 to 5.529.099
inhabitants (from 4.390 to 5.029.181 for the RMB), which amounts to a growth of 15 % from
2001. Madrid and Barcelona have been the protagonists of an intense process of
suburbanization and decentralization of economic activity: Barcelona’s relative weight
decreased from the 41,3 % of the total RMB population to 32,3 % of the total population in
2010; Madrid’s relative population in relation to the whole CAM reduces its weight from 67,4
% to the 50,7 % (Pujadas y Bayona, en prensa). The foreign population goes first towards the
main cities of each region and later moves to other neighbouring towns (Bayona y GilAlonso, 2011).
As will be discussed in section 3, national and international high classes have chosen
the metropolitan regions of Madrid and Barcelona as their main area of concentration
(approximately about 1.336.000 high class people in the CAM and 993.000 in RMB,
according to the 2001 census). Traditionally high classes have been located in the big inner
cities. Their sprawl started in the sixties, towards nearing municipalities which became more
and more socioeconomically specialised. This strong presence of the highest social groups
among regional centers and metropolitan regions is one of the main traits that shape the areas
of study, and constitutes the main composition of some socioeconomically specialised cities.
3. Methodology
The spatial distribution of high classes in the metropolitan regions of Madrid and
Barcelona is approached through multivariate analysis of the data of the 2001 Census tracts;
and through the calculation of segregation index for both region to determine its intensity
(segregation index and bi-group dissimilarity index).
The regions under study are Madrid and Barcelona. The Comunidad Autónoma de
Madrid (CAM) will be used as an approximation to Madrid metropolitan region. Two
administrative units will be used for Barcelona’s case: the provincia (province) for the
multivariate characterization of territory. The province of Barcelona will be used because its
size is more comparable to the CAM; the other administrative unit is the Region
Metropolitana de Barcelona (RMB), that is the territory managed by the autoridad del
transporte. Thus, the RMB will be used to calculate the segregation indices because it is a
better approximation to the functional metropolitan area.
3.1. Variable selection
The 2001 census offers a vast range of variables at a very detailed level. The
variables selected are part of the 5 key stratification dimensions: social class, lifestyle,
nationality, employability, and location in rural/urban area. Each of those dimensions has
been operationalized following criteria of relevance, reliability and parsimony: social class by
job category; lifestyle by the surface of the residence’s living area, nationality by current
nationality, employability by % of unemployment, and location in rural/urban area by the %
of agricultural workers (see table 2).
Table 1. Ocupational dimention, census viables and variables of analysis.
Variables Census 2001
Variable used in the multivariate analysis
National Classification of Occupations of 1994
1. Managers
2. Scientific and intellectual technicians and professionals
3. Technicians and support professionals
4. Administrative type employees
5. Service and sales workers
Social Class by occupational categories
High category 1+2
Middle-high category 3+4
6. Farm workers and fishermen
7. Miners and skilled construction and industry workers
8. Industrial installations operators and drivers
10. Members of armed forces
9. Unskilled workers
Middle-low category 5+6+7+8+10
Low category 9
Source: INE, Censo 2001. Own table.
The population’s education level or professional status (employers, independent
workers, salaried workers…) were other possible variables to operationalize social class, but
previous analysis on CAM and RMB (Rubiales et al., in press) showed some inconsistencies
in professional status distribution and a redundant covariation (95%) between job category
and level of education. The professional status variable (covariation of 70%) is not the best
variable for the operativization of class in Spain: there is a big and heterogeneous group of
independent workers, of employers without (or with few) employees and many small
agricultural enterprises and properties. Every of those traits of the Spanish social structure and
job market, leads to prefer an operativization of class based on job category rather than any
other.
The main problem of job category is its limitation to formal employed population
only, which could underestimate low classes since they are more excluded from the (formal)
job market. The dimension of employability helps to avoid this problem. The multivariate
analysis includes, by taking into account the unemployment ratio, the socio-spatial structure
derived from formal exclusion from the job market.
Nationality or ethnicity, strongly related to symbolic capital, could also work as a
criterion for territorial inequities and social segmentation in a way that is not reducible to job
categories or unemployment. Segregation based on nationality shows the differentiation
produced by cultural barriers and a very restrictive regulatory framework for migrant
workers’ rights. It also contributes, according to the 2001 census data, to distinguish between
metropolitan zones and neo-rural or suburban areas (that showed lower levels of migrant
population).
