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This study analyzes at a local level (i.e. census tract) the spatial patterns and main contextual factors related to the electoral resurgence of the extreme-right party (VOX) in Southern Spain (Andalusia) in 2018 and 2019. The 2019 electoral data was associated with the percentage of total foreign-born population, degree of territorial concentration of economic migrants, average income level, percentage of elderly people, urban/rural areas and the percentage of vote for VOX in 2018 (t − 1). We used a global and local spatial autocorrelation analysis to detect the spatial patterns of the vote for VOX and a spatial Durbin regression model to assess the role of contextual variables and spatial effects. The results underline the importance of space in modelling the vote for VOX and point to the existence of a spatial diffusion process. Previous electoral behavior and the urban milieu also play key roles in explaining the vote for VOX. Moreover, the territorial concentration of economic migrants is negatively related with the vote for VOX, which illustrates the positive character of interracial contact.
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Vol.:(0123456789)
Spatial Demography (2022) 10:117–141
https://doi.org/10.1007/s40980-022-00105-1
1 3
A Spatial Approach totheStudy oftheElectoral
Resurgence oftheExtreme Right inSouthern Spain
RicardoIglesias‑Pascual1 · FedericoBenassi2 · VirginiaPaloma3
Accepted: 10 March 2022 / Published online: 4 April 2022
© The Author(s) 2022
Abstract
This study analyzes at a local level (i.e. census tract) the spatial patterns and main
contextual factors related to the electoral resurgence of the extreme-right party
(VOX) in Southern Spain (Andalusia) in 2018 and 2019. The 2019 electoral data
was associated with the percentage of total foreign-born population, degree of ter-
ritorial concentration of economic migrants, average income level, percentage of
elderly people, urban/rural areas and the percentage of vote for VOX in 2018 (t 1).
We used a global and local spatial autocorrelation analysis to detect the spatial pat-
terns of the vote for VOX and a spatial Durbin regression model to assess the role
of contextual variables and spatial effects. The results underline the importance of
space in modelling the vote for VOX and point to the existence of a spatial diffu-
sion process. Previous electoral behavior and the urban milieu also play key roles
in explaining the vote for VOX. Moreover, the territorial concentration of economic
migrants is negatively related with the vote for VOX, which illustrates the positive
character of interracial contact.
Keywords Migrants· Extreme right-wing vote· Southern Spain· Spatial patterns
* Ricardo Iglesias-Pascual
riglpas@upo.es
Federico Benassi
benassi@istat.it
Virginia Paloma
vpaloma@us.es
1 Universidad Pablo de Olavide, Edificio nº 2, Ctra. de Utrera, km. 1, 41013Sevilla, Spain
2 Italian National Institute ofStatistics (Istat), Rome, Italy
3 Universidad de Sevilla, Sevilla, Spain
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1 Introduction
The significant growth in electoral support for extreme right-wing parties,
together with the increased support for anti-Europeanist positions (Allen, 2017;
Dijkstra etal., 2020; Rodríguez-Pose & Dijkstra, 2021) are among the key social
challenges facing Western European societies in order to maintain the levels of
social and territorial cohesion they have enjoyed since the end of the Second
World War. The proliferation of the xenophobic, nationalist, authoritarian, and
exclusionary discourses of these political parties has resulted in their presence
in different government institutions at local, regional, and national levels (David
etal., 2018), and the parties have even contributed to the formation of coalition
governments in countries such as Hungary or Poland. The studies which have
analyzed this phenomenon have so far been carried out mainly in central and
northern Europe, as this is where this type of political party first gained elec-
toral support (e.g. Biggs & Knauss, 2012; Bowyer, 2008; Coffe etal., 2007; Ford
& Goodwin, 2010; Johnston etal., 2000; Lubbers et al., 2002; Pattie & John-
ston, 1999). However, this electoral trend and populist discourse have also begun
to develop in other Eastern European countries (e.g. Rehák etal., 2021), South
America (de Souza, 2020), Turkey (Deniz etal., 2021), and southern European
countries such as Italy (Bobba & McDonnell, 2016; Passarelli, 2013), Greece
(Charalambous & Christoforou, 2018), and, more recently, Spain, with the
appearance of VOX (Simón, 2020).
It should be remembered that in the 1979 Spanish general elections, an extreme
right-wing party called Fuerza Nueva, which was a successor to the previous dic-
tatorial regime, obtained one parliamentary representative with 2.1% of the vote.
However, the new political party VOX cannot be considered as a successor to
Fuerza Nueva, nor does it bear a direct relationship with the political forces which
express nostalgia for General Franco’s dictatorship. VOX represents a break-off
group from the ideological environment of the PP (Popular Party) which has
become disillusioned with what they understand to be the PP’s drift towards the
moderate center (Ortiz Barquero etal., 2020). Thus, the arrival of VOX has led
to a greater polarization of Spanish politics, and the party has assumed an ideo-
logical discourse in line with other European far-right populist parties, based on
national patriotism, cultural traditionalism, and a clear anti-immigration stance as
the central axis of its political message. VOX emerged for the first time in Spain
in December 2018, during the regional elections held in Andalusia, the southern-
most region of this country. This extreme right-wing party obtained 10.97% of
the total votes and twelve seats in the Andalusian Parliament. Later, in November
2019, VOX was the third most-voted party in the country in the national elec-
tions, thus ending the total absence of the extreme-right in the Spanish Parlia-
ment since the restoration of democracy in 1978.
This study aims to analyze the spatial pattern and contextual determinants of
the vote for the extreme right, in order to achieve a better understanding of the
behavior of this political party from the moment it first appeared on the scene
(Iglesias-Pascual etal., 2021). Given the novel character of this electoral trend
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A Spatial Approach totheStudy oftheElectoral Resurgence…
in Andalusia and Spain and its unequal acceptance in Spanish territory (Simón,
2020; Turnbull-Dugarte, 2019;Turnbull-Dugarte etal., 2020), and to investigate
in greater depth the geographical diversity of the VOX vote in Andalusia, we first
decided to focus our analysis on identifying spatial clusters of the vote for VOX
in the 2018 and 2019 Andalusian elections. Lastly, using the percentage of vote
for VOX in 2019 as a dependent variable, we applied a Spatial Durbin model to
assess, on the one hand, the impact of the main contextual variables usually ana-
lyzed in the academic literature to analyze right-wing vote, and, on the other, to
verify the existence of a spatial diffusion trend in electoral behavior.
To achieve this, our study is based on 5944 census tracts from Andalusia. The
census tract is the smallest statistical unit used in Spain, covering, on average,
between 800 and 1000 voters. Using this local scale has allowed us to make a sig-
nificant number of observations which make our statistical analyses more robust
(Rydgren & Ruth, 2013) and to use areas of great socio-economic uniformity as our
units of analysis.
