How far away is the capital city?
Geographical remoteness as an element of the centre-periphery cleavage,
and its impact on individual vote choice
Lorenzo De Sio, European University Institute
([email protected])
Cristiano Vezzoni, University of Trento (Italy)
([email protected])
Paper prepared for the ECPR General Conference - Reykjavik, Iceland, August 2011
Section 66 - Old and New Models in Electoral Research. Comparative Perspectives
Panel 95 - Comparative Studies of Social Structure and Party Choice
Panel Session: 5 - Friday, 26 August, 0900-1040
Panel chair: Oddbjørn Knutsen, Oslo Universitetet I (Norway)
Draft, 10.08.2011
Do not quote without permission of the authors
ABSTRACT:
Operationalizations of the Rokkanian centre-periphery cleavage have traditionally focused on the
presence of specific regional political cultures, as well as on cultural fragmentation within a country
(at the aggregate level) or on proxy indicators such as town size (at the individual level). We
suggest that geographical remoteness from political centres – a key element of the centre-periphery
cleavage in the Rokkanian framework – could provide a more accurate measurement of the
subjective position of individuals in relationship to the cleavage. As new indicators of this concept,
we introduce measures of distance between the place of residence of a citizen and political centres
at different hierarchical levels, based on road distances and travel times along traditional roads (also
accounting for differences in orography and geography among different countries and regions). We
empirically test the impact of such indicators on vote choice on the Italian and French cases. We use
survey data from the 2006 ITANES and the 2002 PEF, and information about road distances and
travel times obtained through online mapping/routing services. We first assess differences between
the two countries in terms of orography and geography. We then estimate multivariate models of
vote choice at the individual level, in order to test the following hypotheses: a) that the new
indicators add significant explanatory power, compared to traditional indicators related to the
centre-periphery cleavage; b) that the new indicators have different effects on vote choice for
different parties, expressing different affinities of these parties with the cleavage.
1
1. Introduction1
The importance of geographical distances for political processes was to our knowledge first
recognized and formulated by the Marquis de Condorcet, which – in reflecting on the institution of
provincial assemblies in the more general framework of an administrative reorganization of the
French kingdom – expressed the view that “it is a real advantage that each community has an
extension such that, during one day, those citizens that are farthest from the centre might reach the
centre, conduct their business for several hours, and then come back to their places”2 (Condorcet
1788, 231). A principle which, though applied to a very limited extent in the actual design of French
departments, is still considered as inspiring their institution.
A little less than two centuries later, Stein Rokkan (1967; 1999) recognized the territorial
dimension as one of the four axes of conflict that structured European party systems, by identifying
the centre-periphery cleavage regarding the territorial redistribution of resources during the
formation of nation-states. The conceptual framework behind the Rokkanian idea of a centreperiphery cleavage was formulated with a considerable degree of sophistication and articulation;
however, we argue that its use in contemporary research on voting behaviour is mostly in two
directions that partly simplify its original articulation and complexity. The first direction is
connected to the recognition of the emergence of local sub-cultures, leading to “regional” parties
that have a strong local concentration in areas where they are often enjoying majority positions;
such parties precisely result from a politicization of the centre-periphery conflict. The second
direction concerns in our view the effect of a generic geographical remoteness (regardless of the
politicization of the centre-periphery dimension), and is often operationalized, e.g. in survey
research analyzing individual voting behaviour, in terms of the size of the city of residence of the
respondent, where small towns would ideally identify more peripheral contexts.
This paper aims to contribute in this second direction, by trying to provide a more
articulated conceptualization, as well as new indicators that in our opinion would provide a more
appropriate operationalization of the aforementioned dimensions. Such indicators are essentially
based on the notion of travel distance, which – compared to the more basic notion of geodesic
distance (commonly known as the “as the crow flies” distance) – takes into account orographic
features of the territory, as well as the presence of natural obstacles to transportation, which result in
1
The authors would like to thank Matteo Cataldi and Moreno Mancosu for their help in phase of collection and
organization of the geographical data used in this paper.
2
“C’est un avantage réel que chaque communauté n ait qu une étendue telle que, dans l espace d’un jour, les citoyens
les plus éloignés du centre puissent se rendre dans le chef lieu, y traiter d affaires pendant plusieurs heures et retourner
chez eux…”.
2
increased travel times in particularly disadvantaged regions, which are then more remote than
others.
The structure of the paper is as follows: in section 2 we outline our conceptual model of the
role of geographical remoteness in a variety of both historical and contemporary political processes
that are connected to the development of political culture at large, including voting behaviour, and
introduce our measure of geographical remoteness based on travel distance, as obtained from online
mapping/itinerary services. We also discuss the distinction between the geometrical and
orographical components of geographical remoteness. The following sections present empirical
analyses based on the two case studies of Italy and France. Section 3 shows the importance of a
distinction between geodesic and travel distance, by showing that the new indicator allows to
highlight the very different territorial structure of the two countries in terms of their geographical
fragmentation, much of which can be explained in orographic more than in geometrical terms.
Section 4 moves on to an analysis at the individual level, by estimating multivariate models of vote
choice for Italy and France, based on the 2002 PEF and the 2006 ITANES electoral surveys. In
particular, we focus on the travel distance of the place of residence of the respondent from a variety
of both political, administrative and modernization centres, and discuss its explanatory power both
in absolute and in comparison with traditional indicators such as town size. We then analyze the
effect of geographical remoteness on vote choice for different parties. A concluding section follows.
