THE SPATIAL DISTRIBUTION
OF POPULATION IN SPAIN:
AN ANOMALY IN EUROPEAN
PERSPECTIVE
2020
Eduardo Gutiérrez, Enrique Moral-Benito,
Daniel Oto-Peralías and Roberto Ramos
Documentos de Trabajo
N.º 2028
THE SPATIAL DISTRIBUTION OF POPULATION IN SPAIN: AN ANOMALY
IN EUROPEAN PERSPECTIVE
Documentos de Trabajo. N.º 2028
2020
(*) We thank Mario Alloza and Carlos Sanz for sharing the municipality data. We also thank David Cuberes, JavierPérez, Diego Puga, Jacopo Timini and seminar participants at Banco de España for useful comments. The opinions and analyses are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem.
Eduardo Gutiérrez, Enrique Moral-Benito and Roberto Ramos
BANCO DE ESPAÑA
Daniel Oto-Peralías
UNIVERSIDAD PABLO DE OLAVIDE
THE SPATIAL DISTRIBUTION OF POPULATION IN SPAIN:
AN ANOMALY IN EUROPEAN PERSPECTIVE (*)
The Working Paper Series seeks to disseminate original research in economics and fi nance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment.
The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem.
The Banco de España disseminates its main reports and most of its publications via the Internet at the following website: http://www.bde.es.
Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.
© BANCO DE ESPAÑA, Madrid, 2020
ISSN: 1579-8666 (on line)
Abstract
We exploit the GEOSTAT 2011 population grid with a very high 1-km2 resolution to
document that Spain presents the lowest density of settlements among European
countries. We uncover that this anomaly cannot be accounted for by adverse geographic
and climatic conditions. Using techniques from spatial econometrics, we identify the
clusters that exhibit the lowest densities within Spain after controlling for geo-climatic
factors: these areas mainly belong to Teruel, Zaragoza, Ciudad Real, Albacete, Sevilla
and Asturias. We also explore the attributes that characterize the municipalities located
in these low-density areas: larger population losses during the 1950-1991 rural exodus,
higher shares of local-born inhabitants, longer distances to the province capital, higher
shares of population employed in agriculture, and larger increases in regionalist vote
after the Great Recession.
Keywords: economic geography, Spain.
JEL classifi cation: R10.
Resumen
Utilizando información sobre la distribución de la población por cada kilómetro cuadrado
de Europa (GEOSTAT 2011), este artículo documenta que España es el país con un
mayor porcentaje de territorio deshabitado y una mayor concentración de la población
en determinadas zonas geográfi cas. Esta anomalía en la distribución de la población a lo
largo del territorio no puede explicarse por condiciones geográfi cas y climáticas adversas.
Asimismo, utilizando técnicas de la econometría espacial, se identifi can las zonas que
exhiben las densidades más bajas dentro de España al tener en cuenta los factores
geoclimáticos: estas áreas pertenecen principalmente a Teruel, Zaragoza, Ciudad Real,
Albacete, Sevilla y Asturias. Finalmente, se exploran las características de los municipios
ubicados en estas áreas de baja densidad: mayores pérdidas de población durante el
éxodo rural de 1950-1991, mayor proporción de habitantes nacidos en el municipio,
mayores distancias a la capital de la provincia, mayor proporción de población empleada
en la agricultura y mayores aumentos en el porcentaje de voto regionalista después de la
crisis fi nanciera global.
Palabras clave: geografía económica, España.
Códigos JEL: R10.
BANCO DE ESPAÑA 7 DOCUMENTO DE TRABAJO N.º 2028
1Allen and Arkolakis (2014) show that geographic location alone can explain at least 20% of the spatial variationin income across the United States.
2According to Duranton and Turner (2018), most daily activities take place within an area of 10-km2 that doesnot necessarily coincide with administrative areas. In addition, other dimensions beyond administrative units suchas the number of centers within an area or the compactness of development might be relevant in determining urbanoutcomes (Harari 2020).
1 Introduction
How human settlements are distributed across space shapes economic interactions and influences
economic development (see e.g. Krugman 1991).1 This article explores the spatial distribution of
population in Spain compared to that of other European countries. Using the GEOSTAT 2011
population grid, we uncover an anomaly in Spanish settlement patterns. We find that Spain presents
a much lower share of inhabited areas than its European neighbors. Moreover, this pattern cannot
be accounted for by geographic and climatic factors. The remaining of this introduction provides a
detailed advance of the different parts and findings of the paper, which should enable a reader to
grasp in advance the essence of our contribution.
In Section 2, we describe the different datasets we exploit throughout the paper. The GEOSTAT
2011 population grid is the main data source that allows us to analyze the distribution of population
and settlements at a very granular level. In particular, it provides the figures of population living
in each squared kilometer within Europe. This information is crucial to properly measure the dis-
tribution of population across space as opposed to the traditional density indicators (based on the
ratio of population to surface of administrative areas) that do not appropriately reflect the density
actually faced by individuals and firms (see Duranton and Puga, 2020).2 We thus consider three
alternative indicators to compare the actual density experienced across European countries: (i) the
ratio of population to surface within each 250-km2 grid cell; (ii) the so-called settlement density
given by the percentage of 10-km2 cells that are inhabited within each 250-km2 grid cell; (iii) an
indicator of population concentration given by the percentage of the population living in the most
populated one percent of the territory within each 250-km2 grid cell. Section 2 also describes the very
granular information considered in our analysis on geo-climatic factors such as temperature, rainfall,
altitude, ruggedness or soil quality for the different European countries at the grid-cell level. The
municipality-level database exploited in the last section of the paper to characterize underpopulated
areas within Spain is also presented in Section 2.
Section 3 uncovers an anomaly of Spanish settlement patterns in European perspective that
cannot be accounted for by geo-climatic conditions. In particular, the regions with the lowest amount
of settlements relative to its surface within Europe are located in Spain, Iceland, Finland, Norway
and Sweden. However, while the low settlement density is fully explained by adverse geographic and
climatic factors in Scandinavian countries, this is not the case in Spain, which remains the only outlier
in terms of low settlement density after accounting for geo-climatic conditions. Using the alternative
measures of spatial distribution of the population, we conclude that Spain is characterized by a
very low settlement density and a very high concentration of the population in a small share of the
BANCO DE ESPAÑA 8 DOCUMENTO DE TRABAJO N.º 2028
3On the contrary, Madrid, Malaga and Barcelona stand out as the provinces with higher settlement density withinSpain.
4The 35 European countries included are Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina(BI), Bulgaria (BG), Croatia (HR), Czechia (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany
(DE), Greece (EL), Hungary (HU), Iceland (IS), Ireland (IE), Italy (IT), Kosovo (KO), Latvia (LV), Liechtenstein(LI), Lithuania (LT), Luxembourg (LU), Montenegro (ME), Netherlands (NL), Norway (NO), Poland (PL), Portugal(PT), Romania (RO), Serbia (SR), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH) and theUnited Kingdom (UK).
territory. These two characteristics render traditional population density indicators somehow in line
with other European countries, albeit relatively low. According to Oto-Peralıas (2020), insecurity
characterizing the Reconquest period may explain the anomalous spatial distribution of Spanish
population. Continuous warfare and insecurity heavily conditioned the nature of the colonization
process, characterized by the leading role of the military orders as colonizer agents, scarcity of
population, and a livestock-oriented economy (Gonzalez-Jimenez 1992). This determined a spatial
distribution of the population characterized by a low density of settlements and an economy based
on ranching, as mobile assets were favored by military conditions (see Bishko, 1975).
In Section 4, we characterize underpopulated areas within Spain along two dimensions. First,
we use spatial econometric techniques to identify clusters of underpopulated areas once we account
for geography and climate. Our results indicate that these areas are mainly located in the provinces
of Teruel, Zaragoza, Ciudad Real, Albacete, Sevilla and Asturias.3 Second, we exploit density
measures at the municipality level together with socio-economic characteristics of each municipality
in order to characterize the underpopulated areas. Our findings suggest that low-density areas
belong to municipalities characterized by higher shares of local-born inhabitants that experienced
large population losses during the 1950-1991 rural exodus, longer distances to the province capital,
higher shares of population employed in agriculture, and larger increases in regionalist vote after the
Great Recession. Finally, underpopulated municipalities tend to present lower income per capita in
nominal terms but, interestingly enough, higher income per capita if we use a PPP-adjustment based
on municipality-level housing prices taken from Registro de la Propiedad.
2 Data
2.1 GEOSTAT 2011 population grid
The GEOSTAT population grid provides data on population distribution in space at a very high
1-km2 resolution (Eurostat 2016). The sample consists of the territory covered by GEOSTAT 2011
after excluding the overseas regions (see Figure 1), an area slightly larger than 5 million km2 with
approximately 2.08 million populated 1-km2 grid cells.4
We construct three indicators measuring different dimensions of the spatial distribution of popu-
lation in the different countries. First, a settlement density indicator that measures the distribution
of settlements along the territory. More specifically, it refers to the percentage of 10-km2 grid cells
BANCO DE ESPAÑA 9 DOCUMENTO DE TRABAJO N.º 2028
that are inhabited in each 250-km2 cell. A 10-km2 grid cell is considered to be populated if it contains
at least one 1-km2 populated cell within it. We choose 10-km2 as cell area because it is a meaningful
size from an economic point of view. For instance, Duranton and Turner (2018) conclude that most
daily activities take place within an area of 10-km2. Also, the average size of a commune in France
is 15-km2 and, typically, each commune has more than one settlement. It is worth noting that,
according to this indicator, settlements are identified through the presence of populated 1-km2 cells.
An important advantage of this way of identifying settlements is its homogeneity across countries.
Other alternatives such as data on municipalities or on local administrative units cannot be used for
comparative purposes since they are heterogeneous across countries.
Second, we compute an indicator of population concentration that measures the percentage of
the population living in the most populated one percent of the territory within each 250-km2 grid
cell. It is worth stressing that the level of spatial aggregation used is important to this indicator.
Population concentration may be high at the country level but moderate or low at the sub-national
level. However, there is in practice a strong correlation (0.86) between the country level value and
the average of grid-cell level values.
Third, we also consider a traditional indicator of population density given by the ratio of pop-
ulation to surface in each 250-km2 grid cell. Regarding the correlation among these indicators,
settlement density is negatively correlated with population concentration (-0.75) and positively with
the logarithm of population density (0.77), while population concentration and population density
are negatively correlated (-0.58). Table A.1 provides some descriptive statistics of the three indicators
at the grid-cell level.
2.2 Geographic and climatic factors
We construct several geographic and climatic variables at the grid cell level, including temperature,
rainfall, altitude, ruggedness, distance from the coast and soil quality (see Table A.1 for descriptive
statistics).
Temperature and rainfall refer to annual average temperature and annual precipitation in hun-
dred of milliliters, respectively. They are both sourced from the WorldClim database (Hijmans et al.,
2005). Altitude refers to average altitude of the surface area of the observation unit while ruggedness
corresponds to the standard deviation of the altitude of the territory corresponding to the observa-
tion unit, both are constructed using data from GTOPO30 (Data available from the U.S. Geological
Survey). Distance to the coast refers to the geodesic distance between the centroid of the observa-
tion units and the nearest point of the coast (in km). Soil quality, taken from from Fischer et al.
(2008), corresponds to the average of seven key soil dimensions important for crop production: nu-
trient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess
salts, toxicities, and workability (the average value for each component is calculated for the surface
area corresponding to the observation unit). Finally, we also consider latitude and longitude, the
geographic coordinates of the grid cell centroids in decimal degrees.
BANCO DE ESPAÑA 10 DOCUMENTO DE TRABAJO N.º 2028
5Collantes and Pinilla (2011) provide a detailed description of rural-urban migrations over this period.6Note that the fiscal variables only refer to the city budget and include items such as spending on garbage collection
or street lighting and revenues from property taxes. However, they do not include public spending on e.g. health andeducation, and public revenues from income or value added taxes.
2.3 Municipality-level variables for Spain
We also conduct part of the analysis at the municipality level within Spain. We thus collect several
demographic, political, fiscal and economic variables.
Demographic variables include the dependency ratio and the share of citizens born in the mu-
nicipality, which are extracted from the 2011 Census. The former refers to the share of citizens
older than 65 over the population aged 15-64. We also consider the share of citizens born in the
municipality as an indicator of the attraction of the area with an average of almost 50% of citizens
living where they were born. We also explore how migration flows during the so-called rural exodus
from 1950 to 1991 can explain current disparities in population density across municipalities.5
With respect to political variables, we use the share of discontent vote in 2019 general elections
and the increase of regionalist votes between 2007 and 2019. These municipal variables derive from
the Spanish Ministry of Home Office. While extreme political parties surged after the crisis reaching
18% of the vote on average in 2019, the share of regionalist votes increased by around 3pp over the
same period.
Lastly, we measure fiscal characteristics of municipalities with public spending and public revenues
per capita;6 and socio-economic conditions with the share population employed in agriculture from
the 2011 Census and the income per capita in 2015 published by INE. Average public spending
is around 1,222 euros per capita, slightly below average revenue per capita. The average share of
population employed in agriculture is around 7% and income per capita in the (unweighted) average
municipality is around 10,000 euros. Descriptive statistics at the municipality level are available in
Table A.2.