We hope to contribute to the studies of the electoral geography of extreme right-
wing parties by analyzing (i) the spatial dimension of the electoral resurgence of an
extreme right-wing party in Spain; (ii) the main contextual factors relating to the
electoral resurgence of the extreme right-wing in Andalusia in the 2018 and 2019
elections; and finally (iii) to determine whether this recent phenomenon in southern
Europe presents a similar pattern to those already analyzed in other European coun-
tries or, on the contrary, shows different patterns of behavior.
Firstly, we will review the main contextual variables highlighted in the literature
that account for the vote for extreme right-wing parties. Secondly, we will present
data about Andalusia as our study context and describe the method carried out.
Then, we will show the results, classified according to the three established objec-
tives. Finally, we will discuss the results obtained in light of the existing literature,
analyze their main implications and offer ideas for lines of future research.
2 Explaining theVote fortheExtreme‑Right Party
To detect the contextual variables related to the electoral growth and geography of
the radical right, research carried out mainly in central and northern Europe has
highlighted the relationship between the presence of a migrant population or ethnic
minorities (Coffe etal., 2007; Lubbers etal., 2002; Savelkoul etal., 2017; Schneider,
2008; Stockemer, 2016; Valdez, 2014) and the socio-economic level of the receiving
native population, on the one hand, and the tendency to support extreme right-wing
forces on the other (Biggs & Knauss, 2012; Hjerm, 2009).
2.1 Migrant Population andSupport fortheExtreme Right
The presence of a migrant or ethnic minority population has been highlighted by
nearly all the studies as the key element to explain the vote for the extreme right.
When analyzing this variable, studies have focused on aspects such as the degree
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and evolution of ethnic diversity in the area studied (Arzheimer & Carter, 2006;
Bowyer, 2008; Lubbers & Scheepers, 2000; Savelkoul etal., 2017; Van Gent etal.,
2014), or the nationality of the immigrant population (Coffe etal., 2007; Rydgren &
Ruth, 2013).
This presence of migrants has been traditionally measured by the proportion of
the population belonging to the minority group in each of the areas analyzed. Stud-
ies ascribing to the conflict theory affirm that contact between the migrant popula-
tion and the receiving society is related to anti-immigrant prejudice, and that this
increases with the out-group size (Semyonov etal., 2004). The greater the anti-
immigrant prejudice, the more support for radical right-wing parties (De Blok &
Van der Meer, 2018; Green et al., 2016; Kaufmann, 2017), especially when the
migrant population is of non-Western origin (Biggs & Knauss, 2012; Ford & Good-
win, 2010).
However, rather than focusing on the proportion of the migrant population, recent
studies in Spain (Iglesias-Pascual et al., 2019) and Europe (Hoxhaj & Zuccotti,
2020) have underlined the importance of including the territorial concentration of
the migrant population, as this can be considered a key element to understanding the
attitudes held by the receiving society and the support for parties with radical right-
wing ideology. The territorial concentration shows the degree of visibility of a social
group in a specific territorial area (van Wijk etal., 2019) and most certainly helps
to indicate the likelihood of real contact between migrants and the receiving soci-
ety. The contact theory argues that the greater the territorial concentration of immi-
grants, the more familiarization and contact there is with the ethnic minority popula-
tion, and the more positive attitudes there are towards this population (Pettigrew &
Tropp, 2006). It therefore follows that ethnically diverse environments should pro-
duce a greater possibility of contact and should reduce electoral support for far-right
parties (Biggs & Knauss, 2012; Kaufmann, 2017). This is found in some studies
(Arzheimer & Carter, 2006; Biggs & Knauss, 2012; Janssen etal., 2019), which
state that contact and daily observation in the immediate environment favor famil-
iarization with the immigrant population (Schneider, 2008) and, in turn, reduce the
probability of supporting radical right-wing parties (Lonsky, 2021; Savelkoul etal.,
2017). However, other studies relate this micro-scale to a tendency to support the
far-right more (Kaufmann & Goodwin, 2018). In fact, these areas of migrant territo-
rial concentration in municipal areas with a low presence of migrants could play a
key role in the support for extreme right parties (Charitopoulou & García-Manglano,
2018). As a result, the research has not yet offered generalizable results, and there is
a significant disparity when it comes to interpreting the influence of the proportion
or territorial concentration of immigrants and the increase in right-wing voting.
2.2 Socio‑economic Context andSupport fortheExtreme Right
Another factor that has been closely linked to the likelihood of voting for extreme
right-wing parties is the socio-economic composition of neighborhoods. Thus,
the perception of competition for resources and employment, leads to a potential
conflict between the minority group and especially among the most disadvantaged
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population of the host society (Blalock, 1967; Semyonov etal., 2008). In fact, some
studies even stress that socio-economic factors are of greater importance than the
proportion of the immigrant population in accounting for the increased support for
radical right-wing parties (Rydgren & Ruth, 2013).
When explaining the characteristics of the socio-economic context, two variables
are usually used to categorize them: the level of unemployment and average income
(De Blok & Van der Meer, 2018). Regarding unemployment, a significant number of
studies have found a positive relationship between the level of unemployment in the
area and the probability of voting for far-right parties (David etal., 2018; Rydgren
& Ruth, 2013; Strömblad & Malmberg, 2016). However, not all studies have found
a close link and, in some cases, unemployment rates have been shown to be a poor
indicator of electoral support for these parties (Arzheimer & Carter, 2006; Green
etal., 2016; Lubbers etal., 2002).
In view of the lack of clear indications that unemployment is a predictor of the
vote for the extreme right, other research has chosen to assess the characteristics of
the context through the level of average income, either as a complementary factor or
as a main variable (Lubbers etal., 2000; Van Gent etal., 2014). The level of wealth,
in turn, allows us to directly link the relationship between voting for the extreme
right with the idea of social class, which allows a more realistic overall assessment
of the environment than that offered by unemployment.