2. Geographical remoteness and the centre-periphery divide
Contemporary politics takes place in political systems that, in territorial terms, are clearly
defined and well delimited by explicit boundaries. Such boundaries and territorial definitions are the
results of historical developments that have originated from the emergence of nation-states: a
process that has shown significant heterogeneity across different European countries (Tilly 1975).
Our suggestion is that such process can hardly be thought as not influenced by intrinsic features of
the territorial extension that sees the emergence of a nation-state, especially in terms of travel time
and conditions between the emerging centre and the peripheral regions of the forming state. The
very notion of the importance of travel time, highlighted in the previous citation by Condorcet,
should suggest how important such aspects were perceived, at a time where the emergence of
nation-states across Europe was in a crucial phase. Such processes of development have followed
different historical paths with different outcomes (a heterogeneity in outcomes that will drive our
3
case selection); we argue that the outcomes of this process (also in terms of the features of the
territory that became later a nation-state) are still relevant for contemporary politics.
In operational terms, we suggest that – in trying to analyze how territorial features could
affect the structure of political systems – a useful concept is that of geographical remoteness from a
centre. We essentially define such concept in terms of travel time: the more time is required – with
different travel technologies in different epochs – to travel from one place to the political centre of
its polity, the more such place can be considered geographically remote3. Anticipating a later point,
it is pretty easy to understand how we deem this concept to be more useful than town size in
measuring positions on the centre-periphery cleavage: we expect e.g. the interests of the population
from a small village very close to a country capital to be better represented (and the political culture
of its inhabitants to be much closer to that of the capital) than compared to a village of the same
size, but located in a region lying very far from the capital, perhaps only reachable through long and
difficult mountain roads.
Given the introduction of this concept, we essentially suggest that it might help highlight
two different aspects connected to the centre-periphery divide4. The first aspect derives from the
aggregate measure of geographical remoteness in a political system. On average, how far are most
cities from the capital? How far is the farthest village? How long does it take to affirm a revolution
on the whole territory of a political system? How much time is required to established a full
territorial organization for a newly formed party? How do all these figures vary, in comparing
different political systems? Most of these questions could in our opinion benefit from a single
measure expressing the aggregate amount of geographical remoteness in a political system5: a
measure which would also end up providing a new concept of country size, compared to existing
concepts based on population size or on land size; a concept that in our opinion would express the
potential for the importance and mobilizability of the centre-periphery divide. We think that
geographical dispersion could be an appropriate label for this concept. The second aspect is instead
connected to subjective positions (of individuals, cities, regions) on the centre-periphery divide.
How far is a particular city or region from the capital? How long does it take for representatives of
that city to travel to the capital? How different is the political culture of inhabitants of that city
compared to the capital? Such questions could benefit from subjective measures of geographical
3
The choice of travel time is of course not obvious: different alternatives are offered at least by the geodesic distance
between two places (commonly known as the “as-the-crow-flies” distance) or by the road distance between them. See
below for a brief discussion.
4
We follow Bartolini and Mair (1990) in distinguishing between a divide and a cleavage (a divide which has been
successfully politicized by one or more political actors, and that has then become salient in a political system).
5
Such as e.g. the (geometric) average travel time to municipalities that are part of the political system.
4
remoteness, expressing where a specific actor (an individual, a local leader, a city) is placed on the
centre-periphery divide.
Once such two aspects are introduced, it is worth spending a few words on the potential
mechanisms that could link such aspects to the actual emergence and importance of the centreperiphery divide, and to subjective positions and alignments on it. The core of our argumentation
for the importance of geographical remoteness is essentially connected to the importance of travel
(and, as such, of travel distances and times) for most political processes. Example of such processes
not only obviously involve the construction of the nation-state in terms of a centralization of
resources, but they also include everyday mechanisms of political administration and politics: from
the travel speed of information concerning a revolt (and of the information travelling back in terms
of instructions on how to react), to the possibility of citizens (or subjects) to formulate grievances to
local authority that are easily reachable (vs. not), to the ease of national coordination for activists to
found a new political party, etc.
In this regard, we could expect an objection on the time dimension. It is true that all our
considerations might hold, when trying to analyze the process of the construction of nation-states,
which took place – in most European countries – at times where information, people and goods
travelled at the same speed, and essentially no motorized transportation was available. But one
could argue that contemporary politics takes place under very different transportation technologies:
information can now travel worldwide instantly and en masse; people and goods can travel quickly
through widely available air transportation, so that the travel difficulties that made places remote
are not politically relevant anymore. Our opinion is that such objection has essentially two answers.
The first is connected to historical aspects: we hypothesize that the remoteness of different regions
and cities at the time of formation of the nation-state would have had an initial imprinting on the
geographical redistribution of resources within the polity (the main conflict defining the centreperiphery divide), and that such imprinting would exert a lasting influence through later periods of
time, even if different transportation technologies could remove or change the original remoteness
of a region or city. Also, evolution in travel technology – if exploited uniformly across a country –
would decrease the aggregate level of remoteness, but not change the relative positions of different
cities and regions. If the centre-periphery divide was already activated at the time of the new
technology, its salience would maybe remain mostly unchanged; and specific regions and cities
would remain in the same relative remoteness position, compared to the capital and to each other6.
