Local determinants of the location choices of immigrants in
Spain
Autores y e-mail de la persona de contacto:
Vinuela, Ana (avinuela@uniovi.es)
Gutierrez Posada, Diana
Rubiera Morollon, Fernando
Departamento: REGIOlab, Economia Aplicada
Universidad: Oviedo
Área Temática: Poblacion y movimientos migratorios
Resumen: In the 2000s and with natural population growth rates close to zero, Spain
experienced an inflow of almost 5 million immigrants. These new Spanish residents did
not tend to locate evenly across the territory and contributed to put pressure on the
already strong spatial population imbalances. Covering the whole Spanish territory and
working with municipal data for 2001-2011, the objective of this paper is to analyze
what are the local characteristics explaining the attractiveness of a place for
immigrants and if the effect exerted by those determinants vary accross space. Applying
a Geographically Weighted Regression (GWR) estimation procedure, which accounts
for spatial dependence and spatial non stationarity, results show the relative
importance of the local economic structure or the effect of previous foreigners residing
in the area.
Palabras Clave: location choice, international migration, local attractiveness,
geographically weighted regression
Clasificación JEL: R23, J11 and C19
1
1.
Introduction
Data from the Continous Register Statistics of Inhabitants (Padron) released by the
National Statistics Institute (INE) every year, show the extraordinarily high population
growth that Spain has experienced since year 2000. With national population growing at
a very slow pace (Table 1), the overall population increase was mainly due to the
massive inflows of immigrants, which departure in the aftermath of the economic crisis
–specifically from 2011 onwards- explains now the overall decrease of population1.
Table 1: Population and population growth: 2000-2014
Population
National
Foreign-born
Annual growth (%)
Total
National
Foreign-born
Total
2000
39,575,911
923,879
40,499,790
0.31
23.36
0.74
2001
39,746,185
1,370,657
41,116,842
0.43
48.36
1.52
2002
39,859,948
1,977,946
41,837,894
0.29
44.31
1.75
2003
40,052,896
2,664,168
42,717,064
0.48
34.69
2.10
2004
40,163,358
3,034,326
43,197,684
0.28
13.89
1.13
2005
40,377,920
3,730,610
44,108,530
0.53
22.95
2.11
2006
40,564,798
4,144,166
44,708,964
0.46
11.09
1.36
2007
40,681,183
4,519,554
45,200,737
0.29
9.06
1.10
2008
40,889,060
5,268,762
46,157,822
0.51
16.58
2.12
2009
41,097,136
5,648,671
46,745,807
0.51
7.21
1.27
2010
41,273,297
5,747,734
47,021,031
0.43
1.75
0.59
2011
41,439,006
5,751,487
47,190,493
0.40
0.07
0.36
2012
41,529,063
5,736,258
47,265,321
0.22
-0.26
0.16
2013
41,583,545
5,546,238
47,129,783
0.13
-3.31
-0.29
2014
41,747,854
5,023,487
46,771,341
0.40
-9.43
-0.76
Source: 2000-2014 Continous Register Statistics of Inhabitants, Padron (INE)
Not only foreign-born population multiplied by four in just one decade and but also
contributed to reinforce the Spanish territorial imbalances. For the period 2000-2006, 55
per cent of the foreign-born population concentrated in just three autonomous
communities or NUTS 3 regions: Madrid, Cataluña and Comunidad Valenciana
(Conde-Ruiz et al, 2008), which are also the regions concentrating most of the Spanishborn population. In the year 2011, when more than 12% of the Spanish residents had a
foreign nationality and the absolute number of immigrants reached its peak, the spatial
concentration remained the same. This pattern of regional geographical concentration
1
The terms immigrant and foreign-born will be used as synonyms in this paper.
2
for the immigrant population is common in other developed countries. For instance, in
USA in 1990, 63 per cent of the foreign-born population were clustered in the four most
populous states –California, New York, Florida and Texas– where 31 per cent of the
overall population lived (Zavodny, 1999).
However, it is not accidental that those three Spanish regions also contain the three
bigger metropolitan areas in Spain, namely Madrid, Barcelona and Valencia. Thus, it
could be argued that it is not the region itself that is the attracting force, but the city and
its surrounding areas, as they might be offering new job opportunities, mostly
manufacturing and service sector jobs, and prompting the arrival of new workers (Bover
and Velilla 1999). For instance, in 2010 more than 95 per cent of foreign-born
population were living in metropolitan areas in the US (Wilson and Singer, 2011).
Evenmore, the ability of cities to attract international migrants is increasingly seen as an
important indicator of their growth potential (Glaeser and Resseger, 2010; Glaeser and
Saiz, 2003; Moretti, 2012).