3 The Spanish anomaly in settlement patterns
This section documents that the spatial distribution of the population in Spain represents an anomaly
with respect to other European countries. In particular, it presents an abnormally low settlement
density with the highest prevalence of uninhabited areas even after accounting for geographic and
climatic conditions. The section also provides some heuristic discussion on the potential causes of
this pattern, which seem to be rooted in historical patterns of warfare.
3.1 A first glimpse at the data
We first describe the main features of the population location in Spain in comparison with other
European countries. Compared to other European Union countries, population in Spain stands out
for two characteristics. First, total population is low. And second, it is highly concentrated. The
BANCO DE ESPAÑA 11 DOCUMENTO DE TRABAJO N.º 2028
first two columns of Table 1 show that, despite being the second country by land area, Spain only
ranks 5th in terms of total population. For example, the number of inhabitants is 25% lower than
in the United Kingdom, despite the land area being twice as much.
The low number of people living in Spain stems from the fact that there is a very high amount of
uninhabited areas. Figure 1, which plots in red (white) the inhabited (uninhabited) 1 km2 grid cells
in Europe, shows that this feature stands out in sharp contrast with other countries. Indeed, Spain
appears comparable only to areas where geographical and climatological conditions deter population
settlements, such as the Scandinavian Peninsula or the Alps. Column 4 of Table 1 shows that only
13% of the Spanish land area is inhabited, which is the lowest value in the European Union.
This exceptionally low settlement density makes population in Spain be very concentrated. In-
deed, column 5 of Table 1 reveals that the population density in inhabited areas reaches 737 people
per squared km, which is the second largest in Europe (being the largest the tiny island of Malta). If
we compute Lorenz curves estimating the inequality in the spatial location of population along the
Table 1: Population Density in European Union Countries (2011).
Surface (km2) Population (thousands) Density Inhabited area (%) Density inhabited area(1) (2) (3) (4) (5)
France 549,060 62,765 114 67.8 168Spain 498,504 46,816 94 12.7 737Sweden 449,896 9,539 21 25.2 84Germany 358,327 80,213 224 59.9 374Finland 337,547 5,340 16 30.0 53Poland 313,851 38,500 123 62.6 196Italy 301,291 59,429 197 57.2 345United Kingdom 247,763 63,154 255 51.7 493Romania 239,068 20,122 84 29.4 287Greece 131,912 10,634 81 19.7 409Bulgaria 110,995 7,365 66 21.3 312Hungary 93,013 9,938 107 29.8 358Portugal 88,847 10,562 119 46.6 255Austria 83,944 8,402 100 51.4 195Czech Republic 78,874 10,437 132 56.0 236Ireland 70,601 4,575 65 80.2 81Latvia 65,519 2,081 32 50.5 63Lithuania 65,412 3,029 46 54.5 85Croatia 56,539 4,290 76 43.4 175Slovakia 49,035 5,399 110 32.4 340Estonia 45,347 1,294 29 45.6 63Denkmark 43,162 5,535 128 90.4 142Netherlands 37,824 16,651 440 81.0 544Belgium 30,668 10,990 358 83.0 432Slovenia 20,277 2,049 101 66.0 153Cyprus 9,249 840 91 37.3 244Luxembourg 2,595 513 198 65.5 302Malta 315 417 1325 92.7 1430
Source: Eurostat.
Note however that this anomaly is somehow masked when looking at traditional population density
measures at the aggregate level (inhabitants per square km) in which Spain ranks slightly below the
mean (see column 3).7
7Rae (2018) shows inhabited areas density in 39 European countries to reflect that the traditional measure is notcomplete.
BANCO DE ESPAÑA 12 DOCUMENTO DE TRABAJO N.º 2028
8We also consider a third measure of population concentration. Since the findings corresponding to this measureare very similar to those of settlement density, its discussion is relegated to Appendix B.
3.2 Testing for the anomaly and the role of geo-climatic factors
The evidence presented in Table 1 and Figure 1 is suggestive of a Spanish anomaly in population
patterns. However, Spain is characterized by high temperatures and large mountainous lands that
might account for the high prevalence of uninhabited areas. This section explores whether geographic
and climatic factors may explain the differences between Spain and other European countries in the
distribution of population across the territory.
Using the GEOSTAT 2011 population grid, we compute two proxies for the settlement patterns in
the different countries: settlement density and population density.8 Crucially, using the GEOSTAT
2011 population grid allows us to identify population patterns through 1-km2 cells.
inhabited 1 km2 grid cells, one can see that Spain is the most spatially concentrated country among
the selected ones. For example, while in Germany, Poland and Portugal 15% of the most populated
cells account for 80% of total population, in Spain they account for 90%. The corresponding figure
for France, Italy and the United Kingdom is 85%.
All in all, Table 1 suggests that population in Spain is very concentrated in the space because
there is an abnormally large number of uninhabited areas in comparison to other European countries.
According to these measures, we are able to identify a Spanish anomaly in settlement patterns.
where yic refers to either settlement density or (log) population density of 250-km2 grid cell i be-
longing to country c. xic is a vector of geographic and climatic variables for each grid cell including
temperature, rainfall, average altitude, ruggedness, soil quality and distance from the coast. In or-
der to allow for non-linear effects of those variables on settlement patterns, quartile dummies are
included with the omitted category being the first quartile. An island dummy and latitude and lon-
gitude coordinates are also included in the vector xic. Table A.3 in Appendix A shows the estimated
coefficients for the geographic and climatic variables included in the regressions.
Finally, our main object of interest in equation (1) is the vector ηc that refers to a full set of
country dummies. In order to test the significance of the Spanish anomaly after accounting for
geography and climate, we analyze the estimated country dummies (ηc) from equation (1) and their
associated standard errors. France is the omitted category in all cases so that the estimated country
dummies reflect the average difference in settlement density between each country and France.
The following regression allows us to better understand the role of geography and climate in
explaining the Spanish anomaly in terms of settlement density and population density:
yic = ηc + x′icβ + εic (1)
BANCO DE ESPAÑA 13 DOCUMENTO DE TRABAJO N.º 2028
Source: Eurostat.Notes:This figure depicts in red (white) the inhabited (uninhabited) 1 km2 grid cells in Europe.
Figure 1: Inhabited Grid Cells in Europe (2011).
Figure 2: The Spanish anomaly in settlement and population density.
AT
BE
DE
FI
IE
IT
LU
NL
PT
UK
ES
−50 0 50 100 −50 0 50 100
Baseline Geo−climatic controls
AT
BE
DE
FI
IE
IT
LU
NL
PT
UK
ES
−5 0 5 10 −5 0 5 10
Baseline Geo−climatic controls
Figure 2 presents the estimated country fixed effects without controls (baseline) and with ge-
ographic and climatic controls (geo-climatic controls) for both settlement density (left panel) and
population density (right panel). In the baseline panels without controls, Spain is the country with
the lowest share of inhabited 10km2 areas within each 250-km2 grid cell as well as the second lowest
BANCO DE ESPAÑA 14 DOCUMENTO DE TRABAJO N.º 2028
9Figure A.1 in Appendix A shows that these findings also hold in a larger sample of European countries. However,for the sake of clarity we present here the results for the Eurozone sample and the UK.
population density only above Finland. For instance, the average 250-km2 grid cell in Spain presents
a settlement density 50 percentage points lower than the corresponding average cell in France, while
this figure is around 30 pp. in Finland. Interestingly enough, this difference becomes positive and
statistically significant in the case of Finland after accounting for geographic and climatic factors,
while it remains negative and significant in Spain, albeit smaller in magnitude.
The left panel of Figure 2 clearly illustrates that geography and climate cannot fully explain
the Spanish low settlement density, which is the lowest within Europe.9 In sharp contrast, the
Finnish low settlement density is fully explained by geographic and climatic factors. Indeed, after
accounting for those factors, settlement density in Finland is the highest in Europe. This pattern
remains unaltered if we use population concentration instead of settlement density as the dependent
variable in our regressions as shown in Figure B.6 in Appendix B. Population concentration is thus
the highest in Spain even after accounting for geography and climate. Indeed, both variables present
a very high and negative correlation of -0.75. Turning to population density in the right panel of
Figure 2, the difference between the average Spanish and French 250-km2 grid cell is not statistically
significant once we account for geo-climatic conditions.
A potential concern is that we are comparing countries that are very heterogeneous in surface
area. One could suspect that the singularity of Spain is perhaps conditional on the specific territorial
division used in our analysis. In order to address this concern, we compare Spain to a set of virtual
countries with similar surface areas. For that purpose, we randomly choose 1,000 grid cells and
then create virtual countries by selecting up to 2,500 neighbors falling within 700 km from the cell’s
centroid (similar to Spanish land area). Then we run 1,000 regressions of settlement density on the
full set of geo-climatic controls, the Spain dummy, and the virtual countries dummies (included one
by one). Remarkably, in every case the coefficient on Spain for settlement density is lower than
on the virtual region (see Figure A.2 in Appendix A). This figure is slightly above 70% in the case
of population density, which somehow confirms the lack of statistical significance of the Spanish
coefficient in the right panel of Figure 2.
In order to further explore the Spanish anomaly, we now turn to the analysis at the regional
level. In particular, we estimate models analogous to equation (1) but instead of including country
dummies, we now include region dummies at the NUTS3 level corresponding to Spanish provinces.
The omitted category is now the Paris area (FR101) so that all estimated dummies capture the
difference with respect to Paris.
The left panel of Figure 3 confirms the Spanish anomaly in settlement density by region. The
y-axis plots the estimated region dummies without geographic and climatic controls while the x-
axis refers to the estimated dummies with those controls. Interestingly enough, region dummies of
nordic countries such as Iceland (IS), Sweden (SE), Finland (FI) and Norway (NO) present highly
negative coefficients without geographic controls (much lower settlement densities than Paris) that
BANCO DE ESPAÑA 15 DOCUMENTO DE TRABAJO N.º 2028
Figure 3: The Spanish anomaly by regions.