6
This introduces the crucial question of how uniform is the application of travel technology innovation. There could
also be a perverse mechanism, such that travel innovation (trains, motor highways, high-speed trains) is first introduced
in more central regions, so that it actually ends up relatively increasing, rather than decreasing, the remoteness of
already remote cities and regions.
5
The second answer, instead, does not involve time considerations. We simply argue that many key
processes in contemporary politics travel at not more than the universally available road speed.
Political opinions of citizens change not simply in reaction to the media, but mostly when media
messages interact with local opinion leaders; new parties – especially when ignored by the
mainstream media – often need to develop local organizations in order to become relevant in the
political system; the even increasing importance of local politics (through the emergence of multilevel governance) places a even heavier burden on political parties for coordinating national and
local leaderships, requiring in-person contact and then resulting in frequent travelling, often from
and to places where high-speed transportation is not available. As such, we argue that geographical
remoteness can be helpful in analyzing the centre-periphery divide (both in terms of its aggregate
relevance and salience, and in terms of the position of different actors on it) not only in historical,
but also in contemporary perspective.
3. Data and measurement
Moving to the empirical part of our analysis, a first comment is needed on case selection. In
needing to obtain extensive travel information about a large number of cities for each countries, we
chose to limit as most as possible the number of countries to the minimum of two that can be used
for a comparison. We then chose the two cases of Italy and France. We argue that such selection
can be appropriate for a first test study, for several reasons. First of all, the two countries are similar
on several features. They have roughly the same population size (see below); they share a very
similar system of administrative subdivisions, articulated in the three levels of regions, provinces
(départements) and municipalities7; this is not surprising, as the Italian province were explicitly
modelled on the French départements after the Italian unification. Given such similarities (which
also ease statistical comparability, in terms of number of regions and provinces – though not of
municipalities), the two countries present relevant differences at first sight. First of all, in terms of
our key variable: the presence and amount of geographical remoteness. While having an extension
much larger than Italy’s, France has an almost circular shape that minimizes distances to most
cities, unlike Italy that – having a longer and narrower shape – presents a suboptimal configuration
for the connection of the territory to the centre. Also, in orographic terms, France essentially lacks
internal obstacles such as important mountain ranges, while Italy is not only delimited by the Alps,
but internally divided by the Appennini range that covers the whole continental territory of the
7
While provinces and municipalities exist from the unification of both countries, Regions were introduced only in the
in 1970 in Italy and in 1982 in France.
6
country. These geographical differences are then complemented by and anecdotal observation: that
while France was essentially the first nation-state to unify, and an idealtypical example of an
effectively centralized administration, Italy has been almost the latest large European country to
unify, and has been historically (and even more today) confronted with internal divisions and
tensions. As such, we would expect these two cases to test the ability of our newly introduced
indicators to highlight meaningful variance not only both between the two cases and within them: in
this latter case, by showing different levels of geographical remoteness in different parts of the two
countries.
We then prepared data collection, by defining the array of travel distances and times to be
calculated, in order to appropriately measure the geographical remoteness of different parts of the
two countries. As stated previously, both countries have a largely similar system of administrative
subdivisions: excluding overseas territories for France, each country is divided in regions (20 for
Italy, 22 for France), provinces or départements (110 in Italy, 96 in France), and finally
municipalities or communes (around 8,000 for Italy, around 36,000 for France, with an average size
that is significantly smaller than in Italy). We adopted a mixed strategy: in order to assess a first
degree of geographical dispersion at the aggregate level, we first calculated distances between the
capital and each provincial capital8. At a second stage, we then calculated distances between each
municipality and its provincial capital; such distances can be used, in turn, to characterize each
province in terms of its internal geographical dispersion. Finally, the combination of both distances
allows us to characterize each municipality with the key distances from its local capital and from
the country capital.
The actual measurement of all distances was performed by using online mapping/routing
services, allowing – for each distance – the calculation of the following measures: geodesical (“asthe-crow-flies”) distance; travel distance and travel time by car using controlled-access highways
(motorways) if possible; travel distance and travel time by car, systematically excluding motorways.
Unless otherwise noticed, most of the calculations involve this last distance. We chose this option
as it allows a more precise reproduction of the historical routes that were in use throughout the
state-building processes outlined above, while motorways have provided a transportation
technology that is relatively recent, compared to the scale of the above processes.
8
From now on, the term province will be used for both Italian provinces and French départements.
7
4. Geographical dispersion in France and Italy
We now move on to the first section of empirical finding. This first section is dedicated to
an assessment of geographical dispersion in France and Italy, both at the aggregate level and
separately by province. How far are, on average, cities of these two countries from their country
capital? How far are local municipalities from their local provincial capital?
Regarding this research question, a focus on different indicators is essentially a focus on
different aspects of geographical dispersion. The simple focus on geodesic distances (that ignore
obstacles such as mountain ranges) allows to concentrate first on geometrical differences between
countries: those differences in shape that – holding total surface constant – might provide or prevent
efficient transportation among different regions. In this regard, France is an almost idealtypical case
of a country with a nearly circular shape, providing the concentration of a maximum possible
surface in a minimum radius. Classical examples in the literature usually pit France against
countries such as Chile, Viet Nam or Italy, whose long and narrow shapes only allow the
concentration of a much more limited surface in the same distance from the centre.