The big scope and internal variability of metropolitan regions presents the problem
of double hierarchy: There is a strong contrast between opportunities and costs associated to
urban zones and those small towns located far form the main city. Sometimes, it is necessary
to consider two different but overlapping systems of hierarchy. Despite this, it is easy to mix
up middle groups from small, aged municipalities with lower classes linked to and excluded
from metropolitan dynamics, since both populations work in lower category jobs. To achieve
an accurate differentiation between both, instead of removing small municipalities, two new
variables were added: the percentage of agricultural workers and the surface of the residence’s
living area1. Those dimensions, ruralness and life style, are also related to socioeconomic
stratification, but help to differentiate between the metropolitan structure and the neorural
components.
3.2. Calculations: Segregation index, factorial and cluster analysis.
The multivariate data analysis includes a factorial and a cluster analysis. The principal
components analysis (rotated by varimax method) generated 3 components that explained
The surface of the residence’s living area synthesizes different combinations of urban centrality, conspicuous
consumption and house living standards. This variable allows to take into account the distinction based on
consumption and to consider the better class situation resulting from cheaper costs and higher standards for those
living in rural / suburban areas. Thus, the surface of the residence’s living area is a key variable to operationalize
the spatial component in process and strategies of class distinction.
1
77% of total variability. These 3 components have been used to run hierarchical cluster
analysis with solutions from 4 to 10 clusters. The 8 clusters solution has been considered
optimal. Segregation and dissimilarity indices of each occupational category have been
calculated in order to offer some numeric summary for both metropolitan area. Those values
are merely indicative and cannot be considered to be accurate reflection of the territorial
distribution of social inequities. Segregation indices can suffer changes depending on the
definitions of the social groups considered, their relative size or the different boundaries of
the area of study. The segregation index compares the ratio of a population in one spatial unit
with the proportion of this population in the whole area in order to measure if the
concentration in each spatial unit is low, average or high in comparison to average values. A
bi-group dissimilarity index calculates if the presence of a group is related with the presence
of others.
4. Patterns of socioeconomic residential distribution
Conjunct cluster analysis (see figure 1) for the provinces of Madrid and Barcelona is
consistent with previous results obtained specifically for the CAM and the RMB (Rubiales et
al., in press). The average values of the clusters show aggregations of census tracts with
pronounced class profiles and consistent with its cartographic representation (see table 2).
This inframunicipal characterization allows a detailed account of high class concentration
patterns: seclusion (high class flight) to suburbs, low density areas and high class
municipalities, congregation in city centers, and gentrification in some central popular
neighbourhoods.
The cluster number 1, Elite, is composed of elite census tracts. It shows the highest
levels in the two highest job categories (48% and 26%), almost half of its population lives in
houses of more than 120 m2, there is low unemployment (9%) and the average age is 39 years
old. The census tracts of this cluster are typically urban, with little agrarian activity and little
immigrant population (5,4%). Although elite characteristics are not applicable to every one of
the 1.425.000 residents in cluster 1 (about a 25% have jobs in low middle and unskilled jobs),
it is probable that the country’s elite and higher classes live precisely in this census tracts.
Table 2. Clusters’ average values. The first ten variables were used to calculate the multivariate analysis.
Variables
1.
Elite
OCCUPATION
High category
Middle high cat.
Middle low cat.
Low category
HOUSE’S SURFACE
Less than 75 m2
75 to 120 m2
More than 120 m2
OTHER
Unemployment
Foreign populat.
Ocup act agríc
2.
High
3.
Middle
4.
Middle low
periurban
5.
Polarised
Area
6.
Low
7.
Rural
8.