2.3 The Neighborhood Effect andtheSpatial Diffusion Process
The role of the neighborhood effect has been extensively analyzed in the general
literature on electoral geography (Gallego etal., 2016) but has been almost totally
neglected in studies that have analyzed the electoral growth of extreme-right parties
(van Wijk etal., 2019). In highlighting the importance of the neighborhood in the
way people vote, Miller (1978) showed how people are more likely than would be
expected to support a party in their neighborhood based solely on their individual
characteristics. This effect has been reaffirmed in different investigations (Gallego
etal., 2016; Johnston & Pattie, 2006). Analyzing this aspect can therefore contrib-
ute to understanding the expansion and geographical diffusion of the vote for the
extreme right and how this ideology is reproduced from its first areas of support to
later growth throughout the territory. In this context, and in general in our study,
the concept of spatial diffusion process constitutes a highly relevant issue. In the
words of Morrill etal. (1988, p. 7): “The spatial diffusion process is the process
by which the behaviour or characteristics of the landscape change as a result of
what happened elsewhere earlier. Spatial diffusion is the spread of the phenomenon,
over space and time, from limited origins”. The idea is that the diffusion through
space of a given behavior (for example, in our case, electoral behavior) can occur
through two processes: by contagion or through a hierarchical process (Doignon
etal., 2021; Morrill etal., 1988; Saint-Julien, 2007). In this paper, we deal with
both of these concepts of the spatial diffusion process. Firstly, we deal directly with
this point by using a spatial global and local autocorrelation analysis and a spatial
regression analysis (Spatial Durbin Model) based upon the concept of the first law
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R.Iglesias-Pascual et al.
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of Geography (Tobler, 1970), which can be considered as the theoretical background
to all spatial diffusion process theories. Secondly, we deal with it indirectly when we
use the urban/rural divide as a covariate in the spatial regression model, assuming
that urban census tracts are hierarchically higher compared to rural census tracts.
2.4 The Urban/Rural Dimension intheExtreme‑Right Vote
The relationship between right-wing extremist voting and the contextual factors sug-
gests that voting behavior differs according to urban and rural areas (Alba & Foner,
2017; David etal., 2018; van Gent etal., 2014). Here, we encounter opposing views.
While some studies affirm that there is a marked urban character of the vote for the
extreme right (David etal., 2018), other studies developed in Europe highlight the
more rural and suburban character of the vote for the extreme right (de Maesschalck,
2011; Fourquet, 2012; van Gent & Musterd, 2013). In these cases, anti-urban senti-
ment, revanchism in the face of social change and the deterioration of public ser-
vices are cited as key elements which explain the increased support for the extreme
right in non-urban areas (Rickardsson, 2021; van Gent etal., 2014). In this context,
the first contextual studies carried out in Andalusia show that although the tendency
is broadly similar, the association with the vote for the extreme right is greater in
rural areas than in metropolitan areas (Iglesias-Pascual, Hurtado-Rodríguez & De
Oliveira, in press).
3 Context andMethod
3.1 Andalusia: theContext oftheResurgence oftheExtreme Right inSpain
Since the restoration of democracy in Spain in 1978, the political landscape has
been dominated electorally by a two-party system consisting of the PSOE (Span-
ish Socialist Workers Party) and the PP (Popular Party), without any significant
presence of alternative political forces such as green parties or the extreme right.
As a result of the economic crisis starting in 2008, two new parties arrived on the
scene (Ciudadanos and Podemos), which clearly weakened the dominance of this
two-party system, and led to a new, highly unstable political scenario. Against this
backdrop, after successive elections failed to produce a solid parliamentary major-
ity, VOX managed to become the third most popular political party in the Spanish
parliament in the November 2019 elections (15, 1% of the vote and 52 parliamen-
tary seats). However, this rapid electoral growth in Spain in 2019 was anticipated
by their emergence in the 2018 Andalusia elections, in which they obtained 395,978
votes (10.97%).
Andalusia, the southernmost region of Spain, accounts for 17.9% of the Span-
ish population, constitutes the most densely-populated region of the country and
contains 7.4% of the foreign-born population, which is below the national average
(10.1%; INE, 2019). A significant proportion of the foreign-born population resident
in Andalusia is made up of European Union nationals who have chosen to live in
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A Spatial Approach totheStudy oftheElectoral Resurgence…
this region during their retirement (Rodríguez etal., 2004). The foreign-born popu-
lation that could be classed as economic migrants is therefore even smaller. Further-
more, within Andalusia, the foreign population is not evenly distributed, with the
provinces of Malaga and Almeria being home to 60%. However, despite the lesser
importance of the foreign population in the region, the strategic position of Andalu-
sia as one of the southernmost borders of the EU and the important role it plays as
an entry point for immigration flows from Africa ensure that the social perception of
the presence of immigrants has become a hotly-debated issue in Andalusian politics.
Despite this, it is generally considered that anti-immigrant attitudes in Andalu-
sia have always been moderate, and not even the grave social consequences arising
from the 2008 economic crisis led to a growth in these feelings (Rinken & Trujillo-
Carmona, 2018). However, results from a recent survey on the attitudes of the Anda-
lusian population to immigration (OPAM, 2019) show an increased perception of
immigration as a problem among the Spanish population in general and the Andalu-
sian population in particular. These social attitudes, although not held by the major-
ity, constitute a significant change in the trend of Andalusians’ attitude towards
immigration and could be related to the emergence of VOX in December 2018.
3.2 Study (Dependent) Variable andIndependent Variables
The study variable is the percentage of VOX votes in 2019 recorded at the 10,033
polling stations in the eight Andalusian provinces. In order to maintain a consistent
data unit level, we grouped the data according to the 5,944 census tracts in Anda-
lusia, as the contextual factors are usually described using this scale. For the 2019
elections, which were national elections, the data were obtained from the Spanish
Ministry of the Interior. As can be observed from Fig.1, the geographical distribu-
tion of the study variable presents a fairly high spatial variability in 2019. In particu-
lar, the greatest electoral support for VOX is to be found in coastal areas, normally
characterized by tourism and intensive greenhouse agriculture. To a lesser extent,
we find isolated areas of high support for VOX in the interior of Andalusia, the pro-
vincial capitals and some areas with a mainly rural economy.
From a statistical point of view, it can be seen (Table1) how the average level of
the variable in 2019 was fairly high (20.3%), which corresponds to a certain level of
variability (sd is equal to 6.4).
Electoral data from each census tract were then associated with the contex-
tual factors considered in this study. We included demographic (INE, 2018) and
socio-economic (INE, 2017) data obtained from the Spanish National Institute of
Statistics.1
The contextual variables used in the regression model are related to the presence
of foreign population, the degree of territorial extent of economic migrants, the level
of wealth, the level of aging in the resident population, the urban/rural divide and,
1 For the demographic data, we used information from Continuous Municipal Population Register of
2018, the date of the first election analyzed. For the socio-economic data, that of 2017 was used, data
issued by Spanish Household Income Distribution Atlas of the Spanish National Statistical Institute
(INE).
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R.Iglesias-Pascual et al.