Such features are immediately visible as we compare the distributions of capital-province
distances for the two countries. Table 1 reports several summary measures of the around hundred
distances that occur between each country’s capital and all its provincial capitals. Given the nature
of the variables, geometric rather than arithmetic means are always employed. For each row, values
for the two countries are presented in separate columns. For each indicator, the absolute value is
presented, along with a ratio comparing such value with the analogous value in case of geodesic
(“as-the-crow-flies”) distance.
Table 1 - Average travel distances and times (geometric means) between the country capital and
each provincial capital
France (N = 96)
Italy (N = 108)
Absolute Ratio to
value
geodesic
Travel distance (geometric mean), kilometers:
Geodesic
246.7
1.00
Car (motorways)
302.0
1.22
Car (no motorways)
290.6
1.18
Italy/France
ratio
Absolute Ratio to
value
geodesic
294.8
387.1
390.6
1.00
1.31
1.33
1.19
1.28
1.34
Travel time (geometric mean), hours:
a
Geodesic
3.0
3.3
1.00
1.00
1.11
Car (motorways)
3.7
1.22
4.4
1.31
1.19
Car (no motorways)
5.4
1.82
8.0
2.41
1.47
a
Simulated, by hypothesizing the average motorway speed.
b
For technical reasons, the Italian provinces of Lecce and Medio Campidano are not included in
the analysis. As both are very far from Rome, this results in a conservative estimate of the
differences between Italy and France.
8
Probably the most striking value is the one presented in the first row, reporting the average9
geodesic distance between the country capital and provincial capitals. France has a lower value than
Italy: the distance between Paris and the average prefecture10 is slightly below 250 km (246.7),
while the distance between Rome and the average capoluogo di provincia is almost 300 km (294.8).
Such figures become striking if read together with the overall surface of the two countries11: France
has an overall surface of 551,500 sq. kms, while Italy’s surface is of 301,336; in other words,
France is almost twice (83%) as large as Italy; although, its radically different, almost circular
geometry proves enormously more efficient, providing an even lower average distance for reaching
provinces that cover a surface that is almost twice as large.
However, the two countries have very similar demographic sizes (around 63 millions for the
Metropolitan French territory, around 61 millions for Italy) so that we will proceed to a comparison
by essentially ignoring differences in territory size12.
The second and third rows introduce our first innovation in measuring distance, by taking
into account actual road distances rather than geodesical distances. In general, the presence of
natural obstacles clearly shows in terms of a distance penalty: in France, compared to air distances,
distances increase respectively by 22% and 18% when taking into account car travel including
motorways or excluding motorways; the same conditions introduce in Italy a penalty respectively of
31% and 33%: on average, travelling by car from Rome to a provincial capital (not using
motorways) implies a route that is one third longer than the air distance. One aspect that is partly
surprising is the very high (and mostly unexpected) similarity between road distances with or
without motorways: in France, motorways even introduce a slight average increase in distances.
Though unexpected, this finding is clearly explained when travel times are analyzed (see below).
As a last note to this first pane, the last column of the table presents the comparison between
Italy and France in terms of the ratio – in each row – between the absolute values for the two
countries. In geodesic terms, Italy is penalized by 19% compared to France (the 294.8 / 246.7 ratio
is 1.19); when car travel with motorways and finally without motorways is analyzed, such
penalization factor increases respectively to 28% and 34%.
The second pane of the table presents then travel times, expressed in hours, as calculated by
mapping/routing services. Such calculations introduce more thorough assessments of the orography
involved, as routes of the same length might result in much longer travel times in unfavourable
orographic condistions. As in the first pane, the first row starts from geodesic distances. In this case,
9
From now on, all values referred to as “averages” will actually be geometric means, unless otherwise noted.
As the capital of each département is defined.
11
Excluding overseas territories for France.
12
This does not mean that such differences highlight at least radical differences in terms of per capita land resources.
10
9
no service offers the calculation of a travel time as no route exists on the geodesic path: we
simulated this calculation (only for the purpose of comparison with the rows that follow) by
applying the average estimated motorway speed13 to the length of the geodesic path. The second
row presents the actual travel time by using motorways, and the ratio to the geodesic path is
obviously the same as in the top pane, for the aforementioned reasons (the first row being simply a
simulation). On average, travelling from Paris to a provincial capital takes 3.7 hours using
motorways, while travelling from Rome to a provincial capital takes 4.4 hours, with Italy being
penalized by 19%. But the situation radically changes when examining travel without motorways,
and it does so in a much more significant fashion than when only taking into account road distances
as in the top pane. Compared to the geodesic reference, in France travel times increase by 82%
when travelling by car without motorways (vs. a bare 18% increase in travel distance); in Italy such
increase jumps to 141%, with the average time increasing to eight hours. As a result, Italy appears
penalized by 47% in travel times compared to France.
This very significant effect of motorways in terms of travel times clarifies the apparently
counterintuitive finding of the top pane, where motorways introduced little or no differences. The
point here is in travel time: motorways in these two countries do not apparently introduce radical
differences in road distances between cities, but they radically innovate in allowing travel speeds
that are systematically much higher. As a result, the motorway advantage is not visible in distance
terms, but clearly visible in travel time terms. In our opinion, this even emphasizes the importance
of introducing new indicators that not only take into account travel distance (even when amended
with actual road paths), but most importantly travel time. For these reasons, we will mostly refer to
travel time in the remainder of the paper.