In Spain, if representing foreign-born population growth figures from 2001 to 2011 at
local level –Maps 1a and 1b–, we observe that certain cities and-or localities (as
opposed to regions) seem to be very attractive to the immigrant population. Immigrants
have mainly settled along this decade in small and medium size cities as well as in the
localities areas around big cities -not within the metropolitan areas-, which are the areas
that tend to be more specialized in blue collar jobs and manufacturing sectors. This
process of suburbanization of jobs and immigrants has been analyzed broadly in the
USA for various metropolitan areas (Singer, 2008; Katz et al. 2010, Wright et al, 2010),
but in the Spanish case, evidence has only been provided for the main metropolitan
areas (Garcia-Lopez, 2012; Peeters and Chasco, 2014)
Map 1: Location of immigrant population: Foreign-born population growth 20012011
Legend
0 to 4%
More than 4%
3
observed between natives and foreign born population, we realize that the
characteristics of the recipient areas might be exerting different effects over these two
groups. For instance, while the Mediterranean coast ant the bigger cities have been
strong poles of attraction for the nationals, the bigger immigration population increases
were not experienced in neither of those areas.
Given the observed spatial growth pattern, the objective of this paper is to analyze what
are the local characteristics explaining the attraction of immigrant population differ to
certain areas in Spain and to check if those determinants affect the settlement decisions
in different ways across space. Working with data at Local Labour Market level (LLM),
we want to identify which local characteristics have determined the intensity of
population growth from 2001 to 2011 considering: (i) the co-existence of
centripetal/centrifugal factors or local characteristics encouraging/deterring population
(and economic) growth in/out of one locality, making very difficult to predict what the
final outcome for an area will be, and (ii) the existence of spatial heterogeneity in labor
opportunities, economic structure, existing amenities or high-low skilled labor among
other factors.
This paper contributes to the literature on immigrants location decisions in a number of
ways. To our knowledge, this paper contains the first study to examine immigrant
location decisions at local level covering the whole Spanish territory, not only one or
few the metropolitan areas. Second, spatial dependence and spatial non-stationarity is
accomplished applying a Geographically Weighted Regressions (GWR), a novel
approach that allows for interactions or spillover effects between localities and
variations in the effect of the characteristics of the area across space.
The paper is structured as follows. In the next Section we review the literature on
immigration location determinants. Section 3 sets the empirical setting, explaining first
the election of the Local Labor Markets as the local spatial unit, the local variables and
databases. Section 4 presents the model and the estimation strategy, the Geographically
Weighted Regressions or GWR, a methodology that takes into account the spatial
dependence and captures spatial variations in the regression coefficients. Results on the
relative importance of local characteristics of the over the foreign-born population
growth (or immigration location decisions) are described in Section 5. A comparison
with the equivalent Ordinary Least Squares estimates is offered in order to enhance the
benefits of the GWR methodology. Greater emphasis is placed on those cases where
conclusions over the effect of a local factor vary depending on the methodology
implemented. This study ends with a section summarizing the main results and
conclusions, and outlining some economic policy implications derived from the
analysis.
2.
Area determinants of immigrants location
4
Migration theory predicts that immigrants are attracted to regions or areas with
favorable income prospects. Immigrants usually leave their country of birth in search of
a new place to live where they have better work opportunities, higher living standards
and/or greater political freedom. However, migrating to other country is not exempt
from costs, economic, social and psychological. When immigrants decide to move to
another country, they have to incur big travel and relocation expenses.
Assuming that migrating decisions follow an economic rationale, in their adopting
country immigrants will settle in those areas where they can maximize the opportunities
and minimize the psychological and economic cost of migrating. There are basically
two approaches to investigate immigrant’s location preferences: one is focusing on the
pull factors, i.e., the set of negative or positive social or economic factors in the
potential areas of destination which pulls migrants towards them; the other is exploring
the push factors, i.e. the set of negative or positive social or economic factors in the area
of origin which pushes immigrants away (Lee, 1996).
Evidence is far from conclusive. Studies for the USA find contrasting evidence on
whether immigrants are sensitive to local differences (Bauer et al 2002, 2005). Once
they arrive into the country their first choice of location tends to be the large cities
(Aslund, 2005), where there is a large pool of jobs and where earlier cohorts of conationals and other emigrants have settled (Bauer et al, 2001, 2005). Some research
conducted at US state level suggest that immigrant composition is a stronger predictor
of the location choices of immigrants than are labour market conditions (Zavodny,
1999). However, when immigrant concentration reaches some level, negative
externalities may arise in the so-called “traditional” migration destinations as the
competition among similarly low skilled jobs increase, putting therefore also pressure
on wages (Bauer et al., 2007, 2009; Light and Johnston, 2009).
Initially attracted to regions with large overall immigrant populations and high
concentration of co-nationals, newly arrived immigrants acquire knowledge of regional
labor opportunities, regional living standards or welfare policies from the previous
immigrants in the area. However, information on labor opportunities or welfare benefits
usually improves over the years (Aslund, 2005) and immigrants are much more likely to
locate in areas with growing demands for their skills and areas with increasing real
wages (Jaeger, 2000).