Under
exclusion risk
48,2
25,7
19,8
6,3
36,5
33,4
24,4
5,8
20,6
30,7
38,1
10,6
17,4
19,3
54,1
9,2
25,3
26,7
33,4
14,7
14,2
23,6
50,8
11,4
16,1
15,0
62,0
6,9
8,4
20,2
54,1
17,3
14,9
39,2
45,9
24,7
67,4
7,9
61,2
36,1
2,6
18,5
56,7
24,7
67,9
26,4
6,5
32,2
61,0
6,8
12,6
44,8
44,5
80,7
18,1
1,2
9,0
5,4
0,8
9,9
3,7
0,3
12,2
6,0
0,5
9,0
3,8
4,2
13,0
14,3
0,6
12,1
3,5
0,7
6,1
2,1
25,7
14,8
7,4
0,8
OTHER CHARACTERISTICS
Number of census
1.002
883
tracts*
Average age*
39,0
40,5
Total population
1.425 1.186
(1000)*
% in Barc prov.
10,5
10,2
% in CAM
17,1
13,0
% in both*
14,0
11,7
1566
482
826
1809
54
1111
42,2
39,9
43,1
37,7
43,8
40,4
1.893
658
916
2.799
22
1.271
17,5
19,6
18,6
9,4
3,8
6,5
3,3
14,1
9,0
35,2
20,8
27,5
0,3
0,1
0,2
13,6
11,5
12,5
Source: INE, Census 2001. Own table.
The cluster nº2, High, is similar to cluster number 1 and includes census tracts where
high classes predominate. The values of this cluster are not as pronounced as the values of the
elite cluster. Most of its population lives in medium size houses (between 75 m2 and 120 m2).
At first glance, it could seem that Elite and High clusters share similar traits, but cartographic
representation shows that not only High census tracts shows lower socioeconomic levels and
living standards, but also lower centrality. Population located in High census tracts shows
lower capacity to achieve big houses, high environmental quality or to hold symbolically
valuated central places.
Figure 1. Spatial distribution of middle and periurban areas of Barcelona (Detail).
Source: INE, Census 2001. Own map.
Clusters 3 (Middle) and 4 (Middle-low suburban) have more balanced profiles. There
is more social heterogeneity, although the percentage of population working in middle job
categories predominates. Cluster 3 presents a mixed proportion of job categories: 21% of
high, 31% of middle high, 38% of middle low and 10% of low category; its population is
embedded in the metropolitan dynamics (central locations); the houses standards are low:
61% living in houses with less than 75m2; and the unemployment is high (12%). Cluster 4
(Middle-low suburban) is mostly populated (54%) by service, sales, farm, construction and
industry workers, drivers, army forces and industrial operators. Despite its lower job category,
population living in this spatial cluster access to bigger houses: 57% of the population living
in houses from 75 m2 to 120 m2 and 25% living in houses bigger than 120 m2. The population
of cluster 4 traded living standards for centrality as shown by the spatial distribution: cluster 3
is located in the main cities of CAM and RMB, since cluster 4 spreads towards more distant
cities. The ring roads of the main cities are almost a perfect barrier between the two clusters
(see figures 1 and 3).
Cluster 5 (Polarised area) is the hardest to classify, but is also the most interesting.
The polarised area shows a sharp internal contrast with a significant percentage of population
working in high (25%) and middle high (27%) categories. It also shows the highest proportion
of population working in unskilled jobs (15%), unemployment (13%), and people living in
less than 75 m2 (68%). Its centrality is maximal: historic city centers and old working class
neighbourhoods (average age: 43 years). It is important to understand that this cluster shows
the maximum values for low class indicators (unskilled workers, unemployment or small
houses), but also that the situation of the low class population of this cluster is even worse,
since the average values are shared with the population of the highest classes (52% of the
whole cluster are white collar workers). Cluster 5 (Polarised area) locates large socio-spatial
processes in which high classes are involved: gentrification, social recomposition and “white
flight”. These city areas have also worked as first bridgeheads of migration fluxes toward
Spain (especially from 2001 to 2007).
Figure 2. Elite, High and Polarised sections in Barcelona and central districts of Madrid
Source: INE, Census 2001. Own map.
Figure 3. Spatial distribution of rural an periurban areas in the provinces of Madrid and Barcelona
Source: INE, Census 2001. Own map.
Cluster 7 (Rural) refers to distant areas, with small, aged populations (average age of
44 years), high ratio of agricultural workers (25%) with middle low job categories (54%) but
few unskilled workers (only 7%). Despite the predominance of low job categories, the risk of
exclusion is also low: it is the area with less unemployment (6%) and the vast majority of the
population (89%) can afford medium (45%) or big (44%) houses. Clusters 6 (Low) and 8
(Exclusion risk) are also related to populations with low job categories but in a metropolitan
environment with higher exclusion risks: in cluster 8, 81% of the population lives in houses
with less than 75 m2, 17% are unskilled workers and the unemployment ratio reaches 15%.