The main finding of Table 1 is thus, essentially, that Italy suffers a 47% higher geographical
dispersion than France, when such dispersion is measured as the average road travel time to a
provincial capital. However, such figures are only indicative, as they only consider the presence of a
provincial capital, without taking into account the population of the corresponding province. We
then repeated the analyses by computing averages that are weighted by the population size of each
province (Table 2).
13
Routing services estimate slightly different average motorway travel speeds for Italy and France.
10
Table 2 - Average travel distances and times (geometric means) between the country capital and each provincial
capital. Data weighted by province population size
France
Absolute
value
Travel distance (geometric mean), kilometers:
Geodesic
155.0
190.6
Car (motorways)
Car (no motorways)
183.0
Travel time (geometric mean), hours:
a
Geodesic
Car (motorways)
Car (no motorways)
2.1
2.5
3.7
N
96
a
Italy/France
ratio
Italy
Ratio to
geodesic
Absolute
value
Ratio to
geodesic
1.00
1.23
1.18
186.1
238.5
238.3
1.00
1.28
1.28
1.20
1.25
1.30
1.00
1.23
1.82
2.7
3.4
6.3
1.00
1.28
2.36
1.31
1.36
1.69
104
Simulated, by hypothesizing the average motorway speed.
b
For technical reasons, the Italian provinces of Lecce and Medio Campidano are not included in the analysis. As both
are very far from Rome, this results in a conservative estimate of the differences between Italy and France.
The findings present results that are even stronger than in the previous table. Comparing
only travel times and taking into account the distribution of the population, Italy is now penalized
by 31% already in geodesic terms (it was only 11% when not considering population), with the
penalization increasing to 36% using motorways (vs. 19%) and finally to a striking 69% (vs. 47%
without population) when excluding motorways14. As a summary comment, we could note that we
introduced this paragraph by mentioning that – in terms of land surface – France is 83% larger than
Italy. But when measuring country size in terms of the average travel time to reach one of the about
hundred provincial capitals, it appears that – due to geography and orography, and in historical
roads terms – Italy ends up being 69% larger than France. In our view, this proves the usefulness of
our indicators, which could lead to more informed views of the structural geographical constraints
that affect politics in different countries. In political terms, our opinion is that the centre-periphery
14
We also analyzed travel times from each provincial capital to each municipality, albeit with a more difficult
comparison due to the very different number of municipalities in Italy (around 8000) and in France (around 36000), that
would lead to an overestimation of geographical dispersion in France. We ran separate analyses by first directly
comparing municipalities, resulting in an average travel time (from the municipality to the provincial capital) of 0.82
hours in France and 0.76 hours in Italy. In trying to obtain a more balanced comparison, we then summarized French
data by Canton-Ville (around 3000 cases, rather than 36000), which allowed to emulate for France the larger average
municipality size observed in Italy. This second analysis yielded an average travel time of 0.75 hours in France and 0.76
in Italy. Taking population distribution into account, figures stabilize in both scenarios to 0.69 hours for France and
0.66 for Italy, configuring a penalty by 4.5% for France. These findings are a confirmation of the previous: despite the
much larger average size of French départements and the smaller size of French municipalities (leading to a more
dispersed administrative organization), the more difficult geography and orography of Italy results in average local
travel times that are almost equal: France is locally penalized by 8% if considering the 36000 small municipalities, but
advantaged by 1.3% if aggregating them to the around 3,000 cantons-villes (comparing to the 8000 Italian comuni).
And when finally taking population distribution into account, the penalty for France reduces to around 4.5%.
11
divide could be hypothesized to have a larger extension in Italy than in France, where geographic
dispersion appears lower. We will then proceed to a possible empirical testing of this hypothesis, by
trying to assess the impact of geographical remoteness at the individual level on survey data in the
two countries. We expect the centre-periphery divide to be more politically relevant (being closer to
an actual cleavage) in Italy than in France.
5. Geographical remoteness and voting behaviour in Italy and France
The test of the effect of the center-periphery divide on individual electoral behaviour is run
on Italian and French data. For Italy, we considered the 2006 national election and the data come
from ITANES 2006. They are part of a post-electoral survey, that included new cases and cases
coming from a pre-post electoral panel. The dependent variable is the party vote recall for the low
chamber (Camera dei deputati). For France, we considered the 2002 presidential election and the
data come from PEF 2002, that is a three-wave panel study. As a dependent variable, we took the
vote for the presidential candidate recall after the first round.
After data cleaning, the number of cases are 3373 for Italy and 1790 for France. In the two
countries, there were several choices in terms of either parties or candidates, reduced in both cases
to 9 answer categories. Being so defined the dependent variables, we applied in he following
analysis a multinomial logit model, in both cases with 9 categories, with the non vote as a reference
category. For each country, we run separate models. The following tables summarize the
distribution of the dependent variables in the two countries.