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finally, the percentage of vote for VOX in 2018 (t 1). The presence of foreign-born
population was measured by using the percentage of foreign-born population in the
total population resident in each census tract. By using this variable, we aimed to
analyze the different relationships with the extreme right produced by the presence
of foreigners. The territorial concentration of economic migrants was been meas-
ured by the use of Local Quotient (LQ) only in a subset of the foreign-born popula-
tion, the economic migrant population. For the identification of this sub-group of
population, we consulted reports from the Spanish Monitoring Centre on Racism
and Xenophobia (Cea D’Ancona & Vallés, 2015) and other studies (Iglesias-Pascual
etal., 2019, 2021), which identified economic migrants as the foreign-born popula-
tion from South America, Central America and the Caribbean, Africa, Asia, and
Fig. 1 Geographical distribution of the study variable (percentage of vote for VOX 2019) at census tract
level (quintiles map)
Table 1 Descriptive statistics of
the study variable (vote for VOX
2019%)
Statistical parameters 2019
Min 0.6
Max 48.8
Q1 16.2
Median 20.1
Q3 24.0
IQR 7.8
Mean 20.3
Standard deviation 6.4
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A Spatial Approach totheStudy oftheElectoral Resurgence…
those from Russia, Ukraine, Romania, Poland and Bulgaria. The LQ allows us to
compare the percentage of foreign-born individuals of X country relative to popu-
lation T of a particular section i with the percentage that shares the same country
across the administrative municipality. This indicator, which varies from 0 to
,
enables us to assess the over-representation (LQ > 1) of an immigrant population in
a city’s different census tracts, and has been used successfully in studies conducted
in several cities (Iglesias-Pascual etal., 2019; Wright etal., 2005). In the dimension
of socio-economic status, we used the average income level (Gross Domestic Prod-
uct per capita) from the data of the Spanish Household Income Distribution Atlas
(INE, 2017). Demographic ageing is measured as the percentage of elderly people
(aged 65 and over) in the total population resident in each census tract. The idea is
that where the proportion of elderly people is higher the ‘traditional’ values have a
major social impact, and therefore the ‘racial prejudice’ tends to be more accentu-
ated (Ford, 2011; Gorodzeisky, 2011; Pichler, 2010). The urban/rural area divide
is a crucial variable in explaining many aspects of human behavior. In their demo-
graphic studies, Champion and Hugo (2004) noted that where people live can be a
relevant factor in explaining demographic behaviour and, therefore, social attitudes.
However, the latter are also strictly linked to electoral behavior (e.g. David etal.,
2018; Rickardsson, 2021; van Gent etal., 2014). To differentiate between urban and
rural contexts, our research considered the OECD delimitation for urban areas in
Europe (Dijkstra etal., 2019). Finally, in order to measure how the 2018 percentage
of vote for VOX can have influenced for Andalusia the level of the same variable in
national elections, one year later—i.e. our dependent variable—we used as a covari-
ate the observed variable at t − 1, i.e. one year before. The idea here is quite clear: in
our view, the temporal dimension can be a relevant factor in explaining the VOX´s
electoral growth and especially its dimension of temporal diffusion. In Table2 and
Fig.2, information about the independent variables used in the model is given.
3.3 Statistical Analysis
The first step was to detect the level of global and local spatial autocorrelation of
the study variable (in 2018 and in 2019). In particular, we were interested in ver-
ifying whether and how the study variable is clustered across the whole territory
and whether its level of spatial autocorrelation (i.e. spatial clustering) changed from
2018 and 2019. For this purpose, we considered the classic global Moran’s I index
of spatial autocorrelation (1948) and, as a measure of local spatial association, the
local index
G
i
(Ord & Getis, 1995). Both indicators allow us to detect whether the
spatial distribution of a variable –represented here by the percentage of vote for
VOX—follows a random distribution or, in contrast, is characterized by spatial auto
correlation (i.e. spatially clustered). The difference between the two indicators is
that Moran’s I is a global index and is therefore a single number that varies between
− 1.0 (negative spatial autocorrelation, i.e. similar values of the variable which tend
to repel each other spatially) to + 1.0 (positive spatial autocorrelation, i.e. similar
values which tend to attract/to cluster one each other in space). On the opposite the
Ord and Getis index (
G
i
) is a local indicator which enables us to identify clusters of
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R.Iglesias-Pascual et al.
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Table 2 Descriptive statistics of the independent variables used in the regression analysis
Independent
Variables
Min Max Q1 Median Q3 IRQ Mean Standard deviation
Foreign-born
population
(%)
0.0 72.4 1.7 3.3 7.2 5.5 6.4 8.3
LQ economic
migrants
0.0 8.4 0.5 0.8 1.3 0.8 0.9 0.7
Average
annual
income
2987.0 27745.0 7308.0 8283.5 9829.5 2521.5 8931.5 2664.2
Elderly people
(%)
0.9 50.0 13.6 18.6 23.5 9.9 18.6 7.1
Vote for VOX
2018 (%)
0.0 36.9 6.9 9.9 13.0 6.1 10.3 4.9
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A Spatial Approach totheStudy oftheElectoral Resurgence…
hot spots (i.e. census tracts in our case, where high values of the observed variable
are spatially clustered) and cold spots (census tracts in our case, where comparative
low values of the observed variable are spatially clustered). The hypothesis for the
existence of a condition of spatial clustering is tested at a level of statistical signifi-
cance ≤ 5% (p-value 0.05). We also followed a spatial approach for statistical mod-
eling of VOX voting: from the several models existing in the literature, we adopted
the spatial Durbin model (Anselin, 1988). The reasons for this choice are straight-
forward. In fact, the spatial Durbin model has been proved to outperform the more
classic autoregressive spatial econometrics models such as spatial error or spatial
lag because, as underlined in Yang etal. (2015), it is the only means of producing
unbiased coefficient estimates (Elhorst, 2010). Moreover, the kind of model used
in our case study allows us to detect if it is the characteristics of neighboring ter-
ritories (i.e. census tracts) that influence the proportion of vote for VOX in a given
territory (i.e. a specific census tract i), also taking into account the spatially-lagged
effect of y. In other words, this kind of spatial regression model allows us to find
Fig. 2 Territorial distribution of the contextual variables used in the regression analysis. Maps created
using Qgis ‘Odense’ version 3.20.2. These categories have been computed using the Jenks methods (nat-
ural breaks) for the variables: “Foreign population (%)” and “Elderly people (%)”; for LQ variables, the
categories were computed manually on the basis of the meaning of LQ and its distribution. For “Average
annual income per person” and “Percentage vote for VOX 2018”, the categories were computed as quin-
tiles
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R.Iglesias-Pascual et al.