ALB0
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BE100BE211BE212BE213BE221BE222BE223BE231BE232BE233BE234BE235BE236BE241BE242BE251BE252BE253BE254BE255BE256BE257BE258BE310BE321BE322BE323BE324BE325BE326BE327BE331BE332BE334
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DE111DE112
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DEA26DEA27 DEA28DEA29 DEA2ADEA2BDEA2CDEA2DDEA32DEA33DEA34DEA35DEA36DEA37DEA38DEA41DEA42DEA43DEA44
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DEA5C DEB12DEB13DEB14
DEB15
DEB16DEB17DEB18 DEB19DEB1ADEB1BDEB21DEB22DEB23
DEB24DEB25DEB32
DEB35DEB36
DEB3ADEB3B
DEB3C
DEB3D
DEB3E
DEB3F
DEB3GDEB3HDEB3J
DEB3K
DEC01DEC02DEC03DEC04DEC05DEC06DED21DED2C
DED2D
DED2EDED2FDED41
DED42DED43DED44DED45DED51DED52DED53
DEE01DEE03DEE04DEE05
DEE06DEE07
DEE08
DEE09DEE0A
DEE0BDEE0CDEE0D
DEE0E
DEF03DEF05DEF06DEF07
DEF08DEF09DEF0ADEF0B
DEF0C
DEF0DDEF0EDEF0FDEG01DEG02DEG03
DEG04DEG05
DEG06
DEG07DEG09
DEG0ADEG0B
DEG0CDEG0D
DEG0EDEG0F
DEG0G
DEG0H
DEG0IDEG0JDEG0KDEG0LDEG0M
DEG0PDK011
DK012DK013DK014DK021DK022DK031DK032
DK041DK042DK050
EE001EE004
EE006
EE007
EE008
EL111
EL112EL113
EL114
EL115EL121EL122EL123
EL124
EL125EL126EL127
EL131EL132EL133
EL134
EL141
EL142
EL143 EL144
EL211
EL212
EL213
EL214EL221
EL222
EL223EL224
EL231
EL232
EL233
EL241
EL242
EL243
EL244EL245EL251 EL252
EL253
EL254
EL255EL300
EL411
EL412
EL413
EL421
EL422
EL431
EL432
EL433
EL434
FI193FI194FI195
FI196FI197FI1B1FI1C1FI1C2
FI1C3FI1C4FI1C5FI1D1FI1D2
FI1D3
FI1D4
FI1D5FI1D6
FI1D7
FI200
FR103FR104FR105FR106FR107FR108
FR211FR212FR213FR214
FR221FR222FR223FR231FR232FR241FR242FR243FR244FR245FR246FR251FR252FR253
FR261FR262FR263
FR264FR301FR302
FR411
FR412
FR413FR414
FR421FR422FR431FR432FR433FR434FR511FR512FR513FR514FR515FR521FR522FR523FR524FR531FR532FR533FR534FR611
FR612FR613
FR614
FR615
FR621
FR622FR623
FR624FR625
FR626
FR627FR628 FR631FR632FR633FR711FR712FR713
FR714
FR715FR716
FR717
FR718
FR721FR722FR723FR724FR811FR812FR813
FR814
FR815FR821
FR822
FR823
FR824
FR825FR826
FR831FR832 HR031HR032
HR033
HR034HR035
HR036
HR037
HR041HR042
HR043HR044HR045
HR046HR047
HR048
HR049
HR04A
HR04BHR04C
HR04DHR04E
HU101
HU102
HU211
HU212
HU213HU221HU222
HU223HU231
HU232HU233
HU311
HU312
HU313
HU321
HU322
HU323
HU331
HU332
HU333
IE011IE012
IE013
IE021IE022IE023IE024IE025
IS001
IS002
ITC11
ITC12ITC13
ITC14
ITC15
ITC16
ITC17ITC18
ITC20
ITC31
ITC32ITC33ITC34ITC41
ITC42ITC43
ITC44
ITC46ITC47
ITC48ITC49ITC4AITC4BITC4CITC4D
ITF11
ITF12ITF13
ITF14
ITF21
ITF22ITF31ITF32ITF33
ITF34ITF35
ITF43ITF44ITF45
ITF46
ITF47
ITF48
ITF51ITF52ITF61ITF62ITF63
ITF64ITF65
ITG11
ITG12
ITG13ITG14
ITG15ITG16
ITG17
ITG18ITG19
ITG25
ITG26
ITG27ITG28
ITG29
ITG2A
ITG2BITG2C
ITH10ITH20
ITH31ITH32
ITH33
ITH34ITH35ITH36ITH37
ITH41 ITH42
ITH43ITH44ITH51ITH52ITH53ITH54ITH55
ITH56
ITH57ITH58
ITH59
ITI11
ITI12ITI13ITI14ITI15
ITI16
ITI17 ITI18ITI19
ITI1AITI21ITI22ITI31ITI32
ITI33ITI34
ITI35
ITI41ITI42
ITI43ITI44
ITI45
KOS25
KOS26
KOS27
KOS28KOS29
KOS30
KOS31
KOS33KOS34
KOS35
KOS36KOS37
KOS39
KOS40KOS42
KOS43
KOS44KOS45
KOS46
KOS47
KOS48
KOS49KOS50KOS51
KOS52
KOS53LI000LT001
LT002LT003
LT004LT005LT006LT007
LT008LT009LT00A
LU000
LV003LV005
LV006
LV007 LV008LV009
ME00
NL111NL112
NL113
NL121
NL122NL123NL131NL132NL133NL211NL212NL213NL221NL224NL225NL226NL230NL310NL321NL322
NL323NL325NL326NL327NL332NL333NL337
NL338
NL339
NL33ANL341NL342NL411NL412NL413NL414NL421NL422NL423NO011
NO012
NO021
NO022
NO031
NO032
NO033
NO034NO041
NO042NO043
NO051
NO052
NO053
NO061
NO062 NO071
NO072
NO073
PL113PL114PL115PL116PL117PL121PL122PL127PL128PL129PL12APL213PL214
PL215
PL216PL217PL224PL225
PL227PL228
PL229
PL22APL22BPL22C
PL311PL312PL314PL315
PL323
PL324PL325PL326PL331PL332
PL343PL344PL345PL411
PL414PL415PL416PL417PL418
PL422PL423PL424
PL425
PL431
PL432
PL514
PL515
PL516 PL517PL518PL521PL522PL613
PL614PL615PL621PL622PL623PL631
PL633PL634
PL635
PT111
PT112PT113PT114PT115PT116
PT117
PT118
PT150PT161PT162PT163
PT164PT165PT166PT167
PT168
PT169
PT16A
PT16B
PT16CPT171
PT172
PT181
PT182
PT183
PT184
PT185RO111
RO112
RO113
RO114
RO115
RO116
RO121
RO122
RO123RO124
RO125
RO126
RO211
RO212
RO213
RO214
RO215
RO216
RO221
RO222
RO223
RO224
RO225
RO226RO311
RO312
RO313
RO314
RO315
RO316
RO317
RO322
RO411
RO412RO413RO414 RO415
RO421
RO422
RO423
RO424
SE110
SE121
SE122SE123
SE124SE125
SE211SE212SE213SE214
SE221SE224SE231
SE232
SE311
SE312
SE313SE321
SE322
SE331
SE332
SI011 SI012SI013
SI014SI016
SI017
SI018
SI021
SI022SI023
SI024
SK010
SK021 SK022SK023
SK031
SK032SK041SK042
SRB54
SRB55
SRB56
SRB57SRB58SRB59
SRB60SRB61SRB62
SRB63SRB64SRB65SRB66SRB67
SRB68
SRB90SRB91
SRB92SRB93
SRB94
SRB95
SRB96SRB97
SRB98
SRB99
UKC11UKC12
UKC14UKC21
UKC22UKC23
UKD11UKD12
UKD31
UKD32
UKD41UKD43
UKD61UKD62UKD63UKD71UKD72UKD73UKD74UKE12UKE13UKE21
UKE22
UKE31
UKE32
UKE41UKE42UKE44
UKE45UKF12UKF13
UKF15UKF16UKF22UKF24UKF25UKF30UKG11UKG12UKG13UKG21UKG22UKG24UKG31UKG32UKG37UKH11UKH12UKH13UKH14UKH23UKH24UKH25UKH32UKH33UKI12UKI21UKI22UKI23UKJ11UKJ12UKJ13UKJ14UKJ21UKJ22UKJ23UKJ24UKJ31UKJ33UKJ34UKJ42UKK11UKK12UKK13UKK14UKK15
UKK21UKK22UKK23UKK30UKK42UKK43
UKL11
UKL12
UKL13UKL14
UKL15UKL16UKL17
UKL18UKL21UKL22UKL23
UKL24
UKM21
UKM22
UKM23
UKM24
UKM26
UKM27
UKM28
UKM31
UKM32
UKM33
UKM34UKM35UKM36
UKM37
UKM38
UKM50
UKM61
UKM62
UKM63
UKM64
UKM65
UKM66
UKN02
UKN03UKN04
UKN05ES111
ES112
ES113
ES114
ES120ES130ES211
ES212
ES213
ES220ES230
ES241
ES242ES243
ES300
ES411
ES412ES413
ES414ES415ES416
ES417
ES418
ES419
ES421ES422
ES423
ES424
ES425
ES431ES432
ES511ES512
ES513
ES514
ES521
ES522
ES523
ES531
ES532
ES533
ES611
ES612
ES613
ES614
ES615ES616
ES617
ES618
ES620
−100
−80
−60
−40
−20
0Se
ttlem
ent d
ensi
ty
−50 0 50 100Settlement density (geo−climatic controls)
ALB0ALB1
ALB2ALB3 ALB4
ALB5ALB6ALB7
ALB8ALB9
ALB99
AT111
AT112
AT113AT121AT122
AT123
AT124AT125
AT126AT127
AT130
AT211
AT212AT213
AT221
AT222AT223AT224
AT225
AT226
AT311
AT312
AT313AT314
AT315
AT321AT322
AT323
AT331
AT332
AT333AT334
AT335
AT341
AT342
BE100
BE211BE212
BE213
BE221
BE222
BE223BE231BE232
BE233
BE234BE235BE236BE241BE242BE251
BE252BE253
BE254BE255
BE256BE257
BE258BE310
BE321
BE322
BE323BE324
BE325
BE326
BE327BE331BE332
BE334BE335
BE336
BE341
BE342BE343
BE344BE345
BE351
BE352
BE353
BG311BG312BG313BG314
BG315BG321 BG322BG323
BG324
BG325
BG331
BG332
BG333BG334
BG341
BG342
BG343
BG344
BG411
BG412
BG413BG414
BG415
BG421
BG422BG423
BG424BG425
BIH10
BIH11BIH12
BIH13
BIH14BIH15BIH16
BIH17
BIH18
BIH19BIH20
BIH21
BIH22BIH23BIH24
BIH97
BIH98
BIH99
CH011
CH012
CH013CH021
CH022CH023
CH024
CH025
CH032CH033 CH040
CH051
CH052
CH053
CH054CH055
CH056
CH057
CH061
CH062
CH063
CH064
CH066
CH070
CZ010
CZ020
CZ031CZ032 CZ041
CZ042CZ051CZ052
CZ053CZ063CZ064CZ071CZ072CZ080
DE111
DE112DE113DE114DE115DE116
DE117
DE118DE119DE11ADE11B
DE11CDE11DDE121
DE122
DE123DE124
DE126
DE127
DE128DE129
DE12ADE12B
DE12C
DE131
DE132DE133DE134 DE135DE136DE137DE138DE139
DE13A
DE141DE142
DE143DE145DE146DE147DE148DE149
DE212
DE213
DE214
DE215DE216
DE217DE218DE219
DE21ADE21BDE21C
DE21D
DE21E
DE21F
DE21G
DE21H
DE21IDE21J
DE21KDE21L
DE21MDE21N
DE222DE223DE224
DE225DE226DE227DE228
DE229
DE22ADE22BDE22C
DE232
DE234DE235DE236DE237DE238DE239DE23A
DE241
DE245DE246DE247DE248DE249
DE24ADE24BDE24C
DE24DDE251
DE254
DE255
DE256
DE257DE258
DE259DE25ADE25BDE25C
DE262DE264
DE265DE266DE267DE268DE269
DE26ADE26B
DE26C
DE271
DE272DE274DE275DE276DE277DE278DE279
DE27A
DE27B
DE27CDE27D
DE27E
DE300
DE401DE402
DE403DE404
DE405DE406DE407DE408DE409DE40ADE40BDE40CDE40D
DE40EDE40F
DE40GDE40H
DE40I
DE501DE502
DE600
DE711
DE712
DE715DE716DE717DE718DE719
DE71A
DE71B
DE71C
DE71DDE71EDE721DE722DE723DE724DE725
DE731
DE732DE733DE734DE735DE736DE737
DE801DE802 DE803DE804
DE807DE808DE809DE80ADE80BDE80CDE80D
DE80EDE80FDE80G
DE80HDE80I
DE911DE912DE913
DE914DE915DE916DE917
DE918DE919
DE91ADE91B
DE922DE923DE925
DE926DE927
DE928DE929
DE931DE932DE933
DE934
DE935DE936
DE937DE938DE939
DE93A
DE93B
DE944DE945
DE946DE947DE948DE949DE94ADE94B
DE94C
DE94DDE94EDE94FDE94GDE94H
DEA11DEA12DEA13
DEA15
DEA1B
DEA1CDEA1D
DEA1EDEA1F
DEA22DEA23
DEA26
DEA27
DEA28
DEA29 DEA2A
DEA2B
DEA2CDEA2D
DEA32DEA33
DEA34DEA35DEA36
DEA37DEA38
DEA41
DEA42DEA43
DEA44DEA45DEA46 DEA47
DEA52
DEA53DEA54
DEA55DEA56
DEA57
DEA58DEA59DEA5ADEA5B
DEA5C
DEB12DEB13
DEB14DEB15DEB16
DEB17DEB18
DEB19DEB1ADEB1BDEB21
DEB22
DEB23DEB24
DEB25
DEB32
DEB35
DEB36DEB3ADEB3BDEB3CDEB3D
DEB3E
DEB3FDEB3G
DEB3HDEB3J
DEB3K
DEC01
DEC02
DEC03DEC04DEC05DEC06
DED21
DED2CDED2D
DED2EDED2F
DED41
DED42DED43DED44
DED45
DED51
DED52DED53
DEE01
DEE03
DEE04
DEE05DEE06DEE07
DEE08DEE09DEE0A
DEE0BDEE0C
DEE0DDEE0E
DEF03
DEF05DEF06
DEF07DEF08
DEF09DEF0ADEF0BDEF0CDEF0D
DEF0E
DEF0FDEG01DEG02DEG03DEG04DEG05
DEG06DEG07DEG09DEG0ADEG0BDEG0CDEG0DDEG0EDEG0FDEG0GDEG0HDEG0IDEG0JDEG0K
DEG0LDEG0MDEG0P
DK011DK012
DK013
DK014DK021
DK022DK031DK032DK041DK042DK050 EE001
EE004EE006
EE007
EE008EL111
EL112EL113
EL114
EL115EL121EL122
EL123EL124
EL125EL126
EL127EL131EL132
EL133
EL134
EL141EL142
EL143
EL144EL211
EL212EL213
EL214EL221
EL222
EL223
EL224EL231EL232EL233
EL241EL242 EL243
EL244EL245
EL251 EL252EL253EL254
EL255EL300
EL411
EL412
EL413EL421EL422
EL431
EL432
EL433
EL434FI193
FI194FI195FI196
FI197
FI1B1
FI1C1FI1C2FI1C3FI1C4
FI1C5FI1D1FI1D2
FI1D3
FI1D4
FI1D5
FI1D6
FI1D7
FI200
FR103FR104
FR105FR106FR107
FR108
FR211FR212FR213FR214
FR221FR222
FR223FR231FR232
FR241FR242
FR243FR244FR245
FR246FR251FR252
FR253FR261FR262
FR263FR264
FR301FR302
FR411
FR412
FR413FR414
FR421FR422
FR431FR432FR433
FR434FR511FR512
FR513FR514FR515FR521
FR522FR523FR524FR531
FR532FR533FR534FR611FR612
FR613
FR614FR615
FR621
FR622FR623
FR624FR625FR626
FR627FR628FR631FR632
FR633FR711
FR712FR713
FR714FR715FR716
FR717
FR718FR721
FR722FR723FR724FR811
FR812FR813
FR814FR815FR821 FR822FR823
FR824FR825FR826
FR831FR832 HR031
HR032
HR033HR034HR035
HR036
HR037
HR041
HR042
HR043HR044HR045
HR046
HR047HR048HR049
HR04AHR04BHR04CHR04DHR04E
HU101
HU102HU211
HU212HU213HU221HU222HU223HU231HU232HU233
HU311
HU312HU313HU321
HU322HU323HU331HU332
HU333IE011IE012
IE013
IE021
IE022
IE023IE024IE025
IS001
IS002
ITC11ITC12
ITC13
ITC14
ITC15
ITC16
ITC17ITC18
ITC20
ITC31ITC32ITC33ITC34
ITC41ITC42
ITC43
ITC44
ITC46ITC47ITC48
ITC49ITC4AITC4B
ITC4CITC4D
ITF11
ITF12ITF13
ITF14ITF21
ITF22ITF31ITF32
ITF33
ITF34ITF35ITF43
ITF44ITF45
ITF46
ITF47ITF48
ITF51ITF52
ITF61ITF62 ITF63ITF64ITF65
ITG11
ITG12ITG13ITG14ITG15ITG16ITG17
ITG18ITG19
ITG25ITG26ITG27ITG28
ITG29
ITG2A
ITG2BITG2C
ITH10
ITH20
ITH31ITH32
ITH33
ITH34ITH35ITH36
ITH37ITH41
ITH42
ITH43ITH44
ITH51ITH52ITH53ITH54ITH55
ITH56
ITH57
ITH58