Table 3 – Distribution of the dependent variables in the Italian sample – Itanes 2006
Sample
Code Party
Lable
Leader
1 National Alliance (Alleanza Nazionale)
AN
2 Olive Tree (L'Ulivo)
Sample Election
Freq
%
%
Fini
317
9
12
Ulivo
Prodi
976
29
31
3 Forza Italia
FI
Berlusconi
556
17
24
4 Lega Nord-Movement for Autonomy
LN
Bossi
86
3
5
5 Greens (Verdi)
Verdi
PScanio
101
3
2
6 Communist Refoundation Party (Rif. Comunista)
RC
Bertinotti
219
7
6
7 Union of Christian Democrats (Unione di Centro)
UdC
Casini
157
5
7
8 Other Centre-Left
Other Cl
125
4
9 Non vote - No answer - Other
NV - NA
836
25
3373
100
12
Total
Table 4 – Distribution of the dependent variables in the Italian sample – Itanes 2006
Sample
Code Candidate
Party
Sample Election
Freq
%
%
1 Lionel Jospin
Socialist Party (Parti socialiste)
310
17
16
2 Jacques Chirac
Rally for the Republic (RpR)
280
16
20
3 Jean-Marie Le Pen
National Front (Front national)
158
9
17
4 Noël Mamère
The Greens (Les verts)
116
7
5
5 François Bayrou
Union for French Democracy (UDF)
113
6
7
6 Arlette Laguiller
Workers' Struggle (Lutte ouvrière)
109
6
6
7 Other Left
306
17
13
8 Other Right
122
7
12
9 Non vote - No answer - Other
276
15
1790
100
Total
The expectation is that remoteness has a larger impact in Italy than in France. To evaluate
this hypothesis, we fit a number of model starting from the simplest one, including only remoteness,
and adding successively a number of controls to see in the effects remain. In each country, we fit
therefore 5 nested models, as specified below:
Model 1 includes
Remoteness
Model 2 includes
M1 parameters +
Population
Model 3 includes
M2 parameters +
Geographical controls
Model 4 includes
M3 parameters +
Demografic controls
Model 5 includes
M4 parameters +
Demografic controls
The variables concerning geographical characteristics are expressed at the level of the
municipality, while the other controls are at the individual level. Remoteness is represented by two
variables, as specified above:
-
Travel time from province capital (hours) in its natural logarithm
-
Travel time from country capital (hours) in its natural logarithm
Population is represented by the natural logarithm of the population of the municipality.
The geographical controls are:
-
curviness, expressed as the ratio between the actual distance and the geodesic distance
surface of the municipality in its natural logarithm.
13
The demographic controls are: gender (male), age (years), education (4 categories), class (5
categories), religiosity (3 point scale). The political control is given by the left-right self-placement
of the individual (inputted in the case of missing values to the average of the voters of the party for
which the individual have voted).
The impact of each variable included in a model is evaluated by a likelihood ratio test. These
tests show when the inclusion of a variable in a certain model produces a significant improvement
of the overall goodness of fit. The following tables present the likelihood ratio tests for each
variable in each model.15
Table 5. Likelihood ratio test for Model 1 to Model 5 in Italy
Model
1
ITALY
Model
2
Model
3
Model
4
Parameter
Df
Ln(time from provincial capital)
8
26
***
18
**
13
*
9
Ln(time from country capital)
8
55
***
78
***
80
***
82
Ln(population)
8
19
**
11
Curviness
8
14
Ln(surface)
8
40
Gender
Model
5
delta Sig. delta Sig. delta Sig. delta Sig. delta Sig.
(Chi2)
(Chi2)
(Chi2)
(Chi2)
(Chi2)
8
***
65
***
11
9
*
13
14
*
***
39
***
35
***
8
25
***
20
***
Age
8
31
***
29
***
Religion
8
88
***
40
***
Education
24
79
***
72
***
Class
32
93
***
42
Left-right self placamento
8
2471
***
Legenda: *** p < 0.01; ** 0.01< p < 0.05; * 0.05< p < 0.10
15
The delta(Chi2) is obtained by computing the difference between the -2 log-likelihoods of the actual model and its
reduced form, that is a model including all the parameters except the one attributable to the effect subject to the
likelihood ratio test. The significance of delta(Chi2) is computed on the basis of the number of parameters omitted in
the reduced form of the model. The significance of the test reports the probability of the null hypothesis, namely all
parameters of that effect are 0.
14
Table 6. Likelihood ratio test for Model 1 to Model 5 in France
Model
1
FRANCE
Model
2
Model
3
Parameter
Df
Ln(time from provincial capital)
8
21
Ln(time from country capital)
8
8
Ln(population)
8
Curviness
8
24
Ln(surface)
8
4
Gender
Model
4
Model
5
delta Sig. delta Sig. delta Sig. delta Sig. delta Sig.