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more evidence to support the discourse on the spatial diffusion process (in a strictly
geographical sense—see Sect.2.3). In fact, following the work of Yang etal. (2015),
a spatial Durbin models are made up of three components (LeSage & Pace, 2009):
a spatially-lagged dependent variable, a set of explanatory variables belonging to a
spatial unit (in our case, the census tract), and a set of spatially-lagged explanatory
variables. The model, again following Yanget al. (2015), can be expressed as:
where y denotes an n × 1 vector of the dependent variable (i.e. percentage of vote
for VOX in 2019), W is the spatial weight matrix,
Wy
represents the spatial lagged
depedent variable (endogenous interaction relationship), ρ denotes the effect of
Wy
,
known as the spatial autoregressive coefficient, and
ln
indicates an n × 1 vector of
ones associated with the intercept parameter α. X represents an n x k matrix of k
independent variables (see previous section), which are related to parameters β; WX
reflects the spatially-lagged explanatory variables (exogenous interaction relation-
ships), and θ denotes an k × 1 vector of the effects of WX. The error term, ε, fol-
lows a normal distribution with a mean 0 and a variance
𝜎2In
, where
In
is an n x
n identity matrix. The equation above clearly indicates that the characteristics of a
specific unit (the census tract, in this study) and its neighbors are considered simul-
taneously in the analysis (Yang etal., 2015). This paper uses this spatial regression
approach to detect whether the percentage of vote for VOX in 2019 in any census
tract is related to the features of its neighborhood and, if so, to discover how they are
associated. Specifically, we looked into whether the dependent variable of a census
tract is associated with the features of surrounding census tracts after accounting for
the characteristics of the specific census tract, in terms of the independent variables
introduced in the model. In both spatial analysis procedures—i.e. global/local spa-
tial autocorrelation and spatial regression—the spatial weight matrix (W) is defined
as a “Queen” contiguity of first order: two census tracts are neighbors if and only
if they share a boundary or a geographical vertex. One crucial aspect of the spatial
Durbin model, and in general of all spatial autoregressive regression models, is the
interpretation of the coefficients: this cannot be done in the same way as OLS mod-
els, as the direct and indirect (spatial spillover) effects need to be taken into account
(Golgher & Voss, 2016). According to these authors, the direct effect “represents
the expected average change across all observations for the dependent variable of a
particular region due to an increase of one unit for a specific explanatory variable
in this region” (Golgher & Voss, 2016, p. 185), while the indirect effect “repre-
sents the changes in the dependent variable of a particular region arising from a
one-unit increase in an explanatory variable in another region” (Golgher & Voss,
2016, p. 185). In our analysis, for “region”, we should read “census tract”. In the
analysis, we compare the classic regression model (OLS) and spatial Durbin model
(SDE) using AIC (Akaike, 1974) to determine whether one model is better than the
other, assuming that the model with a smaller AIC value should be preferred, as it
is more likely to minimize the information loss compared to the true model which
generates the observed data (Burnham & Anderson, 2002; Yang etal., 2015). From
an interpretative point of view, the comparison between OLS and SDE is relatively
=𝜌W
+𝛼l
+X𝛽+WX𝜃+𝜀,𝜀N(0, 𝜎
I
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A Spatial Approach totheStudy oftheElectoral Resurgence…
straightforward: OLS model parameters are estimated under the explicit assumption
that the observations are independent, which means that changes in values for one
observation do not “spill over” to affect values in other observations (Golgher &
Voss, 2016). Spatial regression models, on the other hand, assume that observations
are not independent and can influence each other reciprocally, as is clearly stated
in the first law of geography by Tobler (1970). Global and local univariate and
bivariate spatial analysis is carried out using GeoDa (version 1.18 10.12.2020), and
regression models (OLS and SDE) are estimated using R Studio (Mendez, 2020).2
The thematic maps were created using Qgis “Odense” version 3.20.2.
4 Results
4.1 Spatial Diffusion Process oftheVote forVOX3
Our first objective was to analyze the spatio-temporal variations of votes for VOX
in Andalusia and detect spatial clusters (hot spots, in particular). The variable is the
percentage of votes for VOX in 2018 and 2019 (i.e. a single variable referring to two
years). From 2018 to 2019, the statistical parameters of the study variable changed
significantly (see Table1 and the last row of Table2), with the mean rising from
6.1% to almost 8% and the variability also increasing (standard deviation rose from
4.9 to 6.4). However, the biggest change was seen in the impressive increase of the
median: 9.9% in 2018 and 20.1% in 2019.
For our purposes, the geographical distribution of the vote for VOX in 2018 and
2019 and its variations across space and time are highly relevant. This is a crucial
Fig. 3 Geographical distribution of votes for VOX in 2018 and 2019
2 The package used are: ‘tidyverse’, ‘spdep’, ‘spatialreg’, ‘rgdal’, ‘rgeos’ (Mendez, 2020).
3 The analysis and processing of the data was carried out at census tract level; however, the cartographic
representation of the results has been expressed by showing a map of the municipal boundaries. Our aim
here is to show the results more graphically, in a way which is more closely related to the political and
social reality of Andalusia.
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aspect to investigate when the spatial units of analysis are, as in our case, territories,
and especially when we are attempting to detect the spatial diffusion patterns of a
given process/phenomenon (Matthews & Parker, 2013). From Fig.3, it can be seen
how the number of territorial units (i.e. census tracts area) with a percentage of vote
for VOX greater than 25% was as little as 69 in 2018 (1.2% of all census tracts in
the region), while the percentage vote for VOX was less than 10% in 3,014 territo-
rial units (almost 51%). One year later, the situation had changed dramatically: the
census tracts with over 25.0% of vote for VOX had grown to 1,228 (almost 21% of
all census tracts), while in the lowest category (< 10.0% vote for Vox) the number of
territorial units had decreased to 292 (4.9% of the total census tract).
From the maps in Fig.3, it can clearly be seen how spatial proximity influenced
the geographical variation of the vote for VOX from 2018 to 2019. This aspect
allows us to hypothesize the existence of a spatial diffusion pattern which will be
better to investigate using spatial autocorrelation analysis and the spatial Durbin
model. For now, it is interesting to note that after an initial concentration of the VOX
vote on the coast and in specific inland areas in 2018, one year later this pattern had
not only consolidated, but had spreading throughout the region. The results about
spatial global and spatial autocorrelation (Fig.4) provide some other important ele-
ments that help us to understand the spatio-temporal variation of the vote for VOX.
First of all, the spatial distribution of the variable under study is fairly spatially clus-
tered in both years. In fact, the global Moran’s I index remains practically stable
from 2018 to 2019 (0.66 and 0.65, respectively), indicating a condition of positive
spatial autocorrelation at global level. This means that in both years, a similar value
of the percentage vote for VOX tended to spatially clustered. This holds from a local
perspective too: in 2018, there were 915 hot spots, while in 2019, there were 982
(15.4% of the total census tract of Andalusia in 2018 and 16.5% one year later).