4.1. Congregation of high classes in city centers.
The city centers of Madrid and Barcelona are the places with the major congregation
of high classes in the whole country despite decades of internal migrations towards the
suburbs. Congregation in city centers is the oldest spatial pattern of high class distribution
through the urban fabric and can be found in many secondary towns in addition to the big
concentration of the main cities. Subregional centers, like Alcalá de Henares in the CAM or
Terrassa and Sabadell in the RMB, had already developed high classes during the 1970’s (see
figure 4). The historically polycentric configuration of the RMB can be observed in the high
number of middle cities in which centers are populated by high classes.
Figure 4. Examples of high class congregations in metropolitan subcenters like Alcalá, Terrassa and Sabadell
Source: INE, Census 2001. Own map.
4.2. Seclusion (high class flight) towards cities and suburbs
Both main cities have one or several seclusion axes: an urban area of a few
municipalities linked to a transportation pathway through which high class population settles
and sprawls through the metropolitan territory. The seclusion axes start in the main city,
where high class congregation is high, and includes both low density suburban areas and
dense secondary city centers populated homogeneously by high classes. In the CAM this axis
is formed by the municipalities of Tres Cantos, Villaviciosa de Odón, Villanueva de la
Cañada, Torrelodones, Las Rozas de Madrid, Majadahonda, Hoyo de Manzanares, Boadilla
del Monte y Pozuelo de Alarcón; in the RMB the main seclusion axis includes Sant Cugat del
Vallès y Sant Quirze del Vallès. Other axes would be the one composed by municipalities of
Baix Maresme and the municipalities towards Sitges for Barcelona; and the one that includes
some parts of Alcobendas and San Sebastián de los Reyes, goes thorugh San Agustín de
Guadalix and ends in Venturada for Madrid. In those municipalities the elite and high classes
can establish their main residency keeping centrality, high living and environmental
standards, and lesser costs and taxes. Those municipalities are well connected with the main
city, well equipped with public services, public and private transport systems.
4.3 Segregation indices
The differences and uneven size of both metropolitan regions (CAM and RMB)
recommends taking the comparison of segregation indices as a mere indicator. The main
characteristic feature of segregation indices is the symmetry of the results in both areas (see
table 3): the level of general segregation is similar for population in every job category. The
most segregated is the high class and the least segregated is the medium high class (support
professionals, technicians and administrative type employees) who often works as a hinge
between other classes. For bi-group dissimilarity indices, there is an evident gap between
populations with white collar (high and middle high categories) and blue collar jobs (middle
low and low categories). In Madrid, the values increase from 0,26 between high and middle
high to 0,45 and 0,42 for the segregation of high categories from middle low and low,
respectively. In Barcelona there is a similar gap from 0,20 to 0,38 and 0,41. Between both
regions, the segregation indices are slightly superior in Madrid, specially for population with
middle high and low job categories.
Table 3. Segregation and dissimilarity indices for CAM and RMB
Indices of Dissimilarity CAM
High
High
Middle high
Middle low
Low
0,26
0,45
0,42
Middle high
0,26
0,23
0,23
Middle low
0,45
0,23
Segregation index CAM
Low
0,42
0,23
0,14
0,14
Indices of Dissimilarity RMB
High
High
Middle high
Middle low
Low
0,20
0,38
0,41
Middle high
0,26
0,22
0,26
Med Baja
0,45
0,23
0,31
0,17
0,23
0,25
Segregation index RMB
Low
0,42
0,23
0,14
0,13
Source: INE, Census 2001. Own table.
0,31
0,13
0,24
0,20
5. Conclusion
The spatial distribution of high classes follows similar patterns in Madrid and
Barcelona: congregation and gentrification in city centers and seclusion (high class flight)
along high class axes of sprawl. Those distribution patterns have effects on spatial segregation
(the index of high classes segregation is 0,31) and have elective affinities with other strategies
of distinction and social closure.