ITH59
ITI11ITI12ITI13ITI14ITI15
ITI16
ITI17ITI18
ITI19ITI1A
ITI21ITI22ITI31ITI32
ITI33ITI34
ITI35
ITI41ITI42
ITI43ITI44
ITI45KOS25
KOS26
KOS27
KOS28
KOS29KOS30
KOS31
KOS33KOS34
KOS35
KOS36KOS37KOS39
KOS40KOS42KOS43
KOS44KOS45
KOS46
KOS47
KOS48 KOS49KOS50KOS51
KOS52
KOS53LI000
LT001
LT002LT003LT004
LT005LT006LT007LT008
LT009LT00A
LU000
LV003 LV005
LV006
LV007LV008LV009ME00
NL111NL112
NL113
NL121
NL122NL123NL131
NL132
NL133NL211NL212NL213
NL221NL224
NL225
NL226
NL230
NL310
NL321
NL322NL323NL325NL326
NL327NL332NL333
NL337NL338
NL339
NL33A
NL341NL342
NL411
NL412NL413
NL414NL421NL422
NL423NO011
NO012
NO021NO022
NO031
NO032
NO033
NO034NO041
NO042NO043NO051NO052
NO053
NO061
NO062 NO071NO072
NO073
PL113
PL114PL115PL116PL117PL121PL122
PL127
PL128PL129PL12A
PL213
PL214
PL215
PL216PL217PL224 PL225
PL227PL228PL229
PL22A
PL22BPL22C
PL311PL312
PL314PL315
PL323
PL324PL325PL326PL331
PL332PL343PL344
PL345
PL411PL414
PL415
PL416PL417PL418
PL422PL423
PL424
PL425PL431PL432
PL514
PL515PL516 PL517PL518PL521PL522
PL613PL614PL615
PL621PL622PL623PL631
PL633
PL634PL635PT111
PT112PT113
PT114
PT115PT116
PT117
PT118
PT150
PT161PT162PT163
PT164PT165
PT166
PT167
PT168PT169
PT16A
PT16B
PT16C
PT171
PT172
PT181PT182PT183
PT184
PT185RO111RO112RO113
RO114
RO115RO116RO121
RO122RO123RO124
RO125
RO126
RO211RO212RO213
RO214RO215RO216
RO221RO222RO223
RO224
RO225
RO226RO311RO312
RO313
RO314RO315
RO316
RO317
RO322
RO411RO412
RO413
RO414RO415RO421RO422RO423
RO424SE110
SE121SE122SE123SE124
SE125SE211SE212SE213
SE214
SE221SE224SE231
SE232
SE311
SE312
SE313SE321
SE322SE331
SE332
SI011
SI012SI013
SI014SI016
SI017SI018
SI021
SI022
SI023SI024
SK010
SK021SK022
SK023
SK031SK032
SK041SK042SRB54
SRB55SRB56
SRB57SRB58SRB59
SRB60SRB61
SRB62SRB63
SRB64
SRB65SRB66SRB67
SRB68SRB90SRB91
SRB92
SRB93
SRB94SRB95
SRB96SRB97
SRB98
SRB99
UKC11UKC12
UKC14
UKC21
UKC22UKC23
UKD11
UKD12
UKD31UKD32
UKD41UKD43
UKD61
UKD62UKD63
UKD71UKD72
UKD73
UKD74
UKE12
UKE13UKE21
UKE22
UKE31
UKE32
UKE41UKE42UKE44UKE45
UKF12
UKF13UKF15
UKF16UKF22UKF24UKF25
UKF30UKG11
UKG12UKG13UKG21
UKG22
UKG24
UKG31
UKG32
UKG37
UKH11UKH12UKH13UKH14
UKH23UKH24
UKH25UKH32
UKH33
UKI12
UKI21UKI22UKI23
UKJ11UKJ12UKJ13UKJ14
UKJ21
UKJ22 UKJ23UKJ24
UKJ31
UKJ33UKJ34UKJ42
UKK11
UKK12UKK13
UKK14
UKK15
UKK21
UKK22UKK23UKK30
UKK42
UKK43UKL11
UKL12
UKL13
UKL14
UKL15UKL16UKL17
UKL18UKL21
UKL22UKL23
UKL24UKM21
UKM22UKM23
UKM24
UKM26
UKM27
UKM28
UKM31UKM32
UKM33
UKM34
UKM35UKM36
UKM37
UKM38UKM50
UKM61UKM62
UKM63UKM64 UKM65UKM66
UKN02
UKN03
UKN04UKN05
ES111
ES112ES113
ES114
ES120
ES130
ES211
ES212ES213
ES220ES230
ES241ES242ES243
ES300
ES411ES412ES413ES414ES415ES416
ES417
ES418
ES419ES421ES422
ES423ES424
ES425ES431
ES432
ES511
ES512
ES513
ES514
ES521
ES522ES523
ES531
ES532ES533
ES611ES612
ES613
ES614
ES615ES616
ES617
ES618ES620
−10
−50
5Po
pula
tion
dens
ity
−5 0 5 10Population density (geo−climatic controls)
turn positive and large after including geo-climatic controls. However, Spanish regions (red dots)
Figure 4: Settlement patterns across Spanish provinces.
Coruña
LugoOrense
Pontevedra
Asturias
Cantabria
Alava
GuipuzcoaVizcaya
NavarraLa Rioja
Huesca
TeruelZaragoza
Madrid
Avila
BurgosLeon
PalenciaSalamanca
Segovia
Soria
Valladolid
Zamora
Guadalajara
Barcelona
Girona
Lleida
Tarragona
Castellon
Ibiza
Mallorca
Menorca
Albacete
Ciudad Real
Cuenca
Toledo
Badajoz
Caceres
Alicante
Valencia
Almeria
Cadiz
Cordoba
Granada
Huelva
Jaen
Malaga
Sevilla Murcia
12
34
56
Popu
latio
n de
nsity
20 40 60 80 100Settlement density
Southern provinces Rest of Spain
Coruña
LugoOrense
Pontevedra
Asturias
Cantabria
Alava
GuipuzcoaVizcaya
NavarraLa Rioja
Huesca
TeruelZaragoza
Madrid
Avila
BurgosLeon
PalenciaSalamanca
Segovia
Soria
Valladolid
Zamora
Guadalajara
Barcelona
Girona
Lleida
Tarragona
Castellon
Ibiza
Mallorca
Menorca
Albacete
Ciudad Real
Cuenca
Toledo
Badajoz
Caceres
Alicante
Valencia
Almeria
Cadiz
Cordoba
Granada
Huelva
Jaen
Malaga
Sevilla Murcia
−4−3
−2−1
01
Popu
latio
n de
nsity
(geo
−clim
atic
con
trols
)
−80 −60 −40 −20 0Settlement density (geo−climatic controls)
Southern provinces Rest of Spain
present in both cases the most negative coefficient estimates among all European regions. Results
are revealing: the 7 regions with the lowest average settlement density are from Spain. Also, 9 out of
the 10 regions with lowest settlement density (and 16 out of 20) are from Spain. The same pattern
(with opposite sign) emerges if we look at population concentration (see Figure B.7 in Appendix B).
In contrast, the right panel suggests that this anomaly is not so prevalent in the case of population
density.
Figure 4 zooms in the differences across Spanish NUTS3 regions (provinces). In particular, Figure
4 plots the relationship between average population density (y-axis, in logs) and average settlement
density (x-axis) at the province level within Spain. On top of the strong positive association between
both measures, the plot shows that southern provinces (red triangles) present, for a given level of
settlement density, population densities higher than the rest of Spanish provinces (blue dots). This
is so even after accounting for geo-climatic factors. This pattern suggests that population is more
concentrated for a given level of settlement density in the Spanish south, which might have been a
limitation to economic development in the past, when the proximity to the farmland was important.
BANCO DE ESPAÑA 16 DOCUMENTO DE TRABAJO N.º 2028
All in all, the evidence presented so far indicates that Spain has a remarkably low density of
settlements, even lower than Scandinavian countries. Moreover, geographic and climatic factors fail
to account for this anomaly.
3.3 History, warfare and the origins of the Spanish anomaly
Our results so far indicate that geographic and climatic factors do not satisfactorily explain Spain’s
population and settlement patterns, particularly in terms of settlement density and population con-
centration, which suggests that the peculiarities of Spanish history may be behind them together
with agglomeration forces. The Spanish anomaly is not a recent phenomenon. Already in the 17th
Figure 5: Persistence in settlement and population density.
10From the Venetian ambassadors Federico Cornaro (1678-81) and Giovanni Cornaro (1681-82), quoted in Brenan(1950: 128).
11To assess the magnitude of these correlations, it is important to bear in mind that the variable measuring densityof population entities in 1787 is not directly comparable to settlement density in 2011. The former captures thenumber of population entities in 1787 divided by surface area while the latter is the percentage of populated 10-km2cells. Similarly, population density in 1787 is not exactly comparable to the indicator in 2011 as the quality of thedata sources is very different. The implication is that correlations would be actually stronger if the methodology ofthe indicators were more similar. See Oto-Peralıas (2020) for more details.
century, European travelers were impressed by the scarcity of settlements: ”One can travel for days
on end without passing a house or village, and the country is abandoned and uncultivated”; ”Spain
gives the impression of being a desert of Libya, so unpopulated it is”.10
The left panel in Figure 5 shows that there is a high correlation between population density in
1787 and settlement density in 2011 (p = 0.65), while the right panel depicts an also strong correlation
between (log) population density in 1787 and (log) population density in 2011 (p = 0.69).11 This
points to the fact that the main features of Spain’s current spatial distribution of the population were
already present at the end of the 18th century, well before Spain began its (late) industrialization
process. Therefore, historical events taking place before this period are probably the responsible
factors.
BANCO DE ESPAÑA 17 DOCUMENTO DE TRABAJO N.º 2028
On the theoretical side, Allen and Donaldson (2018) show that path dependence is important in
determining the level of economic activity across the space and their findings open up the potential
for historical accidents, such as warfare, to play an outsized role in governing where economic activity
occurs. In the case of Spain, previous research stresses the Reconquest as a key historical event in
this regard (Oto-Peralıas and Romero-Avila, 2016; Oto-Peralıas, 2020). Two factors related to the
Reconquest seem to be important here, namely, the pace or rate of the frontier expansion and military
instability due to frontier warfare. Oto-Peralıas and Romero-Avila (2016) argue that a fast frontier
expansion by the Christian kingdoms led to an imperfect colonization of the territory because the
material and human resources available were insufficient relative to the magnitude of the colonization
effort. The consequence was an occupation of the space characterized by a few settlements with large
jurisdictional areas. Consistent with this, the authors find a positive relationship between the rate
of Reconquest and the size of municipalities’ surface areas.
Oto-Peralıas (2020) emphasizes frontier warfare as a key determinant of the way the territory
was colonized by the Christian kingdoms. Military insecurity favored a colonization characterized
by the concentration of the population in a few well defended settlements, scarcity of population
and a livestock-oriented economy. Exploiting a discontinuity in military insecurity across the River
Tagus during the 11th to 13th centuries, Oto-Peralıas (2020) shows that the area south of the river,
more exposed to frontier warfare, has today much lower levels of settlement and population density
and higher population concentration. Interestingly, the difference in settlement density across the
Tagus is close to the whole difference between northern and southern Spain, suggesting that the
explanatory power of historical frontier warfare is important. The evidence supports the conjecture
by some historians linking the prolonged exposure to frontier warfare to population concentration
and scarcity of dispersed village communities (Bishko, 1975).
4 Narrowing down underpopulated areas within Spain
This section identifies the areas within Spain with the lowest settlement and population density
based on hot and cold spot analysis. It also investigates the characteristics of such areas at the
municipality level.