What is important to keep in mind, however, is that this stable level of spatial auto-
correlation (both at a global and local level) is associated with a very intense growth
in the level of vote for VOX from 2018 to 2019 (see Fig.3). What also seems inter-
esting to note is that the little growth there was in the number of hot spots from
2018 to 2019 (+ 67) seems to have originated in some cases from some of the major
municiapility (Malaga, Granada and Jerez de la Frontera), while in other cases this
was not so (especially the clusters that are scattered on the east and on the west bor-
der). From this analytical perspective, it will be interesting to understand the role of
urban–rural divide in influencing (or not) the geographical variation of the percent-
age of vote for VOX and, of course, the value of y at t 1 too (Table3).
4.2 OLS andSpatial Durbin Model
The results of the model are highly interesting. As a matter of fact, the spatial Dur-
bin model outperformed the OLS model (with a lower AIC in the case of the spatial
Durbin model). In the OLS model, all the coefficients are statistically significant. For
two variables—foreign-born population (%) and vote for VOX (%)—the relationship
with the dependent variable is positive. The effects are extremely weak in the case
of the first variable (0.08) while, in contrast, they are fairly relevant for the second
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A Spatial Approach totheStudy oftheElectoral Resurgence…
variable (1.11). The other four variables (LQ economic migrants, average annual
income, elderly people (%), urban) show an inverse relationship with the depend-
ent variable. In the case of average annual income and elderly people, the effects on
the dependent variable were very low. In contrast, LQ economic migrants and, in
particular, the urban variables both present high effects: 0.46 and 1.32 respec-
tively. Therefore, from an a-spatial perspective, it seems that the contextual factors
which have a more relevant effect on the decrease in vote for VOX, all conditions
Fig. 4 Local spatial clusters (based on local index G*) for the variable “vote for VOX (%)”. 2018 and
2019. Main urban areas are those ones with over 200,000 residents in 2018
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132
R.Iglesias-Pascual et al.
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being equal, are an urban rather than rural area, and a comparatively higher level of
concentration of economic migrants. In addition, the most important factor which,
all conditions being equal, boosted the percentage of vote for VOX in 2019 is rep-
resented by the same variable at t 1 (2018). This implies a sort of ‘endogenous’
effect of the electoral behavior.
The effects of the other contextual variables are less relevant in the OLS model.
However, results of the spatial Durbin model are somewhat different from the OLS
model. In fact, two variables which are not more statistically significant (LQ eco-
nomic migrants and urban area) both recorded an inverse relationship with the
dependent variable in the OLS model. When interpretating the results, although we
cannot directly interpret the coefficients (as in the OLS model), we do we need to
take into consideration the direct effect (DE) and indirect effect (IE). The foreign
population (%) has a positive, weak, direct effect on the dependent variable, while
the indirect effect is not statistically significant. The opposite is true for the LQ eco-
nomic migrants effect, which still has a negative impact on the dependent variable,
although it is statistically significant only as an indirect effect. It seems that the two
variables related to the presence of foreigners had opposite effects on the percentage
of vote for VOX in 2019, albeit with different spatial effects (a direct effect in the
former, an indirect effect in the latter). Average income maintains a weak effect on
the dependent variable which is negative both in terms of direct and indirect effects.
The percentage of vote in 2018 still plays a crucial role, with a positive and high
effect. Both direct and indirect effects play a role on y, with the former having a
stronger impact (0.95). Meanwhile, the variable related to elderly people plays a
Table 3 OLS and spatial Durbin model
Coefficients in bold are not statistically significant. All others are statistically significant at least at
p ≤0.05
Independent variables Ordinary least square Spatial Durbin model
Coefficients Coefficients DE IE TE
Intercept 15.8300 5.5977
Foreign-born population (%) 0.0755 0.0325 0.0336 0.0170 0.0506
LQ economic migrants − 0.4565 0.0324 0.0739 − 0.6200 − 0.6939
Average annual income − 0.0005 − 0.0001 − 0.0002 − 0.0007 − 0.0008
Vote for VOX in 2018 (%) 1.1060 0.9280 0.9493 0.3192 1.2686
Elderly people (%) − 0.0813 − 0.0829 − 0.0807 0.0328 − 0.0479
Urban (opp. to rural) area − 1.3180 0.6704 0.5620 − 1.6196 − 1.0576
Lag in foreign population (%) 0.0151
Lag in LQ economic migrants − 0.2063
Lag in average annual income − 0.0002
Lag in vote for VOX 2018 (%) − 0.4916
Lag in elderly people (%) 0.0664
Lag in urban (opp. to rural) − 1.0342
Rho (spatial lag parameter) 0.6560
AIC 32,186 29,889
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secondary role in explaining the variability of the vote for VOX. Its effects are again
negative, but it is statistically significant only in the direct form and with low inten-
sity (0.08). The last variable, urban area, is extremely interesting: in the OLS models
it was the variable with the highest (negative) impact on the vote for VOX in 2019,
which indicates that this electoral behavior is somehow related to the rural context
(less modern and most probably less culturally developed, i.e. more conservative).
In the spatial Durbin model, it can be seen how this effect is entirely attributable
to an indirect effect, and that it is very strong (− 1.62). The effects of the lagged
independent variables are also of great interest: these are all statistically significant,
with the exception of the foreign-born presence (%), proving the importance of the
regression approach adopted here. Another interesting result is that the Lag LQ for
economic migrants is now statistically significant, although not in terms of DE, and
exercises a negative effect on the dependent variable. The lagged average annual
income and elderly people still have a comparatively weak effect on the dependent
variable (negative in the former case and positive in the latter). However, it is impor-
tant to note that the lagged vote for VOX in 2018 changed to a negative (previously
positive) effect on the dependent variable. This means that the electoral behavior
was only partially transferable in space from one year to another. Most probably,
other local effects played a key role in this process (for example, the different candi-
dates and their electoral campaigns, local media, and so on). The urban/rural divide
has also played a key role in other, better-known electoral/political polls, such as
the Brexit referendum. Here, as can be seen in the lagged variable related to the
urban/rural divide, there is still a negative, relevant (− 1.03) effect on the dependent
variable. Last but not least, there is the role played by the lagged dependent vari-
able, expressed here by the spatial lag parameter Rho. All conditions being equal,
the effect is positive and intense (about 0.66), which supports the idea of the spatial
diffusion of y. This idea is reinforced by interpreting the indirect effect of the “Vote
for Vox in 2018” variable as it represents the vote for Vox of neighboring territories
in 2018 and it is positive (0.32). This means that the higher the neighboring territo-
ries had a Vox vote in 2018, the higher the Vox vote of territory i in 2019. Based on
these results we believe that we can speak about a spatial diffusion process of the
vote for VOX in Andalucia.