The congregation pattern groups together the high class populations in socially
homogenous central areas of the main cities, while the seclusion pattern consists of the
localisation of those classes in a series of very homogeneous neighbouring municipalities that
are specialized in high class social profile, as well as of low density areas situated between
those municipalities. The congregation in city centers provides meeting opportunities with
others groups, or the conflictive possibility of gentrifying new areas. That is the case of the
processes of gentrification under way in the sections that are socially polarised in the historic
centres, or other popular neighbourhoods such as Gracia or Embajadores. Those spaces,
whose social profile is mixed, brings together elderly population, immigrants and highcategory jobs holders.
The levels of segregation and the characteristic features of the distribution in both
metropolitan regions are similar: the high class reaches the highest level of segregation and a
gap can be observed between white-collar occupations and blue-collar occupation: the
segregation between groups relative to the population with high-category jobs in Madrid rises
from 0,26 relative to the middle-high jobs at 0,46 and 0,42 for middle-low and low categories.
In Barcelona, there’s a similar gap with segregation indices rising from 0,20 to 0,38 and 0,41
respectively.
Bibliografía
ALABART, A. y GARCÍA S. 1996. “Estructura i canvi social a Catalunya des de la sociologia
urbana”. Revista Catalana de Sociologia, págs. 5–25.
ÁLVAREZ, M.F. 1995. “La transformación del espacio noroeste de la ciudad de Madrid entre 19701990.» P. 41 en Anales de Geografía de la Universidad Complutense, vol. 15.
ARBACI, S. 2008. «(Re) viewing ethnic residential segregation in Southern European cities: housing
and urban regimes as mechanisms of marginalisation». Housing Studies 23(4):589–613.
ARIAS, F, y NICOLÁS, JL. 2000. “La desigualdad urbana en España». Ministerio de Fomento.
Madrid.
ATKINSON, R., y FLINT, J. 2004. “Fortress UK? Gated communities, the spatial revolt of the elites
and time–space trajectories of segregation». Housing Studies 19(6):875–892.
BALL, S., y PETSIMERIS, P. 2010. «Mapping urban social divisions». en Forum Qualitative
Sozialforschung/Forum: Qualitative Social Research, vol. 11.
BAYONA, J. 2007. «La segregación residencial de la población extranjera en Barcelona: ¿una
segregación fragmentada?» Scripta Nova (11):235.
CARABAÑA, J., GONZÁLEZ, J.J., SERRANO, A., y VALLEJOS, A. 1992. «Clases sociales:
estudio comparativo de España y la Comunidad de Madrid 1991». Madrid, Comunidad de Madrid,
Consejería de Economía.
COLL, M., y PUJADAS. I. 2009. «Migració i segregació residencial a la Regió Metropolitana de
Barcelona: Sant Cugat del Vallès com a elecció residencial per a les categories professionals altes».
Treballs de la Societat Catalana de Geografia 35–50.
HOBERG, K., y MARTORI I CAÑAS, J.C. 2004. «Indicadores cuantitativos de segregación
residencial: El caso de la población inmigrante en Barcelona». Scripta Nova: revista electrónica de
geografía y ciencias sociales (8):169.
LEAL, J. 1994. «Cambio social y desigualdad espacial en el área metropolitana de Madrid (19861991)». Economía y Sociedad 10:61–81.
LEAL, J. 2004. «Segregation and social change in Madrid metropolitan region». The Greek Review of
Social Research 113(A):81–104.
MALHEIROS, J. 2002. «Ethni-cities: residential patterns in the Northern European and Mediterranean
metropolises–implications for policy design». International Journal of Population Geography
8(2):107–134.
MORENO, A.,et al. 2003. La distribución espacial de la renta en la Comunidad de Madrid: análisis y
aplicaciones. Instituto de Estadística de la Comunidad de Madrid.
POZO, E. y GARCÍA PALOMARES, J.C. 2009. «Inmigración y cambio demográfico en la región
metropolitana madrileña entre 1996 y 2006». Pp. 111–138 en Anales de geografía de la Universidad
Complutense, vol. 29.
PUJADAS, I. y BAYONA, J. en prensa
WACQUANT, L. 2007. Los condenados de la ciudad: Gueto, periferias y Estado. S. XXI editores.
WRIGHT, E.O. 1997. Class counts: comparative studies in class analysis. Cambridge Univ.