4.1 Hot and cold spot analysis
In order to identify clusters of low-density grid cells, we apply methods from spatial analysis to the
GEOSTAT 2011 population grid. In particular, we consider the hot and cold spot technique from
Getis and Ord (1992). This approach overcomes the border effect problem, that is, the allocation of
population in spatial units such as districts, municipalities or regions.
The so-called Getis-Ord statistic tests whether a grid cell and its neighboring grid cells form a
spatial cluster. In other words, hot and cold spots are detected as spatial outliers. In particular,
BANCO DE ESPAÑA 18 DOCUMENTO DE TRABAJO N.º 2028
the hot (cold) spot is detected when a cell and its neighboring cells have similar values and higher
(lower) values than the average. More formally, the statistic for variable yi in grid cell i is given by:
G∗i (d) =
∑Nj=1 ωij(d)yj∑N
j=1 yj(2)
where yj refers to settlement or population density of grid cell j, N is the number of grid cells and
ωij denotes the ijth element of the spatial weight matrix as
ωij(d) = 1 if dij ≤ d ∀i, jωij(d) = 0 if dij > d ∀i, j
where d is the threshold distance.
Figure 6 plots the resulting hot and cold spots within Spain for both settlement density and
population density using d = 50km.12 According to the upper-left map in Figure 6, most of the
territory in the provinces of Badajoz, Caceres, Ciudad Real, Albacete, Cordoba and Jaen presents
the lowest settlement density within Spain (below the 10th percentile). In contrast, settlement
densities in Galicia, Paıs Vasco and Barcelona are the highest (above the 90th percentile). In the
upper-right map we control for geo-climatic variables, which alters the picture. Low density areas
of Extremadura and Castilla la Mancha as well as high density in Galicia and Asturias are partly
explained by these geo-climatic conditions, which are better in the North that is closer to the coast
and presents better soil quality and more rainfall (see Figure A.3 in Appendix A). Indeed, once
we account for the role of geo-climatic conditions both the Madrid13 and the Malaga areas appear
as the highest-density zones within Spain together with Barcelona. Contrarily, the areas with the
lowest settlement density belong not only to Ciudad Real and Albacete but also to Sevilla, Zaragoza,
Teruel, and Asturias.
A very similar picture arises when looking at the two bottom maps of Figure 6 based on hot
and cold spot analysis for (log) population density. The clusters of high population density after
accounting for geo-climatic conditions belong to the provinces of Madrid, Malaga and Barcelona.
The low-density areas are mainly located in Ciudad Real, Sevilla, Albacete, Zaragoza and Teruel.
The similar pictures arising from the hotspot analysis of settlement and population density are
not surprising since the correlation between the resulting G∗i (d) statistics for both indicators is 0.8.
We thus conclude that underpopulated areas within Spain identified from hotspot techniques are
somehow the same regardless on how we measure underpopulation, either in terms of settlement or
population density. Needless to say, the pattern is also the same with population concentration that
presents a correlation of -0.91 with the G∗i (d) statistic from settlement density (see Figure B.8 in
Appendix B).
12Figure A.4 in Appendix A shows the analysis based on d = 20km.13In Appendix C, we explore the settlement of density in each Spanish region compared with Madrid.
BANCO DE ESPAÑA 19 DOCUMENTO DE TRABAJO N.º 2028
Figure 6: Hotspot analysis.
4.2 Characterizing underpopulated municipalities
In order to characterize the identified underpopulated areas within Spain, we need to exploit data at
the municipality level because economic, demographic, fiscal and political variables are only available
at this level. See Section 2.3 for more details on the municipality database as well as the descriptive
statistics in Table A.2 and the geographical distribution of the different characteristics in A.5.
In order to have a first glimpse at the data, we show in Figure 7 the simple bivariate correlations
between log population density and each of the considered municipality characteristics. In particular,
we group the municipality characteristics into four categories: demographic variables (dependency
ratio and share of inhabitants born in the municipality), rural exodus (population growth rate be-
tween 1950 and 1991), political variables (share of discontent votes in 2019 general elections and
increase in the share of votes to regionalist parties between 2007 and 2019), fiscal variables (public
spending per capita and public revenues per capita) and economic variables (share of population
employed in agriculture and log income per capita).
The patterns in Figure 7 suggest that low-density municipalities are characterized by longer
distances to the province capital, higher dependency ratios, higher shares of local-born population,
negative population growth over the 1950-1991 period, higher prevalence of regionalist vote but lower
prevalence of discontent vote, higher (lower) public spending (revenues) per capita, higher employ-
ment shares in agriculture and lower income per capita in nominal terms but higher in purchasing
power parity (PPP) terms.
In any case, the bivariate correlations shown in Figure 7 do not take into account partial correla-
tions among the different characteristics and they do not allow assessing either the relative strength
BANCO DE ESPAÑA 20 DOCUMENTO DE TRABAJO N.º 2028
14Alternatively, one can also consider as dependent variables the G∗i (d) measures computed at the 250-km2 grid cell
level using surface-based weights to compute municipality-level measures. However, these measures present strongerspatial correlation by construction and significantly reduce variation in the data. Anyhow, unreported estimatesconfirm that the main findings in this section remain robust to this alternative specification.
Figure 7: Bivariate correlations.
Notes: This figure shows the bivariate correlation of (log) population density and different municipality characteristicsbased on a binned scatter plot. To generate the plot, we group population density into equally-sized bins, and computethe mean of each characteristic in each bin.
of the associations or the statistical significance of the correlations. In order to address these issues,
we consider the following empirical specification:
dmp = x′mpδ + w′
mpγ + θp + υmp (3)
where dmp refers to the (log) population density of municipality m in province p.14 xmp is a vector
of geographic and climatic variables for each municipality including temperature, rainfall, average
altitude, ruggedness, soil quality and distance to the coast. These geo-climatic controls are included
non-linearly as in specification (1) above. wmp is the vector of available municipality characteristics
that might characterize low density areas. Finally, a set of province dummies (θp) is included in some
specifications to exploit variation across municipalities within each province.
The vector γ includes our coefficients of interest associated to the different municipality charac-
teristics, namely, 65+ over 16-64 dependency ratio and share of inhabitants born in the municipality
(demographics), the population growth rate between 1951 and 1991 (rural exodus), the share of dis-
content votes in 2019 general elections and increase in the share of votes to regionalist parties between
BANCO DE ESPAÑA 21 DOCUMENTO DE TRABAJO N.º 2028
2007 and 2019 (political variables), public spending per capita and public revenues per capita (fiscal
variables), and share of employment in agriculture and log income per capita (economic variables).
In addition to these variables, we include in all specifications the distance in km to the capital of the
province.
Table 2 shows the estimated coefficients for the model in equation (3). As expected, remoteness
is associated to lower population densities. In particular, a municipality that is 100 km away from
the province capital presents population densities around 1.0-1.9 log points lower. Note that we
control for geo-climatic variables in all regressions but their coefficients are not reported for the sake
of brevity (see Table A.3).
Table 2: Characteristics of low population density municipalities.
(1) (2) (3) (4) (5) (6) (7) (8)Baseline +Demographics +Rural Exodus +Politics +Fiscal +Economic +Fixed Effects +Prices
Distance to capital -0.0195*** -0.0150*** -0.0123*** -0.0121*** -0.0127*** -0.0120*** -0.0103*** -0.0121***(0.00299) (0.00209) (0.00161) (0.00166) (0.00170) (0.00181) (0.00176) (0.00199)
Dependency ratio -0.510*** -0.0192 0.0158 0.0526 -0.0426 0.00598 -0.0619(0.166) (0.106) (0.0962) (0.104) (0.162) (0.170) (0.194)
Share born in mun. -1.656*** -1.327*** -1.282*** -1.294*** -0.925*** -1.055*** -1.156***(0.426) (0.274) (0.265) (0.259) (0.245) (0.219) (0.224)
Pop. growth 1950-1991 0.755*** 0.749*** 0.741*** 0.731*** 0.530*** 0.513***(0.0452) (0.0491) (0.0556) (0.0506) (0.0344) (0.0361)
Regionalist vote growth 0.00324 0.00518 0.00394 -0.0128*** -0.0113**(0.00504) (0.00473) (0.00493) (0.00331) (0.00447)
Share discontent vote 0.00475 0.00724 0.00845 0.0174*** 0.0177***(0.00631) (0.00600) (0.00623) (0.00351) (0.00445)
Public spending pc -0.0267 -0.139* -0.194*** -0.221**(0.0472) (0.0735) (0.0711) (0.101)
Public revenues pc 0.00607 0.0178 0.0530 0.101(0.0440) (0.0534) (0.0411) (0.0652)
Share agriculture -2.187*** -1.705*** -1.923***(0.606) (0.462) (0.590)
Income pc (log) 0.608** 0.445**(0.236) (0.186)
Income pc (PPP, log) -0.0566(0.0337)
Observations 8,007 8,005 7,997 7,995 7,222 6,042 6,042 4,662R-squared 0.551 0.597 0.664 0.665 0.670 0.664 0.726 0.718Geo controls YES YES YES YES YES YES YES YESProvince FE NO NO NO NO NO NO YES YES
Notes: Dependent variable is (log) population density. Geo-climatic controls are not reported but included in allcolumns, namely, temperature, rainfall, average altitude, ruggedness, soil quality and distance to the coast. Standarderrors clustered at the province level. *** p<0.01, ** p<0.05, * p<0.1
Regarding demographic variables added in column (2) of Table 2, we see that low density areas
are associated to ageing populations in which the dependency ratio is higher, and a larger share of
local-born inhabitants. As expected, these patterns indicate that underpopulated areas have received
less immigrants and/or have experienced larger emigration flows in the recent past.
A relevant question related to past migration flows refers to the so-called rural exodus. While
the total number of rural inhabitants in 1940 remained about the same as it had been in 1900,
during the phase from 1950 to 1991 large migration flows from agricultural areas to the leading
industrial regions took place in Spain (see e.g. Collantes and Pinilla, 2011). In column (3), we
include population growth from 1950 to 1991 as a proxy for the rural exodus at the municipality
BANCO DE ESPAÑA 22 DOCUMENTO DE TRABAJO N.º 2028
level. The positive and strong association with population density indicates that underpopulated
municipalities in 2011 are those that experienced population losses during the rural exodus. Indeed,
once we account for this characteristic, the dependency ratio is not statistically significant, which
suggests that young inhabitants leaving rural municipalities during the 1950-1991 period account for
the current dependency ratio of those rural and underpopulated municipalities.
The inclusion of political variables in column (4) indicates that less densely populated municipal-
ities are not associated to higher prevalence of discontent parties, namely, VOX and Podemos, which
is more prevalent in high-density and urban areas. Note that the regressor is the share of discontent
vote in November 2019 General Elections, which is exactly the same to the increase in discontent
vote since 2007, given that the share of Podemos and VOX was zero in all municipalities in that year.
The increase in the support to regionalist parties between 2007 and 2019 is not associated either to
low density areas. It has increased in low density municipalities within each province (see column
7).
Turning to fiscal variables in column (5) of Table 2, the estimates suggest that low-density areas
are characterized by higher spending per capita in the local budgets. Note however, that the asso-
ciation with public revenue per capita is much weaker. These estimates suggest that, if anything,
underpopulated areas in Spain are net recipients of public funds through local budgets. In any event,
note that this finding must be interpreted with a grain of salt since public expenditures and revenues
here do not include the components from regional and central government transfers.
In terms of economic characteristics included in column (6), low-density areas in Spain belong to
municipalities characterized by higher shares of population employed in agriculture as well as lower
average income per capita (note that the number of observations is lower when including income per
capita since this information from INE is only available for municipalities above 100 inhabitants).
These associations are highly significant and robust to the inclusion of province fixed effects in column
(7) of Table 2.
In column (8), we re-estimate the specification in (7) but substituting nominal income per capita
by PPP-adjusted income per capita. In particular, we normalize nominal income by an index of
housing prices available at the municipality level. In particular, we use the census micro-data on
real estate transactions provided by the Spanish Ownership Registry (Registro de la Propiedad) to
the Banco de Espana since 2004. Interestingly enough, the positive and significant association turns
negative and significant! Despite a note of caution is in place since the purchasing power parity
(PPP) adjustment considered here refers only to housing prices, this finding suggests that in PPP
terms, inhabitants of low-density areas present indeed higher incomes or at least, they do not present
significantly lower incomes. Finally, it is worth noting that the number of observations is lower than
in column (7) because housing prices data is only available for municipalities with more than 25
mortgages in the year 2011 in order to ensure good quality of the price index. Still, if we re-estimate
the specification with nominal income per capita in column (7) for the subsample in column (8) the
estimates remain virtually unaltered.
BANCO DE ESPAÑA 23 DOCUMENTO DE TRABAJO N.º 2028
Column (1) in Table 3 refers to the baseline specification from column (6) in Table 2. Low
density municipalities are associated to lower shares of discontent vote (Podemos and VOX) and
higher increases in the regionalist vote between 2007 and 2019. Column (2) shows that these areas
are also associated to higher prevalence of regionalism voting in levels. Interestingly enough, this
bias towards regionalist parties in low density municipalities is robust to the exclusion of Catalan
15Note that voting information refers to the 2019 General Election and the changes with respect to to the 2007election.