5 Discussion, Limitations andFuture Developments
5.1 Discussion
In this paper, we have taken an original approach to the study of the resurgence of
the extreme right in Andalusia. We first analyzed the spatial patterns of the vote for
VOX in 2018 and 2019. Secondly, we estimated the relationship between a series
of contextual variables and the behavior of the VOX vote in 2019 by comparing a
classic OLS model and a spatial model (spatial Durbin model) in order to compare
the results underlying the importance of the spatial dimension for electoral behavior.
The results suggest that the vote for VOX between 2018 and 2019 was greatly
affected by a global and local spatial autocorrelation, which underlines the
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importance of space in the process of electoral growth. Secondly, the OLS model
reveals a clear relationship between the presence of the foreign-born population
and the higher percentage of the VOX vote. However, the greater degree of territo-
rial concentration, which in this research was associated with a higher possibility
of the native population coming into contact with migrants, together with the aver-
age annual income, percentage of elderly people, and urban areas, show a negative
relationship with the vote for VOX. Nevertheless, the aspatial nature of the OLS
model obviously puts severe limits on its interpretative and explanatory capacity,
given the intrinsically spatial nature of the phenomenon under study. At this point,
it is particularly important to analyze the results of the spatial Durbin model. In this
case, the positive relationship between the presence of foreign-born population and
the vote for VOX was again confirmed, although more weakly and only in terms of
the direct effect. The role of the concentration of economic migrants (LQ), despite
remaining negative, was indirectly statistically qualified by the spatial effect. The
variables of average income and ageing population show a very weak relationship
in both cases (direct and indirect effects). In the case of average income, the results
obtained by this spatial model qualify those obtained in other studies for Andalusia
using non-spatial regression models (Iglesias-Pascual etal., in press). On the other
hand, for the percentage of elderly people, the weakness of the results at the con-
textual level could show the need to analyze the incidence of this variable using
individual data. However, this model perfectly reflects the importance of the VOX
vote in 2018 in understanding the growth of the VOX vote in 2019 and confirms
the important role played by space in the VOX results in 2019. This aspect is also
highlighted in the relationship between urban areas and the VOX vote, underlining
its negative character and confirming the greater importance of non-rural areas in
the vote for the extreme right than other studies have shown (e.g. Rickardsson, 2021;
van Gent etal., 2014).
All these findings have given rise to an interesting academic debate, on both a
theoretical and methodological level, on the role of spatial diffusion patterns and
socio-territorial variables in the increased electoral support for VOX in Andalusia.
First, it is worth highlighting how the diachronic analysis carried out on the VOX
vote between 2018 and 2019 has allowed us to analyze the spatial diffusion process
of the VOX vote. This has enabled us to reflect, from a contextual dimension, on
the neighborhood effect (Dülmer & Klein, 2005) and the role of the local regulatory
context (Johnston & Pattie, 2006; van Wijk etal., 2019) in the vote for the extreme
right. The fact that support for VOX spread out rapidly between contiguous census
tracts allows us to conclude that the proximity to neighborhoods or districts with
high levels of support for the extreme right also plays an important role (Iglesias-
Pascual etal., 2021). In this context, it can be considered that, in our case, spatial
contiguity has favored the expansion of capital and social reproduction, as under-
stood from the viewpoint of Bourdieu (1984), which has influenced the diffusion of
a social imaginary which has been expressed electorally in greater support for the
extreme right.
Second, our results for the presence of the foreign-born population and the degree
of territorial concentration of economic migrants offer new insights into the debate
between the conflict and contact theories. Indeed, the results strengthen the idea that
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the presence of certain nationalities or ethnic groups, especially those that are per-
ceived socially as economic competitors, is linked to greater support for far-right
parties, thus reaffirming the validity of the conflict theory in this study (Biggs &
Knauss, 2012). Those areas with more migrant nationalities considered as economic
migrants, and therefore as potential competitors for public resources, harbor greater
support for VOX (Halikiopoulou & Vlandas, 2020; Kaufmann & Goodwin, 2018).
However, beyond this trend, we also found an indirect relationship between the
level of territorial concentration of economic migrants and support for VOX, which
would seem to favor the contact theory (Pettigrew & Tropp, 2006). Thus, territorial
concentration would be a good alternative way of measuring the role of the presence
of a migrant population to assess the inter-ethnic contact, and it highlights the role
of areas with a higher concentration of migrants despite their low numbers at the
local level. This aspect also adds complementary nuances to the traditional use of
out-group size (Charitopoulou & García-Manglano, 2018). Given that the traditional
approach focuses on the percentage or size of the minority group in the host society
(e.g., Hopkins, 2010; Savelkoul etal., 2017; van Wijk etal., 2020), we propose, fol-
lowing recent studies (Iglesias-Pascual etal., 2019), that the degree of spatial con-
centration of the role of migrants in the territory should be measured. This would
provide us with new approaches and instruments to enrich the academic debate over
whether it is contact or conflict that predominates in the electoral reaction to the
presence of a migrant population.
Adding the analysis of the socio-economic results to the role of the presence of
the migrant population helps us shape the socio-territorial mosaic that allows us to
understand the rise of the extreme right in Andalusia. While a significant number
of studies relate the rise of the extreme right to areas or individuals in a vulner-
able economic situation, i.e. unemployed or with a low income or (e.g., Coffe etal.,
2007; de Blok & van der Meer, 2018; Lubbers & Scheepers, 2000), other contextual
studies in Andalusia show a different pattern (Iglesias-Pascual etal., in press). Thus,
the relationship between support for VOX and the areas with the highest average
income levels stood out when this phenomenon appeared in 2018. However, for the
2019 results, the application of the spatial Durbin model has allowed us to qualify
these studies by showing that when taking into account the spatial dimension, at
least at the contextual level, it is not possible to show a strong relationship between
income level and a higher vote for VOX. Therefore, our results relativize the theory
that that support for the extreme right has emerged in Andalusia only from economi-
cally privileged areas within the region. However, if we add this aspect to the pattern
of the geographical spread of electoral support for VOX between 2018 and 2019, we
could consider that the vote cast in the wealthiest areas of Andalusia established a
model that was reproduced a year later in adjacent areas with a lower level of wealth.
Thus, this first appearance of the extreme right in Andalusia became a generalized
expression of the social uncertainties of the more economically favored areas, which
were then socially "imitated" in the rest of the territory.
If we combine the analysis of the VOX results in Andalusia in 2018 and 2019,
we can conclude that our results differ from the traditional views of other studies
as regards the role of the presence of migrants and the socio-economic level (e.g.