In Europe, the recent study conducted at NUTS I and NUTS II regional level by
Rodriguez-Pose and Ketterer (2012) examine the factors that determine the
attractiveness of European regions, including economic, socio-demographic and
amenity related territorial features. Similarly to results for the USA, economic factors,
human capital-related and demographic aspects, as well as the existence of networks
and different types of regional amenities, exert an important influence on the relative
attractiveness of European regions.
5
However, NUTS 1 or 2 level regions are very broad and internally heterogeneous.
NUTs regions contain urban and rural areas, amenity scarce and abundant areas,
localities well connected to the economic corridors and localities totally peripheral etc).
Thus, the relevant question is how important are the local economic and noneconomic
attributes for immigrants location decisions. Descending at a higher level of desaggregation than the NUTs regions, each locality poses a wide set of local
characteristics likely to impact on the immigrants’ location choices that go beyond
factors such as the existing immigrant concentration, networks or the labor
opportunities available in the area.
3.
Empirical setting
3.1. The spatial unit: the Local Labor Markets (LLM)
Some studies regarding the concept of a region2 suggest that the administrative
divisions (either NUTS 2 or 3 regions) might not be an appropriate spatial unit to
identify persisting patterns of demographic, social and political behaviour as they fail to
define economically and socially integrated areas that share particular attributes to one
degree or another. Some authors are aware of the importance of working with highly
desaggregated data in order to capture for instance the immigrants’ segregation or
suburbanization phenomena within the metropolitan areas (Timberlake et al., 2011; Liu
and Painter, 2012). While policies addressed to such an important issue should be
designed and executed at local level, the first obstacle to overcome is the lack of official
data available beyond regional, which difficulties any comprehensive national analysis.
In Spain there is basically a unique source of information –the Population and Housing
Census database- that collects and provides data for several socio economic indicators at
municipal level (NUTS 5). For instance, the Census is the only trustworthy non
administrative source of information on the age, gender, educational level, educational
attainment, labor situation, etc. at municipal level. However, if taking the municipality
as the basic spatial analysis unit, we fail to recognize the important role that labor plays
in peoples’ lives, guiding their territorial behavior with regard to the municipality of
residence chosen. Changes are that one immigrant will choose as municipality of
residence the municipality of work or, in case that is not possible, another municipality
at day-to-day travel (commuting) distance.
One way to avoid the discrepancies between the characteristics of the area where
immigrants locate and where immigrants work is choosing as the basic spatial unit the
functional regions called local labor market (LLM). A LLM comprises several
municipalities and describe a space where the population develops most of its economic
and social relationships. For instance, in Spain the metropolitan areas of Madrid and
Barcelona include 151 and 51 municipalities respectively. Thus, a LLM is a place where
2
For a comprehensive review on the concept of Region in Social Sciences, see Agnew 2013.
6
the common interest of the local population can be identified as a whole, and can be the
appropriate level for implementing policies at local or regional level (Parr, 2005),
something theoretically simple but which for political reasons usually faces strong
resistance from the municipal entities involved
As the construction of the LLMs guarantees that more than 75% of the residents living
in the area also work in the area and vice versa, the LLMs have the advantage of
ensuring that their new residents are choosing that location both for labor-economic
related determinants and amenity-proximity reasons. The LLMs are therefore selfcontained spatial units that have a high internal homogeneity in terms of labor and
income (Rubiera and Viñuela, 2012), an additional quality in order to identify what
local characteristics are prompting population settlements.
Using commuting data from the 2001 Census, Boix and Galleto (2006) functionally
divide the Spanish territory into 803 LLMs. In line to many studies dealing with the
concept of city-region (Parr 2005; Newman and Hull, 2009), each LLMs typically
includes an urban core(s) and several municipalities belonging to its hinterland and
linked to it through the functional connectivity provided by the commuting flows.
3.2. Data and variables
Extensive data at municipal level (NUTS 5) are needed and then aggregated into 803
LLMs. Data availability at such level of des-aggregation is reduced to few reliable
official sources.
The dependent variable in the model to be estimated is the ith LLM’s foreign-born
population growth (Yf) from 2001 to 2011. The Population and Housing Census and the
Continous Register Statistics of Inhabitants -released every 10 years and on annual
basis by the National Statistics Institute respectively-, both provide data on the foreignborn population residing in the Spanish municipalities. Given the administrative nature
of the second source, to calculate immigrant population growth figures the 2001 and
2011 information is drown from the Census.
The heterogeneous nature of population behavior both across space and along time has
been highlighted by Glaeser et al. (2014), underlying that this behaviour in turn makes
it necessary to consider many different variables which may in fact play different roles
depending on the place, the moment, the context, and so on. The work from Chi and
Ventura (2011) also emphasizes the need to consider a framework with a wide set of
socioeconomic and geographical factors.