In order to gauge the relative strength of the different statistical associations reported in Table 2,
we also estimate the corresponding partial correlations between population density and each regressor
from column (7) by standardizing the variables. These figures are -0.15 for the distance to the
province capital; -0.001 and -0.11 for the dependency ratio and the share of local-born inhabitants;
0.25 for population growth during the rural exodus (1950-1991); -0.09 and 0.10 for the increase in
regionalist vote and the share of discontent vote; -0.10 and 0.03 for public spending and public revenue
per capita; -0.06 and 0.05 for the share of agriculture employment and nominal income per capita. We
also decomposed the goodness-of-fit (R2) of the model in column (7) into contributions of individual
regressor variables (see e.g. Chevan and Sutherland, 1991). After accounting for geo-climatic factors
and province fixed effects, the highest contribution to the overall R2 comes from 1950-1991 population
growth (32%), distance to the capital (18%), the share of local-born inhabitants (15%), the share of
discontent vote (9%), and the agriculture employment share (8%). The remaining regressors jointly
account for around 15% of the overall R2.
All in all, the strongest associations arise between population density and 1950-1991 population
growth (+), distance to the province capital (-), the share of local-born inhabitants (-), the share of
discontent vote (+), and the share of employment in agriculture (-). Needless to say, these estimates
must be interpreted with caution since they refer to mere correlations. While these correlations are
useful for characterizing low density areas in Spain, we acknowledge that identifying the direction of
causality is beyond the scope of the present paper.
Table 3 presents specifications further exploring the voting patterns in low-density municipali-
ties.15 The process of urbanization and globalization have manifested political discontent in rural
and low-density areas of countries such as the US or the UK, which gives rise to populism reactions
in the so-called revenge of the places that don’t matter (see Rodrıguez-Pose 2018).
municipalities from the sample as shown in columns (3) and (4).
Column (5) indicates that the urban bias of discontent vote in Spain is mainly driven by left-wing
parties (Podemos) while the association between right-wing discontent vote (VOX) and population
density is not statistically significant. Note that both Podemos and VOX were not present in the
2007 General Election so that including their voting shares in levels is equivalent to changes from
2007 to 2019.
Finally, in columns (6) and (7) we include an index of vote fragmentation (as in Sanz, Sole-Olle
and Sorribas-Navarro 2019) both in levels and 2007-2019 changes. On the one hand, there is no
BANCO DE ESPAÑA 24 DOCUMENTO DE TRABAJO N.º 2028
Table 3: Characteristics of low population density municipalities — Political variables.
(1) (2) (3) (4) (5) (6) (7)Baseline Regional Without Without Left-right Fragmentation Fragmentation
specification vote Catalonia Catalonia discontent index increase
Distance to capital -0.0103*** -0.0103*** -0.00948*** -0.00942*** -0.0104*** -0.0103*** -0.0103***(0.00176) (0.00177) (0.00181) (0.00181) (0.00175) (0.00176) (0.00176)
Dependency ratio 0.00598 -0.00170 0.0767 0.0732 0.0267 -0.00416 0.00673(0.170) (0.168) (0.164) (0.167) (0.170) (0.168) (0.170)
Share born in mun. -1.055*** -1.042*** -1.109*** -1.108*** -1.098*** -1.025*** -1.058***(0.219) (0.221) (0.254) (0.257) (0.220) (0.222) (0.220)
Pop. growth 1950-1991 0.530*** 0.535*** 0.539*** 0.538*** 0.530*** 0.533*** 0.533***(0.0344) (0.0370) (0.0390) (0.0408) (0.0343) (0.0350) (0.0351)
Regionalist vote growth -0.0128*** -0.0116** -0.0119*** -0.0131***(0.00331) (0.00445) (0.00347) (0.00351)
Share discontent vote 0.0174*** 0.0173*** 0.0184*** 0.0186*** 0.0152*** 0.0179***(0.00351) (0.00362) (0.00352) (0.00363) (0.00324) (0.00346)
Public spending pc -0.194*** -0.187** -0.237** -0.232** -0.199*** -0.196*** -0.194***(0.0711) (0.0714) (0.0929) (0.0928) (0.0701) (0.0711) (0.0714)
Public revenues pc 0.0530 0.0494 0.0715 0.0677 0.0549 0.0535 0.0523(0.0411) (0.0411) (0.0565) (0.0563) (0.0401) (0.0413) (0.0413)
Share agriculture -1.705*** -1.676*** -1.984*** -1.976*** -1.565*** -1.685*** -1.721***(0.462) (0.470) (0.468) (0.471) (0.455) (0.457) (0.450)
Income pc (log) 0.445** 0.460** 0.318 0.318 0.449** 0.431** 0.445**(0.186) (0.195) (0.197) (0.201) (0.182) (0.190) (0.186)
Share regionalist vote -0.00470 -0.00118(0.00378) (0.00235)
Share VOX 0.00419(0.00463)
Share Podemos 0.0270***(0.00373)
Fragmentation 0.0430(0.0327)
Fragmentation growth -0.0139(0.0319)
Observations 6,042 6,042 5,169 5,169 6,042 6,041 6,041R-squared 0.726 0.725 0.690 0.689 0.728 0.726 0.726Geo controls YES YES YES YES YES YES YESProvince FE YES YES YES YES YES YES YES
Notes: Dependent variable is (log) population density. Geo-climatic controls are not reported but included in allcolumns, namely, temperature, rainfall, average altitude, ruggedness, soil quality and distance to the coast. Standarderrors clustered at the province level. *** p<0.01, ** p<0.05, * p<0.1, + <0.15.
statistical association between population density and political fragmentation in levels. On the other
hand, the increase in political fragmentation is indeed lower in low-density areas, which is related
to the rise of the discontent vote (VOX and Podemos) more concentrated in urban areas. All in
all, there is no clear evidence on any association between low density areas and the rise of political
fragmentation through discontent vote. Still, we find some evidence that the rise in regionalist vote
between 2007 and 2019 might have been stronger in low-density municipalities.
Table 4 includes two additional controls: (log) area in km2 as well as a rural dummy for mu-
nicipalities with less than 10,000 inhabitants, which is a commonly-used threshold in Spain to label
rural areas (see Collantes and Pinilla 2011). The relationship between population density and these
two indicators is somehow tautological, but it may well be that some of the characteristics explored
above are related to these variables rather than to (log) population density per se. For instance,
BANCO DE ESPAÑA 25 DOCUMENTO DE TRABAJO N.º 2028
16Also, Table B.4 in Appendix B.4 shows the estimates for population concentration.
public spending per capita or the share of employment in agriculture may be higher in low-density
municipalities because they present larger areas or they are smaller in terms of population size.
According to the results in Table 4, the sign, magnitude, and statistical significance of all the
estimates in our baseline Table 2 remain robust when we control for land area and population size.
Also, not surprisingly, we observe that rural and larger municipalities in terms of surface generally
belong to low density zones within Spain. In particular, rural municipalities with less than 10,000
inhabitants present 0.4-1.3 log points lower density on average, which represents around one standard
deviation of the dependent variable (see Table A.2); also, since both variables are expressed in logs,
the estimates in Table 4 indicate that a 1 percent increase in land area is associated to a reduction
of around -0.4% in population density.
Table 5 shows the analogous estimates for settlement density at the municipality level as the
dependent variable. All the specifications are analogous to those of Table 2 but we simply replace
the dependent variable.16
While the statistical association with the share of local-born inhabitants remains strong and
significant, the one between settlement density and the dependency ratio is positive in general.
Table 4: Characteristics of low population density municipalities — Land area and population.
(1) (2) (3) (4) (5) (6) (7) (8)Baseline +Demographics +Rural Exodus +Politics +Fiscal +Economic +Fixed Effects +Prices
Distance to capital -0.0155*** -0.0121*** -0.0106*** -0.0103*** -0.0107*** -0.0106*** -0.00943*** -0.0110***(0.00225) (0.00172) (0.00139) (0.00142) (0.00144) (0.00151) (0.00165) (0.00189)
Area (log) -0.436*** -0.410*** -0.395*** -0.400*** -0.427*** -0.448*** -0.383*** -0.415***(0.0487) (0.0463) (0.0434) (0.0436) (0.0378) (0.0363) (0.0393) (0.0388)
Rural -1.281*** -1.155*** -0.489*** -0.476*** -0.495*** -0.421*** -0.402*** -0.390***(0.162) (0.128) (0.0847) (0.0888) (0.0870) (0.0800) (0.0734) (0.0733)
Dependency ratio -0.734*** -0.360*** -0.309*** -0.287*** -0.499*** -0.376** -0.525***(0.148) (0.101) (0.0909) (0.0930) (0.144) (0.148) (0.172)
Share born in mun. -0.879** -0.561** -0.519* -0.514** -0.0683 -0.385* -0.357*(0.374) (0.267) (0.272) (0.247) (0.230) (0.198) (0.201)
Pop. growth 1950-1991 0.696*** 0.690*** 0.663*** 0.633*** 0.451*** 0.429***(0.0437) (0.0432) (0.0475) (0.0444) (0.0294) (0.0370)
Regionalist vote growth 0.00278 0.00505 0.00336 -0.0127*** -0.0117***(0.00486) (0.00423) (0.00456) (0.00318) (0.00398)
Share discontent vote 0.00715 0.0100* 0.0100* 0.0184*** 0.0174***(0.00618) (0.00545) (0.00587) (0.00314) (0.00388)
Public spending pc -0.0585 -0.152** -0.184** -0.205**(0.0452) (0.0705) (0.0691) (0.0955)
Public revenues pc -0.00610 0.0176 0.0476 0.103(0.0376) (0.0514) (0.0420) (0.0634)
Share agriculture -1.963*** -1.528*** -1.683***(0.519) (0.448) (0.560)
Income pc (log) 0.443** 0.350**(0.213) (0.173)
Income pc (PPP, log) -0.0875***(0.0315)
Observations 8,002 8,000 7,997 7,995 7,222 6,042 6,042 4,662R-squared 0.627 0.657 0.704 0.705 0.714 0.712 0.756 0.753Geo controls YES YES YES YES YES YES YES YESProvince FE NO NO NO NO NO NO YES YES
Notes: Dependent variable is (log) population density. Geo-climatic controls are not reported but included in allcolumns, namely, temperature, rainfall, average altitude, ruggedness, soil quality and distance to the coast. Standarderrors clustered at the province level. *** p<0.01, ** p<0.05, * p<0.1
BANCO DE ESPAÑA 26 DOCUMENTO DE TRABAJO N.º 2028
Note that the association between the dependency ratio and population density in Table 2 became
insignificant once we accounted for population growth during the 1950-1991 rural exodus in Spain.
Regarding the political indicators, we observe a positive (negative) relationship between settlement
density and the share of discontent vote (increase in regionalist vote) once we include province
dummies in columns (6) and (7).
Turning to the fiscal characteristics of areas with low settlement density, we observe the same
pattern as in Table 2: once we control for socio-economic characteristics and/or province fixed
effects in columns (5) and (6), low settlement density areas belong to municipalities with higher
public spending per capita but not higher public revenues per capita. Indeed, the partial correlation
between settlement density and public spending per capita is -0.15 and statistically significant while
that of public revenues is +0.04 but insignificant.
Finally, looking at columns (6) and (7) of Table 5, we conclude that lower settlement density
is also associated to higher agriculture employment and lower income per capita in nominal terms.
Table 5: Characteristics of low settlement density municipalities.
(1) (2) (3) (4) (5) (6) (7) (8)Baseline +Demographics +Rural Exodus +Politics +Fiscal +Economic +Fixed Effects +Prices
Distance to capital -0.238*** -0.202*** -0.185*** -0.195*** -0.195*** -0.169*** -0.0821** -0.0935**(0.0364) (0.0342) (0.0333) (0.0341) (0.0347) (0.0384) (0.0351) (0.0389)
Dependency ratio 5.228** 8.175*** 6.585*** 8.182*** 12.64*** 7.269*** 7.111**(2.314) (2.469) (2.427) (2.435) (3.413) (2.543) (3.435)
Share born in mun. -27.60*** -25.71*** -24.48*** -25.50*** -23.16*** -26.33*** -28.64***(6.075) (5.525) (5.042) (4.707) (4.660) (3.624) (3.908)
Pop. growth 1950-1991 4.530*** 4.804*** 4.316*** 4.489*** 1.747** 1.961**(0.812) (0.766) (0.765) (0.746) (0.739) (0.732)
Regionalist vote growth 0.0443 0.0883 0.0304 -0.108* -0.0769(0.128) (0.115) (0.120) (0.0551) (0.0719)
Share discontent vote -0.194 -0.155 -0.142 0.119** 0.131*(0.122) (0.106) (0.120) (0.0539) (0.0693)
Public spending pc -0.520 -3.467*** -2.864*** -2.315**(0.889) (1.188) (0.924) (1.140)
Public revenues pc -0.898 3.29e-05 0.971 0.776(0.640) (0.957) (0.676) (0.748)
Share agriculture -24.12** -11.21 -18.36**(10.54) (6.757) (8.534)
Income pc (log) 14.99*** 6.815**(4.225) (2.899)
Income pc (PPP, log) -0.140(0.423)
Observations 8,007 8,005 7,997 7,995 7,222 6,042 6,042 4,662R-squared 0.528 0.555 0.567 0.573 0.588 0.618 0.750 0.764Geo controls YES YES YES YES YES YES YES YESProvince FE NO NO NO NO NO NO YES YES
Notes: Dependent variable is the settlement density indicator. Geo-climatic controls are not reported but includedin all columns, namely, temperature, rainfall, average altitude, ruggedness, soil quality and distance to the coast.Standard errors clustered at the province level. *** p<0.01, ** p<0.05, * p<0.1
However, if we consider PPP-adjusted income per capita with the only price index available at
the municipality level (i.e. housing prices), the association between settlement density and average
income becomes negative but not significant.