(Chi2)
(Chi2)
(Chi2)
(Chi2)
(Chi2)
**
12
12
11
11
9
2
2
2
11
12
11
11
**
24
**
27
**
4
4
8
8
7
Age
8
60
**
51
**
Religion
8
68
**
34
**
Education
24
56
**
51
**
40
60
*
48
Class
16
Left-right self placement
8
530
**
Legenda: *** p < 0.01; ** 0.01< p < 0.05; * 0.05< p < 0.10
The outcome of our tests is pretty clear. In France the two variables concerning remoteness
never bring a significant contribution to the goodness of fit of the models (except in Model 1 where
the distance form the provincial capital is significant at a level of 5%, but this effect disappears
introducing further controls). In Italy, the picture is rather different: both variables concerning
remoteness bring a significant contribution to the goodness of fit of the model 1, 2 and 3, that is in
all the models that include only variables referring to the municipality level. As soon as the
demographic and political controls are introduced, the time distance from the provincial capital
cease to have a significant effect, but the distance from the country capital (Rome) remains
significant (p< 0.01, in all models). This outcome confirm our expectation that remoteness should
have been more relevant in Italy than in France. Intrerestingly, the inclusion alone in the model of
the size of the municipality in terms of population does not eliminate the effect of distance from
neither the provincial capital nor the country capital. This is a further confirmation that our intuition
on the effect of remoteness was correct, and this new variable tapping the centre - periphery divide
produces genuine effects. As far as the other geographical characteristics, in Italy also the surface of
the municipality has a highly significant effects.
16
In France, for a substantial number of cases it was not possible to assess the social class of the respondent. In the
analyses including class we therefore considered an additional category, that is “no class attributed”.
15
To get to a closer interpretation of our results in the case of Italy, the one where the variables
on which we focused our attention turned out to be significant, we can present the effects on each
single party as produced by the multinomial logistic model. Those effects are presented in tables xz
in terms of exp(B) (the exponent of the linear coefficient in the logistic regression equation) and
referring to the full Model 5, with all individual demographic and political controls.
Table 7. Exp(B) of the significant parameters (with sig. level) in model 5 for Italy
AN
Ulivo
Ln(time from provincial capital)
LN
Verdi
RC
UdC
*
0,81
Ln(time from country capital)
**
0,80
Ln(population)
*
0,87
Curviness
*
1,67
Ln(surface)
FI
**
1,37
Other
Centre-Ceft
*
0,72
***
22,26
**
1,67
*
1,79
**
0,84
***
0,49
**
0,78
Legenda: *** p < 0.01; ** 0.01< p < 0.05; * 0.05< p < 0.10
Given that the figures report exp(B), we have to keep in mind that values > 1.0 indicates an
over-representation of that party as a function of the variable at stake (positive effect), compared to
the representation in the reference group (non vote and no answer). As to the opposite, values < 1.0
represent and under-representation (negative effect).
Focusing first on the time distance from the country capital, we see positive effects mainly
for Lega Nord, Udc and Forza Italia, in order of size. With the exception of AN, those are the
parties that form the centre-right coalition, and it is well know how they strongholds are either in
the North or in the South of the country. Lega Nord is only present in the northern part of the
country, where it is also rather strong. Udc is strong in the South. Berlusconi’s Forza Italia is both
prevalent in North but also present in South, while it is weaker in the centre of the country.
Also the effect of surface that shows a significant negative effect for Forza Italia, Lega Nord
and the Greens. The interpretation of this result should be carried out keeping in mind that in this
model we are controlling for all other geographical characteristics. Remembering that the average
surface of the municipalities increases going South-ward, this effects could quite well tap the fact
that, holding the rest constant, those parties are stronger in the North than in South.
These two results together seem to indicate that, compared to France, the Italian electoral
scene is mere geographically scattered, and the territory is divided in several zones where different
16
parties or coalitions are predominant. This is a well-know phenomenon for Italy (Galli et al. 1968;
Diamanti 2003; Diamanti 2009), but the fact that it can be tapped be means of one single variable
and without the use of nominal labels attached to different territories is a rather interesting progress.
It is interesting to note that including distances and other geographical control, the usual
variable included to indicate the centre-periphery cleavage, that is the size of the town, does not
show significant effects if we except a weakly negative effect on National Alliance (AN).
As far as distance from the provincial capital, Forza Italia again shows a weakly significant
negative coefficient (exp(B) < 1.0), together with the conglomeration of the small Center-Left
parties (all below 2% of total valid votes). But this effect seems to be, at this level, residual. This is
disappointing considering the point from where we started, and force us to thing about the limitation
of our analyses. In reaction to the lack of significant result on survey data, we then guessed that this
could be perhaps due to the nature of the data. First, because two-stage sampling of survey data
usually results in poor representation of territorial heterogeneity of a country; secondly, because
sampling inevitably results in poor representation of small cities, where only few cases are selected,
with a much larger potential representation bias. We then proceeded to a test analysis on aggregate
data from the 2006 Italian elections (party shares for main parties in 7529 Italian comuni). Such
analysis only included few structural and geographical predictors. Our aim was only to essentially
test the presence of little more than bivariate relationships, also to check that the lack of bivariate
relationships in survey data was due to an empirical finding, and not an artifact of the sampling
procedure. For each party, we estimated five nested multivariate OLS models of party support
percentages at the municipal level in the 2006 Italian general elections. The five nested models are
composed as follows: Model 1 only includes our basic new indicators> travel time from the
province capital and from Rome (in their logs); Model 2 adds the logged population; Model 3
introduces a measure of how the travel distance is longer than the geodesic distance. Model 4
introduces the logs of the altitude of the centre of the municipality, and finally Model 5 adds the
logs of the overall territorial extension of the municipality.