The set of explanatory variables at local level can be divided in three groups (Coffey
and Polèse (1988), Cebula (2002), Partridge and Rickman (2003), Polèse and Shearmur
(2004), Shearmur and Polèse (2005), Chi and Ventura (2011) and Glaeser et al. (2014)
among others): (i) urban economics variables, (ii) socio-economic state variables and
(iii) other geographical variables.
7
Within the urban economics variables the first one to consider is size (LnPop01), from
the 2001 Spanish Census, and distance to size (DistMA), from the Spanish National
Centre for Geographical Research (IGN). These two fundamental variables in Regional
Economics pose information on role played by agglomeration (des)economies and the
relative position of an area. Size is proxied by the initial overall population size at the
beginning of the period under study and the relative location is measured by means of a
linear distance to the nearest metropolitan area (being a metropolitan area those LLMs
with more than 500,000 inhabitants). While the variable size provides us information on
the power of the different ranking of urban areas in attracting (or repelling) new
residents, either national or foreign-born population, the relative location of an area
quantifies its degree of accessibility to goods, services, amenities and labor markets
(Coffey and Polèse, 1988; Polèse and Shearmur, 2004).
The effect of size and location should be analyzed hand–in-hand as they can exert
ambiguous effects over foreign-born population growth. As we can observe in Map 1,
although in this decade in every LLM there has been a positive inflow of immigrants,
the higher increases were not experienced in the big metropolitan areas (Madrid,
Barcelona, Zaragoza, Valencia etc.) neither in the Mediterranean corridor, the most
dynamic area in terms of economic and national population growth. The LLMs
corresponding to small cities and rural LLMs located in peripheral areas experienced a
decrease (outflow) in national population figures and therefore might be might be the
ones in need or low-skilled tertiary, manufacturing or agricultural labor (land of job
opportunities for the immigrants). Moreover, size and distance could be exerting a
positive effect on immigrations settlement decisions in some areas but the opposite in
others. For instance, as discussed above, one demographic variable that seems to exert a
big influence on immigrant’s location choice is the presence of previous immigrants in
the area, but the stock of established immigrants can act an attraction for newly arrived
immigrants, but as a repellent for those already settled down or highly skilled
immigrants.
Thus, the first variable to consider in the group of socio-economic state variables is the
percentage of immigrants living in the LLV in 2001 (Foreign01). Data on this variable
are constructed using the 2001 Spanish Census. As pointed above, this is one of the
most claimed factors determining location choices for immigrants as information about
the labor market and many other relevant topics such as housing, schooling, welfare
benefit programs etc. is usually accessed through informal channels and networks. New
immigrants will also find cultural amenities and linguistic affinities in the area.
The rest of variables in the socio-economic state variables group are all related to the
labor opportunities and the economic structure in the area. The 2001 average wage in
the LLM (Wage01), which in principle is likely to affect the immigrants and also the
nationals location choices, are drawn from the Fiscal Micro-Data from the Institute for
Fiscal Studies (IEF). This data source covers the entire Spanish territory except for
those Autonomous Communities with a statutory scheme, ie Navarra and Basque
8
country, for which no data is available. After filtering the individual data –with
information on income, tax liabilities, etc-, we can generate the average wage of the
representative individuals residing in 2001 in the LLM.
To describe the labor market conditions and the level of economic dynamism of the
LLM, from the 2001 Census we can construct the employment rate of the LLM,
defined as the proportion of the LLM’s working-age population that is employed
(Emp01). But not only the relative lack or abundance of jobs in the area is relevant but
also the sectoral composition of that labor market, so the LLM’s location quotients for
the agricultural sector (LQag01) and for the manufacturing sector (LQind01) are
constructed from detailed employment data drawn from the 2001 Spanish Census. In
the case of the foreign-born population, this variable might show interesting results as
many studies show that immigrants traditionally cluster in certain industries or
economic niches with low status and low pay rather than dispersing across all
industries available (Wang and Pandit 2007, Wright and Ellis 2010). To count for the
degree of sectoral specialization or diversification of the LLM, we also include the
specialization index of the LLM (S01)
Finally, as geographical variables we consider several factors that are to some extent
indicators of the quality of life and the existing natural amenities, and that may play a
role in retaining or attracting population. As capital cities tend to have their own
demographic dynamics, a dummy variable (Cap) is considered to control for those
LLMs that correspond to the politico-administrative capital of a region. This variable
underlines the importance of having been appointed as the administrative center,
thereby concentrating a large part of the public sector jobs and offering a larger variety
of public services. The second variable from this group is the distance from the LLM to
the nearest coast (DistCoast), measured as the linear distance in kilometers, and can be
obtained from the Spanish National Centre for Geographical Research (IGN). The
average annual rainfall (Rain) and the average of the maximum (Tmaxjuly) and minimum
(Tminjan) average temperatures in July and January respectively can be derived from the
historical series (1987-2007) published by the Meteorological State Agency (AEMET).