BANCO DE ESPAÑA 27 DOCUMENTO DE TRABAJO N.º 2028
In sum, the main characteristics of low population density municipalities identified from the
analysis in Table 2 also correspond to low settlement density municipalities.
5 Concluding remarks
Using the GEOSTAT population grid with information at the 1-km2 level, this article uncovers an
anomaly in the Spanish distribution of population across the space. Compared to other European
countries, Spain presents the lowest density of settlements (almost 90% of its territory is uninhabited!)
together with the highest population concentration in certain areas. Interestingly enough, these
patterns remain true even after accounting for geographical and climatic conditions. The article also
identifies the areas with the lowest densities within Spain and characterize the municipalities that
17In related projects, Gutierrez et al. (2020) analyze in detail the evolution over the last decades of rural-urbanmigrations in Spain and Budı (2020) explores the macroeconomic consequences of such migration flows.
belong to these virtually uninhabited parcels of land.
Economies of agglomeration rely on increasing returns to scale and lower transportation costs,
and emphasize linkages between firms and suppliers as well as between firms and consumers. The
emptiness of the Spanish territory may thus influence the distribution of economic activity across
firms, sectors and space. The dynamic dimension related to the interaction between internal migra-
tion flows and the process of structural change of the Spanish economy over the last decades may
also shape current economic outcomes.17 Understanding the economic consequences of the Spanish
anomaly in settlement patterns represents an exciting line of open research.
BANCO DE ESPAÑA 28 DOCUMENTO DE TRABAJO N.º 2028
References
[1] Allen, T., and C. Arkolakis (2014) Trade and the Topography of the Spatial Economy. The
Quarterly Journal of Economics, 129(3), 1085-1140.
[2] Allen, T., and D. Donaldson (2018) The geography of path dependence. Mimeo.
[3] Bishko, C. (1975) The Spanish and Portuguese Reconquest 1095-1492, in Kenneth M. Setton
(ed.) A History of the Crusades, Vol. 3: 396-456.
[4] Brenan, G. (1950) The Spanish Labyrinth: An Account of the Social and Political Background
of the Spanish Civil War. Cambridge University Press, Cambridge.
[5] Budı, T. (2020) Rural Depopulation, Urbanization, and Structural Change. CEMFI Master The-
sis.
[6] Chevan, A., and M. Sutherland (1991) Hierarchical partitioning. American Statistician, 45, 90–96.
[7] Duranton, G., and D. Puga (2020) The economics of urban density. NBER WP 27215.
[8] Duranton, G., and M. Turner (2018) Urban form and driving: Evidence from US cities. Journal
of Urban Economics, 108, 170-191.
[9] Collantes, F. and V. Pinilla (2011) Peaceful Surrender: The Depopulation of Rural Spain in the
Twentieth Century. Cambridge Scholars Publishing.
[10] Eurostat (2016) GEOSTAT 2011 population grid. Available at http://ec.europa.eu/eurostat/.
[11] Fischer, G., F. Nachtergaele, S. Prieler, H. van Velthuizen, L. Verelst, and D. Wiberg (2008)
Global agro-ecological zones assessment for agriculture, IIASA, Laxenburg, Austria and FAO,
Rome, Italy.
[12] Getis, A. and J. Ord (1992) The analysis of spatial association by use of distance statistics.
Geographical Analysis, 24, 189–206.
[13] Gonzalez-Jimenez, M. (1992) Frontier and Settlement in the Kingdom of Castile (1085-1350). In
Medieval Frontier Societies, edited by Bartlett Robert, MacKay Angus. Clarendon Press, Oxford,
pp. 49-74.
[14] Gutierrez, E., E. Moral-Benito, and R. Ramos (2020) Tendencias recientes de la poblacion en
areas rurales y urbanas en Espana. Mimeo.
[15] Harari, M. (2016) Cities in bad shape: Urban geometry in India. Processed, Wharton School of
the University of Pennsylvania.
BANCO DE ESPAÑA 29 DOCUMENTO DE TRABAJO N.º 2028
[16] Hijmans, R., S. Cameron, J. Parra, P. Jones, and A. Jarvis (2005) Very high resolution interpo-
lated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.
[17] Krugman, P. (1991) Increasing returns and economic geography. Journal of Political Economy,
99, 483-499.
[18] Oto-Peralıas, D. (2020) Frontiers, warfare and economic geography: The case of Spain. Journal
of Development Economics, 146, 1-19.
[19] Oto-Peralıas, D. and D. Romero-Avila (2016) The Economic Consequences of the Spanish Re-
conquest: The Long-term Effects of Medieval Conquest and Colonization. Journal of Economic
Growth, 21, 409-464
[20] Rae, A. (2018) Think your country is crowded? These maps reveal the truth about population
density across Europe, The Conversation, January 23, 2018.
[21] Rodrıguez-Pose, A. (2018) The revenge of the places that don’t matter (and what to do about
it). Cambridge Journal of Regions, Economy and Society, 11, 189-209.
[22] Sanz, C., A. Sole-Olle and P. Sorribas-Navarro (2019) Betrayed by the Elites: How Corruption
Amplifies the Political Effects of Recessions, mimeo.
[23] Sole-Olle, A. and E. Viladecans-Marsal (2012) Lobbying, political competition, and local land
supply: recent evidence from Spain. Journal of Public economics, 96, 10-19.
BANCO DE ESPAÑA 30 DOCUMENTO DE TRABAJO N.º 2028
A Additional figures and tables
Table A.1: Descriptive statistics at the grid-cell level.
Europe Spain
(1) (2) (3) (4) (5) (6) (7) (8)mean p25 p50 p75 mean p25 p50 p75
Settlement density 72.34 50.00 88.00 100 45.00 24.00 44.00 68.00Pop. concentration 38.00 20.50 31.14 49.27 61.41 44.21 60.58 79.70Pop. density 114.6 5.545 29.05 87.65 133.3 4.920 14.12 51.68Temperature 8.124 5.436 8.505 11.03 13.25 11.41 13.40 15.53Rainfall 7.741 5.842 6.819 8.772 6.222 4.567 5.452 6.953Altitude 371.0 96.75 222.1 516.9 652.7 334.6 651.2 885.0Ruggedness 87.40 17.69 46.76 121.0 109.8 46.63 85.42 145.0Soil quality 8.726 8.429 9 9.429 8.905 8.429 8.934 9.357Dist. coast 134.6 20.02 89.79 205.8 119.4 40.95 109.4 183.6
Table A.2: Descriptive statistics at the municipality level.
(1) (2) (3) (4) (5)VARIABLE N mean p10 p90 sd
Settlement density 8,023 55.20 24 91.67 23.85Pop. concentration 8,024 54.37 32.63 77.72 17.26Pop. density (log) 8,024 3.064 1.204 5.389 1.656Regionalist vote growth 8,108 3.105 -5.051 22.74 12.04Share discontent vote 8,131 18.26 3.823 30.22 9.420Distance to capital 8,113 44.14 15.95 76.41 24.38Share agriculture 7,203 0.0715 0.00786 0.155 0.0604Dependency ratio 8,116 0.505 0.212 0.893 0.312Share born in mun. 8,114 0.466 0.216 0.694 0.177Population growth 1950-1991 8,104 -0.547 -1.371 0.414 0.777Public spending pc 7,338 1.222 0.652 1.938 0.841Public revenues pc 7,338 1.307 0.701 2.101 0.897Income pc (PPP, log) 5,163 10.26 9.655 10.94 0.557Income pc (log) 6,745 9.197 8.924 9.467 0.208
BANCO DE ESPAÑA 31 DOCUMENTO DE TRABAJO N.º 2028
Notes: Standard errors clustered at the country level. *** p<0.01, ** p<0.05,* p<0.1
Table A.3: The role of geography and climate in population patterns.
(1) (2) (3) (4)Sett. density Pop. density Sett. density Pop. density
Temperature pt2 16.27** 0.891*** 18.37*** 0.733(5.445) (0.250) (4.977) (0.519)
Temperature pt3 21.13*** 1.258*** 21.07*** 0.644(5.920) (0.193) (5.666) (0.661)
Temperature pt4 18.47*** 1.667*** 11.18 0.0151(5.647) (0.226) (8.393) (0.896)
Rainfall pt2 5.312*** 0.294 1.840 0.0726(1.666) (0.293) (2.424) (0.160)
Rainfall pt3 10.16*** 0.482 4.346 0.0786(1.987) (0.270) (3.760) (0.296)
Rainfall pt4 9.210** 0.418 2.685 -0.502(2.971) (0.250) (4.094) (0.329)
Elevation pt2 -1.898 -0.443** 3.958* -0.271**(1.448) (0.144) (1.982) (0.130)
Elevation pt3 -8.216*** -1.313*** -3.408 -1.120***(2.592) (0.199) (5.353) (0.406)
Elevation pt4 -11.81*** -1.719*** -19.41** -3.098***(2.665) (0.221) (8.857) (0.825)
Ruggedness pt2 3.419** 0.318*** 5.502*** 0.455***(1.179) (0.0437) (1.289) (0.0647)
Ruggedness pt3 5.047*** 0.427** 6.748** 0.694***(0.714) (0.149) (2.666) (0.206)
Ruggedness pt4 4.423 0.575*** 7.821* 1.366**(2.645) (0.154) (4.443) (0.545)
Soil quality pt2 5.256* 0.208* 7.906*** 0.585***(2.444) (0.0966) (2.080) (0.202)
Soil quality pt3 7.071** 0.288** 9.066*** 0.566**(2.903) (0.120) (2.412) (0.270)
Soil quality pt4 7.562** 0.388** 9.869*** 0.667*(2.862) (0.127) (3.060) (0.341)
Dist coast pt2 -2.475 -0.339* -10.12*** -1.221***(2.811) (0.183) (1.504) (0.193)
Dist coast pt3 -8.852 -0.466 -10.57*** -0.946***(7.091) (0.351) (2.735) (0.175)
Dist coast pt4 -7.531 -0.114 -9.175*** -0.738***(5.606) (0.273) (3.264) (0.226)
Island -1.631 -0.572*** 2.593 -0.289(2.852) (0.140) (2.866) (0.265)
Latitude 26.59*** 1.472*** 18.39*** 0.975***(3.055) (0.169) (4.973) (0.207)
Longitude 1.475 0.111 0.152 0.111(2.071) (0.164) (1.344) (0.109)
Latitude2 -0.278*** -0.0163*** -0.192*** -0.0111***(0.0322) (0.00161) (0.0468) (0.00228)
Longitude2 -0.00970 -0.00493*** -0.0230 -0.00320*(0.0272) (0.00155) (0.0212) (0.00168)
Latitude longitude -0.0143 -0.000385 0.0237 -0.000317(0.0465) (0.00323) (0.0292) (0.00240)
Observations 10,850 10,854 21,172 21,190R-squared 0.697 0.585 0.720 0.681Sample Eurozone Eurozone Europe Europe
BANCO DE ESPAÑA 32 DOCUMENTO DE TRABAJO N.º 2028
Figure A.2: Cumulative distribution of the difference between the coefficient on Spain and on eachvirtual country from 1,000 regressions.
0.2
.4.6
.81
Cum
ulat
ive
dist
ribut
ion
10 20 30 40 50Difference between the Placebo coefficient and the ’true’ coefficient
Settlement density0
.2.4
.6.8
1C
umul
ativ
e di
strib
utio
n
−1 0 1 2Difference between the Placebo coefficient and the ’true’ coefficient
log Population density
Figure A.1: The Spanish anomaly in settlement density (Europe).
ALATBEBGBI
CHCZDEDKEEELFI
HRHUIEISIT
KOLI
LTLULVMENLNOPLPTROSESI
SKSRUKES
−100 0 100 200−100 0 100 200
Baseline Geo−climatic controlsALATBEBGBI
CHCZDEDKEEELFI
HRHUIEISIT
KOLI
LTLULVMENLNOPLPTROSESI
SKSRUKES
−10 −5 0 5 10 −10 −5 0 5 10
Baseline Geo−climatic controls
Figure A.3: Hotspot analysis for geo-climatic variables.
BANCO DE ESPAÑA 33 DOCUMENTO DE TRABAJO N.º 2028
Figure A.4: Hotspot analysis (20 km).
Figure A.5: Municipality characteristics.
BANCO DE ESPAÑA 34 DOCUMENTO DE TRABAJO N.º 2028
B Population concentration results
Alternatively to settlement density and population density, we also follow Oto-Peralıas (2020) and
consider an indicator of population concentration that measures the percentage of the population
living in the most populated one percent of the territory.
