Template-Type: ReDIF-Article 1.0
Author-Name: Estefanía Villar Cheda
Author-Name: Mª Esther Calvo Ocampo
Author-Name: Mª Esther López Vizcaíno
Author-Name: Carlos L. Iglesias Patiño
Author-Name: Solmary Silveira Calviño
Author-Name: Mª Isolina Santiago Pérez
Title: Clasificación de los municipios gallegos según su grado de urbanización
Abstract: RESUMEN:
En el marco de la estadística oficial y dentro de las
clasificaciones que emplean un número reducido de variables geodemográficas, el
Instituto Galego de Estatística (IGE) publicó en julio del 2011 una
clasificación del grado de urbanización de los municipios gallegos en seis
zonas, basada en un estándar europeo. Teniendo en cuenta análisis posteriores
(Calvo et al, 2012) se optó por dividir a Galicia en tres zonas, rural,
semiurbana y urbana. El objetivo de este trabajo es comprobar cómo esta
agregación en 3 clases, se asemeja a la
obtenida con un modelo que emplea características sociodemográficas,
sociolaborales y económicas que, a priori, se vinculan con el proceso de
urbanización.ABSTRACT:Provide
informationat the provincial levelin Galiciadoes not always solvethe problems
associated withdesigningand implementing public policies, especiallythose that
aim to reduce disparitiesin the level of development achieved. Thisdoes not
solve, either, the demands ofprivateinformation.
However,
thereis broad agreementthat the habitatcan be determinant insocioeconomic
status. Therefore it is interesting to have informationrelatesto the
urbanandruralinGaliciaor itsprovinces. Apriori, the difference betweenthe urban
areas oftwo provincesseems lower than betweentheurban andruralareasofthe same
province.It would be usefulto contrastthis assumptionwithempirical evidence.
Inthis
framework and within the officialstatisticsclassificationswhich employ asmall
number ofgeo-demographicvariables, the Galician Statistics Institute(IGE)
published in July 2011a classificationof the degree ofurbanization of Galician
municipalitiesinto six types of areas, based on a European standard. Taking
into account subsequent reviews (Calvo et al, 2012) we divideGaliciainto three
areas, rural, semi-urbanand urban.Theaim of this studyis to see howthis
aggregationinto 3 classes is similar to that obtained witha statistical model
thatuses sociodemographic,labour and economic characteristics that, a priori, are
linked tothe urbanisation process.
In this
case the units of analysis are the 315 municipalities of Galicia and the
dependent variable is the classification of municipalities in rural, semi-urban
and urban areas. To analyze its relationship with other sociodemographic, labor
and economicvariables, we adjusta multinomial logistic regression model which
is an extension of the classical logistic regression to the case where the
response variable is more than two categories(1-rural classification, 2-
semi-urban, 3- urban). In this multinomial logistic regression model, the
category of urban is considered as the reference, and each of the remaining
categories are compared to it.
For the
model variable selection we take into account the results of the three phases
of the exploratory analysis: box plots, univariate multinomial logistic models
and correlation analysis. We select 12 variables for use as potential
explanatory variables in the multinomial model fit, for the first step. After
that, we readjust the model with the variables that were significant in the
first step and we repeat again what was done in the previous step. We finish
the model fit in the third step, in which all the variables included are
significant: the average age, the percentage of people registered in Social
Security System in services, gross disposable income per capita (GDI) and the
social benefits in percentage of GDI.
We
calculate also the Odds ratio (OR) that is the relative probability that a
municipality isclassifiedin the category k of the response variableYoverit is
classifiedinthe reference category(third, inthis case) foran element witha
valueofX=1 comparedwith one that hasa valueof X= 0.If the variableXis ordinalor
continuous, theORcomparesthe relative probabilityassociated witha unit change
inthe variableX. In this case exists interaction between variables, then the
estimate of theORassociated witha variabledepend on the valueof the variable
thatis interactingwith it.Thisis the caseof the social benefits in percentage
of GDI, whichdependsof the ORof the registered in Social Security System in
services.
For
example, in theruralmodel, theORfor oneunit change inthe social benefits in
percentage of GDI dependsonthe value of the ORof the people registered in
Social Security System in services through theexpressionexp (1.895) exp(-0.04*PAFIL_SERV),
so ,in a municipalitywith a percentageequal to30.2% of registered in Social
Security System(minimum value) theORof benefitsis2.011andfora municipality with
anpercentage of registered in services equal84.1%(maximum) is 0.233.Theunit
increaseinthe social benefits in percentage of GDI increases the probability
ofrural versusurban(OR>1)in municipalities withlow values of
thepercentageof registered in Social Security System in servicesanddecrease (OR
<1) in municipalitieswith highervalues of thisvariable.
Thefit of
the modelpermit evaluateif theinitial classificationis similar tothat
predictedby the model.It was consideredthe maximum(p1, p2,
p3) as a criterionto classifymunicipalities. The estimated modelhas
anability to correctly classify85.4% of the municipalities, butthe modelclassifiesbetterruraland
semi-urban. In fact, themodel is consistentwith the classification ofIGEin 93.2%
of rural municipalities, 73.9% of semi-urbanand 72.2% of urban.It is convenient,
however, a more detailedanalysisof the 46municipalities assignedby the model
thatdifferfrom the initial categories.
The
characterization of the degree of urbanization of Galicia inthree areas(urban,
rural and transitional), from theaggregationof a classificationin sixdegrees,thatmainlyuses
thepopulation density,isadequately explainedby amultinomiallogistic modelthat
usesothersocioeconomic variables. With this,ithas achievedthe goalof the workin
its variousfacets. First,the urban spaceinGaliciais broader thanthe ZPDoriginal,enlarged
withtheZIPhigh. Second, a classification thatis basedonlyin basic
considerationsgeo-demographicis consistentwith anotherthat employs
arichersocioeconomic data. Third and last, we have used anew methodology based
ona statistical techniquedifferent fromthose previouslyusedtobuilda clearer anda
moreparsimonious model.
The
methodology usedin this work allowsto obtain conclusionsdirectlyfrom the
variablesconsidered in the model, thus avoiding the useof techniquesto
reducethe dimension(whichare detrimental tothe interpretabilityof the model)as
has been donein other studies ofsimilar characteristics(Iglesiasetal,
2000;Ruaetal, 2003; Penaetal, 2008).
The model alsoallows us to conclude, as inother studies, that the presence
ofthe tertiary sectorand high levelsofincome, linked to the demographic
dynamics, appear to be the determinantsofintra-regionaldisparities in Galicia (Pena
etal, 2008).
Classification-JEL: R1
Keywords: Galicia, Grado de urbanización, Urbano, Rural, Clasificación, Regresión multinomial, Degree of urbanisation, Urban, Clasification, Multinomial regression
Pages: 193-223
Volume: 03
Year: 2013
File-URL: http://www.revistaestudiosregionales.com/documentos/articulos/pdf-articulo-2426.pdf
File-Format: Application/pdf
Handle: RePEc:rer:articu:v:03:y:2013:p:193-223