9
4.
Econometric strategy: Geographically Weighted Regressions vs OLS
Our aim is to identify the relative importance of the urban, socio-economic and amenity
related characteristics of the LLM in attracting3 immigrant population.
We propose to explain foreign-born population growth at local level through a set of
possible determinants or explanatory variables (X). Our most simple empirical model
would be:
[1]
where i are the n spatial units, the Spanish LLMs.
However, it is important to acknowledge that the estimated effects of a variable can
vary greatly across countries, and even within the same country depending on the
temporal or the spatial framework chosen (Shearmur and Polèse, 2005; Shearmur et al.,
2007; Glaeser et al., 2014). Under the presence of spatial heterogeneity, the question is
whether a single estimate can properly explain the settlement choice of the foreign
population. Evenmore, the responses of the immigrant population to particular variables
can change across space, being these differences caused by the interrelationships
between neighboring regions; when there is spatial non-stationarity, adopting a global
regression approach such as Ordinary Least Squares might lead to deceptive estimates.
The conclusions regarding, for instance, the area determinants of foreign-born
population growth can mask significant local variation as a standard overall estimate
may suggest for instance a positive effect of one factor, while in fact such factor could
be stimulating settlements in some areas but negatively affecting location decisions in
others, showing an average effect which is not representative at the local level due to its
high regional variability. This compensation effect is especially dangerous when the
average impact is close to zero, as it might be deemed as non-significant and
disregarded as an element of the analysis or as a potential policy instrument.
The simplest approach proposed in the literature to address spatial non-stationarity is
the fixed-effects model, where dummy variables are introduced to capture site-specific
characteristics (Brunsdon et al., 1998; Greene, 2000). To correct for spatial dependence,
Anselin (1988) suggested a spatial error model (SEM) and a spatial lag model (SLM).
Both models take into account the problems mentioned above, but parametric
3
Given the different immigration growth dynamics experienced between 2001 to present time, this study
will focus exclusively on the 2001-2011 period as we are interested in local factors behind the attraction
of new residents (either nationals or foreigners)).
10
heterogeneity is not accomplished, so an important source of regional information is
lost.
The Geographically Weighted Regression (GWR hereinafter) is a non-parametric model
that represents an alternative to deal with both issues (Brunsdon et al., 1996 and 1998).
The GWR approach can be easily implemented, hypothesis testing is akin to that of
standard methods and results can reveal interesting spatial regularities undetected by
more traditional methods (McMillen and Redfearn, 2010). This methodology captures
spatial variations in the regression coefficients by introducing a weighting matrix in the
estimation process and estimating a locally varying sample for each location, generating
a separate group of regression parameters which reflects the sample heterogeneity by
estimating different responses to an explanatory variable across space.
The GWR model, where a regression for each observation is estimated, is specified as:
[2]
giving as a result a separate set of parameters for each of the n observations, calculated
through the following equation:
[3]
For each observation –for each LLM– a separate regression is estimated in which the
sample is composed of spatial units within a certain distance or bandwidth. There are
different criteria to specify the distance, such as the minimization of the Akaike
Information Criterion (AIC) (information loss indicator) or the minimization of the sum
of squared errors (or the cross-validation score, CVS).
The weights on the GWR depend on the linear distance between observations and
represent the adjacency effects for neighbouring locations within the specified
bandwidth (Cleveland and Devlin (1988); McMillen (1996), and Brunsdon et al. (1996
and 1998).
is a diagonal weighting matrix that selects the observations that intervene
in the estimation of the local coefficients,
in point :
[4]
Following the assumption that more proximate locations are more alike, the weights
should decay with distance. Many weighting notations could be used (dichotomous, bi-
11
square or tri-square decay function, etc.). In this work we have chosen a Gaussian
kernel weighting function specified as:
[5]
where
is the distance between observations i and j, and h is the general distance
bandwidth adopted. Thus, the weight quickly declines with distance from the
geographic observation concerned.
The GWR approach has several advantages over standard methods, but also has flaws.
One advantage is that since each area has its own constant term, it accounts for localfixed effects (Partridge et al., 2008). This approach can reduce spatial error correlation
when there is heterogeneity in the GWR coefficients (Fotheringham et al., 2002). One
shortcoming is multicollinearity, which can be problematic in individual local
regressions, but as the GWR approach produces a considerable amount of regressions,
considering a large range of estimates allows us to "average" them, thereby better
determining their central tendencies and distribution (Ali et al., 2007; Partridge et al.,
2008).
GWR also has some inherent limitations. The fact that each local model does not take
into account local spatial dependence may bias local estimates (Shearmur et al., 2007).