Table 8 reports our findings, in terms of variance explained at the various stages of the
model for each party. In general, our set of geographical covariates provides a predictive power that
is non-negligible for most parties, and pretty high for others (especially given the total absence of
any sociodemographic predictor): the full model reaches an R-squared around or exceeding the 10%
explained variance threshold for most large parties (8.4% for Forza Italia, 12.7% for the other rightwing party National Alliance, 12.8% for the Ulivo). Moreover, the full model reaches a pretty high
predictive power for parties that we could already expect to be affine with the centre-periphery
cleavage: 0.340 for the Northern League (which ran joint lists with the southern autonomist
17
movement Mpa, considered together in the analysis) and finally 0.934 for the Südtiroler
Volkspartei, the leading party of the German-speaking linguistic minority of the Bolzano province.
Table 8 - R-squared values of nested multivariate OLS models of party support for different parties (based on
geographical variables), on the 2006 Italian election results by municipality
Model 1:
Model 2:
Model 3:
Model 4:
Travel time from Model
1
+ Model
2
+ Model
3
province capital; population (logs) actual/geodesic
altitude (logs)
travel time from
length ratio
country
capital
(logs)
Party
Model 5:
+ Model 4 + land
surface (logs)
Fi
0.021
0.021
0.028
0.043
0.084
An
0.111
0.111
0.116
0.127
0.127
Udc
0.014
0.014
0.015
0.015
0.023
Ln-Mpa
0.291
0.300
0.310
0.312
0.340
Ulivo
0.091
0.097
0.097
0.100
0.128
Prc
0.037
0.037
0.036
0.036
0.038
Rnp
0.020
0.020
0.023
0.023
0.023
Pdci
0.012
0.012
0.013
0.013
0.025
Idv
0.008
0.008
0.009
0.010
0.011
Greens
0.006
0.008
0.009
0.015
0.024
Svp
0.924
0.927
0.928
0.932
0.934
Average
share over
the
full
model
69%
70%
75%
81%
100%
N
7529
7529
7529
7526
7526
A second important finding is that – despite the presence of a set of various geographical
indicators, including population size, municipality land surface, and altitude, the largest part of the
explanatory power of the full model is provided simply by our two measures of travel time (from
Rome and from the provincial capital). As shown in the penultimate row, these two variables alone
already account, on average among all the parties, for almost 70% of the full explanatory power. As
we proceed through the nested models, Model 2 shows an interesting finding: the logs of the
population add little or no predictive power, testifying that the importance of town size is almost
fully subsumed by travel times17. Model 3 and 4 testify how travel times are indeed not able to fully
exhaust the effects of orography: our indicator of “curviness” of the road (the actual/geodesic
distance ratio) still adds to the model, as altitude does. It must be noted, though, that these two
17
The reverse does not apply: a model that only includes the logs of the population has an almost null
predictive power for most parties. It reaches the highest R-squared for the Svp (with a bare 0.071, compared to the
0.924 of the minimal travel times model).
18
indicators are partly overlapping, and part of our ratio indicator can be effectively subsumed by
altitude: a model that excludes our indicator but includes altitude (results not shown here) gets still
close to the explanatory power of Model 4. Finally, the territorial extension of the municipality still
adds to the model. As a general conclusion, though, we can comment that our two measures of
travel time provide alone a parsimonious yet effective account of the effect of geographical
predictors. Such effect proves overall significant, and the largest part of it seems to be effectively
captured by our indicators: we see this as a confirmation of the relevant of these new indicators.
In comparison with the lack of significant results in survey data, we would now conclude
that maybe that result appears more as an artifact of the sampling procedure than a robust empirical
finding. Although, it appears clear that territorial effects can be hardly detected with survey data,
especially with two-stage samples. Unfortunately, given the lack of a comparison with France on
aggregate data, we still do not have a clear answer over the different relevance of the centreperiphery cleavage in the two countries. We though have a confirmation of the usefulness and
empirical soundness of our suggested indicators.
6. Conclusions
We started this analysis on the grounds of what was little more than a general intuition: that
– in relationship to the actual political mechanisms that would substantiate in connection to the
centre-periphery divide – a simple indicator such as the travel time (or travel distance) between
municipalities and political centres at various levels would at least show meaningful variance and
some predictive power on political behaviour. We translated this intuition into several research
hypotheses, which could be tested based on the availability of travel distances and times on the two
meaningful country cases of France and Italy, as well as of survey data on these two countries that
included places of residence of respondents. The first broad hypothesis was that our newly
introduced indicators would allow us to highlight meaningful differences between the two
countries; the second was that the same indicators would show significant effects on a model of
vote choice estimated at the individual level, and that such effects would be stronger in the country
(Italy) with a higher overall level of geographical dispersion, where we would expect a higher
relevance of the centre-periphery divide.
As shown in the previous sections, our newly proposed indicators appear relevant and
meaningful: they allowed us to clearly characterize the two countries in terms of geographical
dispersion, by highlighting features that are overlooked when looking at traditional indicators of
country size. With regard to the effects on predicting individual vote choice, however, we obtained
19
mixed results: analyses based on survey data present indeed effects that are not very strong. When
further testing our hypotheses on aggregate data, we definitely find much more convincing evidence
supporting our second hypothesis. As a result, it will be necessary to address the extent to which
survey data can be effectively used to investigate the role of geographical variables on voting
behaviour, given that some intrinsic features of survey research might result in the systematic
underrepresentation of geographic and territorial dynamics. However, our first assessment is that
our indicators provided some first encouraging results, and that research on the role of geographical
remoteness, employing our newly suggested indicators, appears at least potentially promising.
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