Figure B.6: The Spanish anomaly in population concentration.
AT
BE
DE
FI
IE
IT
LU
NL
PT
UK
ES
−50 0 50 −50 0 50
Baseline Geo−climatic controlsALATBEBGBI
CHCZDEDKEEELFI
HRHUIEISIT
KOLI
LTLULVMENLNOPLPTROSESI
SKSRUKES
−100 −50 0 50 −100 −50 0 50
Baseline Geo−climatic controls
Figure B.7: The Spanish anomaly in population concentration by region.
ALB0
ALB1
ALB2ALB3ALB4
ALB5ALB6
ALB7
ALB8
ALB9
ALB99
AT111
AT112
AT113AT121AT122
AT123AT124
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AT126AT127
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AT211AT212
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AT222AT223
AT224AT225
AT226
AT311AT312
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AT321AT322
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AT331AT332
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AT341AT342
BE100
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BE212BE213
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BE223BE231BE232
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BE234BE235BE236BE241BE242BE251BE252
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CH032CH033CH040
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CZ051CZ052CZ053CZ063CZ064CZ071
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DEG0HDEG0IDEG0JDEG0KDEG0LDEG0MDEG0P
DK011
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DK032DK041DK042
DK050
EE001EE004EE006
EE007
EE008
EL111
EL112EL113
EL114
EL115EL121
EL122
EL123EL124EL125EL126
EL127EL131EL132
EL133
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EL141
EL142
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EL144
EL211
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EL213EL214
EL221
EL222EL223EL224 EL231
EL232
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EL241
EL242
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EL245EL251
EL252EL253
EL254
EL255 EL300EL411EL412
EL413
EL421
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EL431
EL432
EL433EL434FI193
FI194FI195
FI196FI197FI1B1FI1C1
FI1C2FI1C3
FI1C4FI1C5FI1D1
FI1D2
FI1D3FI1D4
FI1D5FI1D6
FI1D7
FI200
FR103FR104
FR105FR106FR107
FR108
FR211FR212
FR213FR214
FR221FR222FR223FR231FR232
FR241FR242
FR243
FR244
FR245FR246FR251
FR252
FR253FR261
FR262FR263
FR264
FR301FR302
FR411
FR412
FR413FR414FR421FR422
FR431FR432FR433
FR434 FR511FR512
FR513FR514FR515FR521FR522FR523FR524FR531FR532FR533FR534
FR611
FR612
FR613
FR614
FR615FR621FR622
FR623FR624
FR625
FR626
FR627FR628
FR631FR632FR633FR711FR712
FR713FR714
FR715FR716
FR717
FR718 FR721FR722
FR723FR724
FR811
FR812
FR813FR814
FR815FR821FR822FR823
FR824FR825FR826
FR831
FR832HR031
HR032HR033
HR034HR035
HR036HR037
HR041
HR042
HR043
HR044
HR045HR046
HR047
HR048HR049
HR04AHR04BHR04C
HR04DHR04E
HU101
HU102
HU211HU212
HU213HU221HU222HU223
HU231
HU232HU233
HU311HU312HU313
HU321HU322
HU323
HU331HU332HU333
IE011IE012IE013IE021 IE022
IE023IE024IE025
IS001
IS002
ITC11ITC12
ITC13
ITC14
ITC15
ITC16
ITC17
ITC18
ITC20
ITC31
ITC32ITC33ITC34
ITC41
ITC42ITC43
ITC44
ITC46ITC47ITC48
ITC49ITC4AITC4B
ITC4C
ITC4D
ITF11
ITF12ITF13ITF14
ITF21ITF22
ITF31ITF32ITF33
ITF34ITF35
ITF43ITF44
ITF45
ITF46
ITF47ITF48ITF51
ITF52
ITF61
ITF62
ITF63ITF64
ITF65
ITG11ITG12
ITG13
ITG14
ITG15ITG16
ITG17ITG18
ITG19 ITG25
ITG26
ITG27ITG28 ITG29
ITG2A
ITG2BITG2C
ITH10
ITH20
ITH31ITH32
ITH33
ITH34
ITH35
ITH36
ITH37
ITH41ITH42
ITH43
ITH44ITH51ITH52
ITH53ITH54ITH55
ITH56
ITH57ITH58
ITH59
ITI11
ITI12ITI13
ITI14
ITI15
ITI16
ITI17
ITI18
ITI19ITI1A
ITI21ITI22ITI31ITI32
ITI33
ITI34ITI35
ITI41
ITI42
ITI43ITI44
ITI45
KOS25KOS26
KOS27KOS28
KOS29KOS30
KOS31
KOS33
KOS34
KOS35
KOS36
KOS37
KOS39
KOS40KOS42
KOS43KOS44
KOS45
KOS46
KOS47
KOS48
KOS49KOS50
KOS51
KOS52
KOS53
LI000LT001
LT002LT003
LT004LT005LT006
LT007LT008LT009LT00ALU000
LV003LV005
LV006
LV007LV008 LV009ME00
NL111
NL112
NL113
NL121
NL122
NL123NL131
NL132NL133NL211NL212
NL213NL221
NL224
NL225
NL226
NL230
NL310
NL321
NL322NL323NL325NL326NL327
NL332
NL333
NL337NL338
NL339
NL33A
NL341
NL342
NL411NL412
NL413
NL414
NL421NL422NL423
NO011
NO012
NO021NO022
NO031
NO032
NO033
NO034 NO041NO042NO043NO051
NO052
NO053
NO061
NO062NO071NO072
NO073
PL113
PL114
PL115PL116PL117PL121PL122
PL127
PL128PL129
PL12A
PL213
PL214
PL215PL216PL217
PL224PL225
PL227PL228
PL229
PL22A
PL22BPL22CPL311PL312PL314PL315
PL323PL324
PL325
PL326PL331PL332
PL343PL344PL345
PL411
PL414
PL415
PL416
PL417PL418
PL422PL423PL424
PL425PL431PL432
PL514
PL515
PL516PL517PL518
PL521
PL522PL613PL614
PL615PL621PL622PL623
PL631
PL633
PL634PL635
PT111PT112
PT113
PT114
PT115PT116
PT117
PT118 PT150
PT161PT162PT163
PT164PT165
PT166PT167
PT168
PT169
PT16APT16BPT16C
PT171
PT172
PT181PT182PT183
PT184
PT185RO111
RO112RO113
RO114
RO115RO116
RO121
RO122RO123RO124
RO125RO126
RO211
RO212RO213
RO214RO215 RO216
RO221
RO222
RO223
RO224
RO225
RO226RO311
RO312
RO313
RO314
RO315
RO316RO317
RO322
RO411RO412RO413RO414RO415 RO421
RO422
RO423RO424
SE110
SE121 SE122SE123SE124SE125
SE211SE212
SE213
SE214SE221SE224SE231SE232
SE311
SE312
SE313SE321
SE322
SE331
SE332
SI011
SI012
SI013SI014 SI016
SI017
SI018
SI021
SI022SI023
SI024
SK010SK021
SK022 SK023SK031SK032SK041
SK042
SRB54SRB55 SRB56
SRB57SRB58
SRB59
SRB60
SRB61
SRB62SRB63
SRB64SRB65
SRB66SRB67
SRB68SRB90
SRB91SRB92
SRB93
SRB94
SRB95
SRB96SRB97SRB98SRB99
UKC11UKC12
UKC14
UKC21
UKC22UKC23
UKD11UKD12
UKD31UKD32
UKD41
UKD43
UKD61
UKD62UKD63
UKD71UKD72
UKD73
UKD74
UKE12
UKE13
UKE21
UKE22
UKE31
UKE32
UKE41UKE42UKE44UKE45UKF12
UKF13
UKF15
UKF16
UKF22UKF24UKF25
UKF30UKG11
UKG12UKG13UKG21
UKG22
UKG24
UKG31UKG32UKG37
UKH11UKH12UKH13UKH14
UKH23
UKH24
UKH25UKH32
UKH33
UKI12UKI21
UKI22UKI23
UKJ11UKJ12
UKJ13UKJ14
UKJ21
UKJ22
UKJ23
UKJ24
UKJ31
UKJ33
UKJ34
UKJ42
UKK11
UKK12
UKK13
UKK14
UKK15
UKK21
UKK22UKK23
UKK30
UKK42
UKK43 UKL11
UKL12
UKL13UKL14
UKL15UKL16
UKL17UKL18
UKL21
UKL22UKL23
UKL24
UKM21
UKM22UKM23
UKM24
UKM26
UKM27
UKM28
UKM31UKM32
UKM33
UKM34
UKM35
UKM36
UKM37
UKM38UKM50
UKM61
UKM62 UKM63
UKM64
UKM65
UKM66
UKN02
UKN03
UKN04UKN05 ES111ES112
ES113
ES114
ES120ES130ES211
ES212
ES213
ES220
ES230ES241
ES242ES243
ES300
ES411ES412
ES413
ES414ES415ES416
ES417
ES418
ES419
ES42ES4
ES423
ES424ES425
ES43ES432
ES511
ES512
ES513
ES514
ES521
ES522ES523
ES531ES532
ES533ES611
ES612
ES613
ES614
ES6ES616
ES617
ES618
ES620
−20
020
4060
Popu
latio
n co
ncen
tratio
n
−60 −40 −20 0 20 40Population concentration (geo−climatic controls)
Figure B.8: Hotspot analysis for population concentration.
BANCO DE ESPAÑA 35 DOCUMENTO DE TRABAJO N.º 2028
C The Madrid anomaly
Left panel of Figure C.9 shows that, within Spain, all regions at the NUTS2 level have a lower
settlement density than Madrid (the omitted category) after accounting for geography and climate.
Similar pattern at the NUTS3-province level with the exception of Barcelona and Girona that are
not significantly different. Right panel of Figure C.9 shows that this excess of settlement density
in the capital region is not observed in France. Contrarily, it holds for other European countries
(omitted category is always the region capital for Germany, Italy, Portugal...), both using NUTS2
Indeed, we can summarize the cross-country evidence on the capital anomaly by simply doing a
scatter plot with zero and different colors for each country to show that Spanish dots are always to
the left of zero but this is not the case for other countries.
and NUTS3 regions (available under request).
Table B.4: Characteristics of high population concentration municipalities.
(1) (2) (3) (4) (5) (6) (7) (8)Baseline +Demographics +Rural Exodus +Politics +Fiscal +Economic +Fixed Effects +Prices
Distance to capital 0.196*** 0.175*** 0.152*** 0.152*** 0.157*** 0.148*** 0.0778*** 0.0928***(0.0312) (0.0265) (0.0240) (0.0253) (0.0256) (0.0267) (0.0240) (0.0251)
Dependency ratio -2.604 -6.568*** -6.548*** -8.104*** -14.08*** -9.506*** -9.829***(1.960) (2.079) (2.069) (1.981) (2.776) (1.732) (2.308)
Share born in mun. 15.37*** 12.90*** 12.70*** 14.44*** 15.10*** 17.35*** 19.67***(5.417) (4.423) (4.157) (4.100) (4.034) (3.207) (3.486)
Pop. growth 50-91 -6.044*** -6.049*** -5.855*** -6.523*** -4.125*** -3.891***(0.886) (0.895) (0.895) (0.703) (0.409) (0.418)
Regionalist vote growth -0.0122 -0.0197 -0.00777 0.00729 -0.0262(0.0746) (0.0719) (0.0752) (0.0684) (0.0796)
Share discontent vote 0.000941 -0.00516 -0.0583 -0.160*** -0.202***(0.0909) (0.0925) (0.105) (0.0378) (0.0399)
Public spending pc 0.590 1.804 0.751 -0.130(0.683) (1.224) (0.860) (1.156)
Public revenues pc 0.506 0.605 -0.0852 0.708(0.548) (0.858) (0.577) (0.692)
Share agriculture 12.02 6.948 9.974(8.957) (6.906) (7.417)
Income pc (log) -6.560* -3.182(3.545) (2.273)
Income pc (PPP, log) 0.0733(0.454)
Observations 8,007 8,005 7,997 7,995 7,222 6,042 6,042 4,662R-squared 0.354 0.371 0.411 0.411 0.428 0.473 0.618 0.648Geo controls YES YES YES YES YES YES YES YESProvince FE NO NO NO NO NO NO YES YES
Notes: Dependent variable is the population concentration indicator. Geo-climatic controls are not reported butincluded in all columns, namely, temperature, rainfall, average altitude, ruggedness, soil quality and distance to thecoast. Standard errors clustered at the province level. *** p<0.01, ** p<0.05, * p<0.1
BANCO DE ESPAÑA 36 DOCUMENTO DE TRABAJO N.º 2028
Figure C.9: The capital anomaly.
AND
ARA
AST
CANT
CAT
CLM
CVA
CyL
EXT
GAL
MUR
NAV
PV
RIOJ
−50 0 50 −50 0 50
Baseline Geographic controlsFR21FR22FR23FR24FR25FR26FR30FR41FR42FR43FR51FR52FR53FR61FR62FR63FR71FR72FR81FR82FR83
−40 −20 0 20 −40 −20 0 20
Baseline Geographic controls
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E-mail: [email protected]