Some other drawbacks are linked to the local regressions when using a smaller sample
size, as the resulting coefficients may be less efficiently estimated than those from
global approaches. Apart from that, GWR is computationally intensive and the output
can be overwhelming (Ali et al., 2007; Partridge et al., 2008). Other flaws are the
robustness of the results, which depends on bandwidth selection, and the existence of
possible sample overlaps (Ali et al., 2007). Even more, significant local regression
coefficients do not necessarily indicate correlation with certain spatial unit, but that
correlation can be observed across the bandwidth specified in the GWR process
(Shearmur et al., 2007). Probably the strongest criticism for this methodology is the one
made by Wheeler and Tiefelsdorf (2005), who found evidence supporting the existence
of “false positives” regarding the ability of GWR to distinguish between spatially
stationary processes and varying ones, therefore pointing out the unreliability of the
estimations. In the light of these statements, Páez et al. (2011) conducted several
simulations and concluded that, even when deeper examination should be done and
12
caution is recommended, the severity of the mentioned problems decrease with the size
of the sample4.
5.
Local determinants of the immigrants location choices: results and discussion
Using data for the 803 Spanish LLMs, estimations from the OLS approach will first be
presented and then compared with the ones obtained with the GWR model as an attempt
to understand the relevance of the local characteristics behind foreign-born population
growth or immigrants’ settlement and its spatial patterns.
Estimations for the 2001-2011 decade are shown in Table 2. While the OLS estimation
results –first column-, provides fourteen global parameters, plus one intercept, with the
GWR procedure we get 15 x 803 parameters, one for each LLM and factor –plus de
intercept–. The following five columns show the quartile intervals for the GWR
estimations for each population growth factor considered. We will especially focus on
discussing those factors where the GWR estimates are significant and add useful
information on immigrants location decisions. After that, we will represent those factors
showing a spatially differentiated effect in Spain and suggest some spatial or regional
policy implications5.
4 In this paper, the number of observations in our database (803 LLMs) can be considered large
enoughtoproducereasonableresultsfromaneconometricperspective.
5Onlysomefactors’estimatesarerepresentedinthispaper.Pleasecontacttheauthorstorequest
foradditionalinformation.
13
Table2.Immigrantpopulationgrowth:results2001‐2011
Variable
Global(OLS)
Intercept
2.359 ***
Wage01(Meanofnetlaborincome2001) 0.00004 LnPop01(Logofpop.size2001)
‐0.037 *
Foreign01(Foreignpopulationrate2001) ‐6.184 ***
DistMA(DistancetonearestMA)
‐0.048 **
Ed01(Educationallevel2001)
0.297 Emp01(Employmentrate2001)
‐0.670 **
LQag01(Locationquotientagr.2001)
0.0004 **
LQind01(Locationquotientind.2001)
‐0.0014 ***
S01(Specializationindex2001)
‐0.003 DistCoast(Distancetocoast)
‐0.057 **
Rain(Avg.annualrainfall1987to2007)
0.023 **
Tminjan(Min.temperatureJanuary)
0.012 Tmaxjul(Max.temperatureJuly)
0.013 Cap(Capitalcitydummy)
0.028 AdjustedR2OLS
0.3347
AdjustedR2GWR
0.5382
0.8143
2.2214
F1test(b)
F2test(c)
Min.
1stQu.
Median
3rdQu.
1.750
‐0.00006
‐0.182
‐22.800
‐0.380
‐4.706
‐2.026
‐0.0009
‐0.0027
‐0.161
‐0.158
‐0.075
‐0.040
‐0.090
‐0.222
2.745
0.000002
‐0.067
‐11.700
‐0.177
‐0.851
‐1.001
‐0.0002
‐0.0019
‐0.103
‐0.060
‐0.031
‐0.005
‐0.019
‐0.069
3.270
0.000011
‐0.040
‐7.487
‐0.095
0.185
‐0.340
0.0003
‐0.0014
‐0.071
‐0.014
‐0.007
0.013
‐0.006
0.035
4.053
0.000020
‐0.018
‐5.585
‐0.045
0.700
0.005
0.0011
‐0.0010
‐0.002
0.014
0.009
0.032
0.004
0.104
F‐statistic
+++
+++
28.96 ***
Max. F3test(a)
5.374
0.000039
0.010
‐3.578
0.182
4.244
0.806
0.0018
0.0011
0.046
0.370
0.062
0.062
0.043
0.246
+
+++
+++
+++
+++
+++
+++
+++
+++
‐*/**/***and+/++/+++representglobalsignificanceorsignificantvariationrespectivelyat10%/5%/1%level.
‐(a),(b)and(c):statisticaltestsproposedbyLeung,MeiandZhang(2000).F1andF2areintendedtocomparethegoodnessoffitbetweenOLS
andGWRmodels,whileF3verifiesthesignificanceofthevariationinthesetofcoefficientsobtainedthroughGWRforeachfactor.