Coffe et al., 2007; Green et al., 2016; Rydgren & Ruth, 2013; Savelkoul et al.,
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2017). Indeed, the fact that the greatest electoral support for VOX does not corre-
late with the areas of highest territorial concentration of economic migrants suggests
that the rise of the extreme right in Andalusia is related to new forms of social sup-
port which the extreme right-wing parties are currently developing. The emphasis
has shifted from voters’ concerns about the role of migration towards the cultural
threat, disaffection with traditional politics, a low opinion of the national govern-
ment (Golder, 2016; Halikiopoulou & Vlandas, 2020), the limited possibilities of
influencing the system through the majority parties (Akkerman etal., 2014; Alba &
Foner, 2017; Stockemer etal., 2020), and other elements of Spanish internal politics
(Ortiz Barquero, 2019; Simón, 2020). This is backed up by recent studies analyzing
the latest changes in the dynamics behind the support for the far-right (Mudde &
Rovira Kaltwasser, 2018; Stockemer etal., 2018, 2020).
5.2 Limitations andPerspectives forFuture Research
Traditionally, studies such as this, based on the relationship between contextual ele-
ments and electoral results, have been criticized on two counts. Firstly, they have tra-
ditionally been viewed as suffering from the so-called ecological fallacy (Robinson,
1950), which argues that the characteristics of the administrative or spatial unit of
analysis used can be extended to all the members of that administrative unit (de Blok
& van der Meer, 2018; Savelkoul etal., 2017). However, in the case of Andalusia,
this contextual analysis has been constructed from data obtained from the smallest
statistical unit used in Spain, the census tract, with an average population of between
800 and 1000 people. This has allowed us to work with smaller territorial units than
those recently used in other studies of electoral geography (e.g., de Blok & van der
Meer, 2018; Kaufmann & Goodwin, 2018; Tolsma & Van der Meer, 2017). In turn,
collating the electoral and socio-demographic data from the 5,944 census tracts in
Andalusia has allowed us to make a significant number of observations which have
made our statistical analyses more robust (Rydgren & Ruth, 2013) and have enabled
us to use, as our units of analysis, areas of great socio-economic uniformity and
sense of collective belonging (van Wijk etal., 2020). This homogeneity in the units
of analysis, while not completely dismantling the ecological fallacy, does reduce its
effect (Arzheimer, 2012).
Secondly, it is often pointed out that unless the results are broken down into the
national and foreign voting population, it is impossible to conduct a rigorous anal-
ysis of the electoral results based on the socio-territorial context (van Wijk etal.,
2020). However, the Spanish electoral system bars the foreign population from vot-
ing either in regional or national elections. In addition, the percentage of the Anda-
lusian population of foreign origin able to vote is just 2%, and so, in general, the
voting figures clearly reflect the electoral intention of the native population in each
census tract. Moreover, we must take into account that studies focusing on individ-
ual attitudes based on surveys or focus groups referring to attitudes towards ethnic
minority populations may suffer from a bias of social desirability in the responses
(Arzheimer, 2012; Iglesias-Pascual, 2019; Krysan, 1998). Thus, this type of
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approach based on individual behavior obtained through statistical samples could
produce lower rates of right-wing voting intention than the real figure.
Last but not least, the contrasting differences and nuances between the results of
the OLS model and the spatial Durbin model highlight the importance at all times
of analyzing spatial behavior patterns by applying spatial statistical tools. Further
research is therefore required to delve deeper into the patterns of geographical dif-
fusion of support for extreme right-wing parties through the use of spatial statistical
instruments. This could be a key factor in designing social policies to prevent the
growth and diffusion of the ideas of extreme right-wing parties.
Acknowledgements The authors would like to thank the peer reviewers for their suggestions for the
improvement of the article.
Funding Open Access funding provided thanks to the Universidad Pablo de Olavide/CBUA and CRUE-
CSIC agreement with Springer Nature. This work was supported by grants from the Ministry of Econ-
omy, Industry and Competitiveness of the Spanish Government (RTI2018-095325-B-I00).
Declarations
Conflict of interest The authors declare that they have no conflict of interest. The opinions expressed in the
paper are the ones of the Authors and does not necessary reflect the ones of their Institutions.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
ses/ by/4. 0/.
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Up until the end of 2018, extreme right-wing parties and their anti-immigration discourse, now a common occurrence in other European countries, had not appeared on the Spanish political scene. However, in December of that year, the Spanish extreme right-wing party VOX reversed this trend and made significant electoral gains in the Andalusian regional elections. This phenomenon has led us to analyse, in this study, the role played by contextual factors (i.e., out-group size, territorial concentration of the immigrant population, demographic change in settlement locations, aggregated educational level and unemployment rate among the receiving society) in the rise in the number of VOX voters. To achieve this, VOX's results from all the polling stations in Andalusia contained in its 5946 census tracts were analysed. At the same time, each census tract was associated with its demographic, economic and educational data, and their relationship was analysed using a multilevel analysis with Mplus. The results show that the presence of economic immigrants is indeed associated with a higher percentage of VOX voters in Andalusia. However, despite this general trend, those census tracts with a high territorial concentration of Maghrebi immigrants are associated with a lower percentage of support for this extreme right-wing party. The opposite is found for Romanian immigrants. Moreover, areas with larger percentages of people with a high level of education influenced greater support for VOX. Thus our findings suggest the traditional explanations for the increased support for extreme right-wing parties in northern Europe do not provide clear patterns that can be extrapolated to Andalusia.
Article
The rise of the radical right in Slovakia is associated with stronger attitudes against the European integration and globalization. In this paper, the authors examine the role of the regional factors associated with EU membership in the voter support of the traditional and the new radical right political parties in Slovakia. The main finding is that while the support for the traditional radical right is mostly based on cultural and nationalistic factors, the support for the new radical right is associated with the regional economic factors such as the unemployment rate and wages. The electoral results of the radical right are more influenced by the unemployment rate just after joining the EU than by the situation during elections. Results also show that in the analysis of the impact of investments from the ESIF on voter support of the radical right, it is useful to distinguish between investments from the ERDF and CF and from the ESF.
Article
Some regions in Europe that have been heavily supported by the European Union’s Cohesion Policy have recently opted for parties with a strong Eurosceptic orientation. The results at the ballot box have been put forward as evidence that Cohesion Policy is ineffective for tackling the rising, European-wide wave of discontent. However, the evidence to support this view is scarce and often contradictory. This paper analyses the link between Cohesion Policy and the vote for Eurosceptic parties. It uses the share of votes cast for Eurosceptic parties in more than 63,000 electoral districts in national legislative elections in the EU-28 to assess whether Cohesion Policy investment since the year 2000 has made a difference for the electoral support for parties opposed to European integration. The results indicate that Cohesion Policy investment is linked to a lower anti-EU vote. This result is robust to employing different econometric approaches, to considering the variety of European development funds, to different periods of investment, to different policy domains, to shifts in the unit of analysis and to different levels of opposition by parties to the European project.