14
Contrary to the usual result in the literature, the initial size of the LLM (proxied by its
overall population) exerts a negative and significant effect in attracting immigrants
according to the OLS estimations. However, this result could be explained by the
timeframe analysed, as along one decade immigrants have had enough time to move
from their initial settlement and relocate to a more suitable LLM in terms of housing
availability, living standards or labour opportunities, factors which are indeed not be
positively associated with larger cities. Although apparently not attracted by the size of
the city, immigrants do not want to locate very far from a metropolitan area, as it
indicates the negative and significant result obtained for the variable DistMA in the OLS
estimation.
The literature on inmigrants location suggests that the existence or previous foreigner in
the area is a stronger predictor of the location choices than labour market conditions.
According to the OLS results, Spain seems to be different as the stock of established
inmigrants (Foreign01) has exterted a significant and negative effect over foreign-born
population growth. This unexpected result however could be further explored if
information on the nationality and educational level of the immigrant population was
available for Spain. As Bartel (1989) and McDonald (2004) point out, high-skilled
immigrants are less likely to settle in areas with a high presence of foreign-born
population and Western immigrants are more likely to integrate with the nationals in
areas with low presence of immigrants since the cultural distance between both groups
is not as large (Zorlu and Mulder, 2008).
The other possible explanation pivots around the strong labour motive 6that triggered in
the first instance the arrival of massive inflows of foreign-born population into the
Spanish borders during the 2000s. The location decision of labour migrants is likely to
be determined by the availability of jobs rather than ties with family and/or their
community (Zorlu and Mulder, 2008). However, once again results from the OLS
estimations disturb our initial thinking since the parameter for the rate of employment
(Emp01) shows a significant and negative sign. Was the extraordinary inmigration
phenomena observed in Spain during the 2000s due to pleasure so?
Far from it. Spatial heterogeneity and non-stationarity might be a more plausible
explanation for the unexpected OLS results. But first, we must compare de goodness of
fit between OLS and GWR estimations and then wonder about the significance of the
6
Broadly, there are three types of migration: family reunion migration, asylum seekers migration
(refugees) and labour migration.
15
variation of the 15 x 803 parameters, one for each LLM and factor (plus de intercept),
obtained using GWR. The F1 and F2 statistic tests proposed by Leung et al. (2000)
suggest that the GWR results outperform the OLS approach. The F1 statistic is defined
as the ratio between the squared sum of residuals (SSR) of OLS and GWR, so a value
significantly smaller than one (0.81) supports a better fit for the GWR estimation. The
F2 test is based on the SSR improvement of GWR over OLS, i.e., the difference
between the residuals sums of squares. A large value of this test (2.2) proves once again
that GWR outperforms the OLS approach. In addition to the F1 and F2 statistical tests,
Leung et al. (2000) suggested checking the differences between both approaches with
the F3 statistic, which tests the significance of the variation in the 15 x 803 parameters
estimated using GWR. The last column on Table 2 shows the F3 statistic, which needs
to be interpreted considering also the significance - or lack of it - of the OLS estimates.
Regarding the comparison between global and variability significance, the first scenario
would be having significant OLS estimates and no significant variability in the GWR
parameters: in this case the one OLS parameter for the whole territory could be
considered representative at local level. In other words, the factor under study does not
have a spatially differentiated effect on immigrants location. The second possibility is
having significance of the variation of the coefficients obtained under GWR but no
significance of the global OLS coefficient. This situation might be revealing a
compensation effect in the OLS estimations, i.e. the existing regional variability leads to
an average general effect proximate to zero. The last possibility is when both the OLS
estimator and the F3 statistic are significant. In this scenario, although significant, the
OLS estimates have failed to capture the existence of spatial non-stationarity revealed
by the F3 test. It is in the presence of second and third scenarios, when the use of the
GWR approach becomes necessary to understand the spatially differentiated processes
at work and to propose customized policies and draw economic implications at local
level.
Thus, GWR estimates for the overall initial population (Size) reflect that expected the
attraction exerted by the cities operates for certain areas, while in others the small towns
and rural areas, with better employment opportunities in sectors that have traditionally
attracted workers (Bauere et al 2007) The F3 test for this variable reveals the existence
of spatial non-stationarity, and if representing the GWR estimates on a map (Map 2), we
can observe that both
agglomeration economies and diseconomies processes are
simultaneously taking place within the Spanish borders.
16
Map 2: GWR parameters for the overall initial size
Legend
-0.182 ~ -0.067
-0.067 ~ -0.040
-0.018 ~ 0
0 ~ 0.010
Agglomeration diseconomies processes stand in those urban areas located in the NorthEast (Valle del Ebro Axis), as shown in Map 2. This effect is generally associated with
negative externalities such as congestion, environmental degradation or higher housing
prices in large cities. However, the agglomeration economies are still operating in the
north and north-west of the country, a territorial differentiation that cannot be shown in
the OLS results. In the OLS estimation the positive effect of size (agglomeration
economies) for some LLMS is offset by the agglomeration diseconomies (negative
parameters), and as a result, the parameter for the initial size is negative.
Something similar happens when analysing the importance of the distance to the
nearest metropolitan area (DistMA) on the immigrant’s location choice. Although the
global effect of the relative distance is significant and the expected sign, when we allow
for spatial heterogeneity the GWR estimation shows that in certain LLMs the relative
location could be affecting immigrants location choices in the opposite direction to the
global effect. In other words, for some LLMs, being too close to the large metropolitan
areas could be having a negative influence on foreign-born population growth. This
goes in line with results obtained after the 2004 EU enlargement and the subsequent
large numbers of immigrants arriving into the UK, where they settled in small towns
17
and rural areas, with employment opportunities in sectors commonly hiring migrant
workers (Bauere et al 2007)
The effect of previous foreigners in the area is however always negative regardless of
the LLM under consideration. However, the spatial variation is significant and wide
across the Spanish territory, having a strong negative effect over immigrants location
specially along the Mediterranean coast, Cadiz (south) and Coruna (north west).
Map 3: GWR parameters for the stock of established immigrants
Legend
-21.80 ~ -11.70
-11.70 ~ -7.48
-7.48 ~ -5.58
-5.58 ~ -3.578
As suggested above, most of the immigrants getting into Spain were labour immigrans
so the labour market conditions must have played a very important role in their location
decisions. While wage territorial differentials do not seem to have affected their location
decisions, the local employment rate (Emp01) affects foreign-born population growth in
a different way depending on the location of the LLM under consideration (Map 4). In
most of the LLMs the expected positive relationship between employment and the
arrival of new (foreign) residents can be observed, but around the rural areas of Lerida
(North-East) and Salamanca (Centre-West) it is not the employment opportunities what
is attracting foreign population growth. This can be explained by the (unknow) sectoral
18
composition of employment, as high rates of employment does not necessarily mean the
availability of jobs in those sectors traditionally occupying immigrants.
Map 4: GWR parameters for the employment rates
Legend
-2.026 ~ -1.001
-1.001 ~ -0.340
-0.340 ~ 0.005
0.005 ~ 0.806
In summary, including spatial heterogeneity and non-stationarity in the analysis of the
area determinants for immigrants location decisions allows a better understanding of the
immigrants location patterns observed in the 2000s. Labour opportunities have been the
main drivers of immigrant location choices all around the Spanish geography. Thus,
small and rural LLMs offering employment opportunities in general, and in particular in
the sectors traditionally employing immigrants, have experience an unusual growth in
their foreign-born population beyond what their size and relative location could in
principle suggest. Evenmore, instead of acting as an attracting pole, the stock of
previous foreigners in the area has not triggered immigrant population growth, although
this result should devote more attention distinguishing between ethnical groups.
4. Conclusions
The analysis of population allocation and growth usually searches for long-run universal
patterns. But traditional factors explaining the immigrant population dynamics and
growth can change their effect and significance across space.
The objective of this paper is to analyse what are the local factors attracting immigrants
into one LLM and not other. Following the literature on population growth and
immigrant’s location choices, determinants include socio economic variables - such as
19
the initial population level, the previous existence of foreign-born population or the
specialisation on the agricultural or industrial sectors-; labour related variables –such as
the employment rate at the beginning of the period or the existence of a pool of highly
qualified workforce-; and many geographical and amenity related factors -such as the
distance to the nearest metropolitan area, the extreme temperatures supported in the area
or the distance to the coast-. This exhaustive data at a high level of des-aggregation was
obtained from the Spanish Censuses (2001 and 2011), published by the National
Institute of Statistics, the Spanish National Centre for Geographical Research and from
the Meteorological State Agency.
Adopting a Geographically Weighted Regressions approach, our results for the 803
Local Labor Markets in which the Spanish territory can be divided confirm the
existence of spatial heterogeneity and non-stationarity in this matter. Even factors as
important in Regional Economics as distance and size change their effects depending on
the area under consideration. Being labour market conditions the most important
determinant for immigrants location choices, however in some LLMs high levels of
employment do not seem to be the forces attracting foreign population. A deeper
analysis considering the different groups of immigrants should be considered in future.
In summary, the spatial heterogeneity in immigrants location choices’ determinants
warns against the general perspective presented by global approaches, given that factors
may be significant in opposite ways in different areas, but average to zero or to a certain
figure that might not be representative across the whole territory. Different policies
should be aimed to attract foreign-born population in different territories. In addition,
policymakers should be informed of local socioeconomic processes, a need that stresses
the importance of statistical methods that can reflect spatial heterogeneity, a common
feature in these cases.
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