The Social Welfare in Spain before the Crisis: Territorial and

ISSN: 22782278-3369
International Journal of Advances in Management and Economics
Available online at www.managementjournal.info
RESEARCH ARTICLE
The Social Welfare in Spain before the Crisis: Territorial and Chronological
Analysis
Pilar Zarzosa Espina
Department of Applied Economy, Faculty of Economics, University of Valladolid, Spain.
*Corresponding author:[email protected]
[email protected]
Abstract
The aim of this paper is measure social welfare in Spanish autonomous regions before the economic crisis. To
achieve this, we use the distance method P2 to compose a synthetic indicator of welfare for 2007. The index
comprises information on different social indicators from various life domains and enables a classification of
Spanish autonomous communities, in order to determine regional disparities in social welfare levels. Finally, a
chronological comparison is made.
Keywords:
Keywords: Social welfare, Social indicators, Synthetic indicator of welfare, Distance method.
Introduction
This paper summarizes the results obtained in
research on social welfare in Spanish autonomous
regions1. The aim is to determine regional
disparities and to analyze chronological changes
before the current economic crisis. A synthetic
welfare indicator2 is obtained, using the distance
method P2 [1]. This methodology adopts the
multidimensional measurement approach [2-4]
that is, the social indicator approach [4-8] and
solve the main problems for the development of a
synthetic indicator of welfare [9-12]. Said
problems are: selecting the areas into which social
welfare may be broken down; choosing the simple
or partial indicators which are appropriate
statistical measures for measuring each area; and
aggregating the simple indicators through a
suitable synthetic welfare indicator. Therefore
said synthetic indicator should resolve such
important problems as the heterogeneity of the
measuring units of the various simple indicators,
and the weighting system (duplicity of
information contained therein, and the impact
attached to each).
Somarriba [13] and Somarriba and Pena [14]
compare the DP2 indicator to other methods in
order to obtain synthetic indicators such as
Principal Component Analysis and Data
Envelopment Analysis, concluding that the former
evidences certain advantages over the other
methods. Examples of works which apply the DP2
1 Except Ceuta and Melilla. Research funded by Junta de Castilla y
León: CES, 2009, principal investigators J: M: Gómez García and M.
Prieto Alaiz; and CEH, 1995, principal investigator P. Zarzosa
Espina.
Molpeceres (2008) conducted a detailed description of the most
widely known and internationally used synthetic welfare indicators
2
Pilar Zarzosa Espina| July.-Aug. 2012 | Vol.1 | Issue 4|165-171
method to creating indicators in the area of
welfare include Cuenca et al. [15], Maestro and
Martínez [16], Rodríguez [17], Somarriba [18],
Somarriba and Pena [19], Zarzosa [20], Zarzosa
[21], and Zarzosa and Somarriba [22]. Merino,
Somarriba and Negro [23] apply the DP2 method
to measuring the employment quality in the
Spanish regions.
Recent applications of other synthetic indicators
applied for various goals in the case of Spain may
be found in Ayala and Navarro [24], Jurado and
Pérez-Mayo [25], Murias et al. [26,27], and PérezMayo[28]. However, there is a line of research
which obtains synthetic welfare indicators
comprising solely economic indicators, such as
Ayala et al. [29, 30].
Somarriba [15] applies the multidimensional
measurement approach to the European case,
Halleröd and Larsson [31] to Sweden and Van
Oorschot and Meuleman [32] to the Netherlands.
Haveman [33] proposes a multidimensional
approach for measuring poverty and social
exclusion in European Union countries. Kakwani
and Silber [34,35] discuss the many dimensions of
poverty and presents different techniques for
applying the multidimensional approach to
measuring poverty. Caminada, Goudswaard and
Koster [36] use a multidimensional approach
(multiple regression) to analyzing the impact that
social expenditure has on poverty rates for OECD
countries.
The paper is structured as follows. Firstly, a brief
overview is provided of the methodology used to
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measuring social welfare. The following section
presents the system of simples indicators used to
create the synthetic indicator. We then apply our
synthetic indicator to measuring welfare in
Spanish Autonomous Regions, before the present
economic crisis. After, we performed a
chronological analysis of social welfare in Spain
before the economic crisis. The final section
contains the main conclusions to emerge from the
work.
Methodology
Besides being based on the concept of distance,
the DP2 indicator belongs to a group of measures
based on axiomatic derivations (Deutsch and
Silber, 2005), in other words created to meet a
series of requirements deemed necessary to
achieve the stated goal. The DP2 indicator verifies
a set of properties which we shall see later.For a
thorough review of the P2 indicator, see Pena [1],
Zarzosa and Somarrib [22]. Here, we merely offer
a brief overview of the technique in order to
provide a basic outline of its application to the
current research.
The P2 Distance from region j is defined as
follows:
(
)
 d i

 1 − R 2


i , i − 1 ,..., 1 
σ
i 

i=1  
2
with R1 = 0 ; where d i = d i (r ∗) = xri − x*i with
DP
2
=
n
∑
thereference base X * = ( x*1 , x*2 ,..., x*n )
where:
- n is the number of variables
- xri, is the value of the variable i in region r
- σ i is the standard deviation of variable i
- Ri2,i −1,...,1 is the coefficient of determination in the
regression of Xi over Xi-1, Xi-2,...,X1 , already
included
Thus defined, the synthetic indicator measures
the distance, with regard to the object studied,
between each region and a fictitious base
reference. In this instance, the base reference
comprises the results from an imaginary region
which reflects the worst possible scenario for all
the simple indicators and would therefore be
attributed a value of zero in the synthetic welfare
indicator.
By dividing it by a standard deviation, the
problem of heterogeneity of the measuring units
of the variables is dealt with, such that all the
partial indicators (quotients involved in the
expression) are expressed in abstract units.
The coefficient of determination, R2i.i-1,...,1,
measures the part of the variance of each variable
explained by the linear regression estimated
Pilar Zarzosa Espina| July.-Aug. 2012 | Vol.1 | Issue 4|165-171
using the preceding variables. As a result, the
factor (1-R2i.i-1,...,1), referred to as the ”correction
factor" by Pena, prevents redundancy by
removing from the partial indicators the
information already contained in the preceding
indicators. In this way, the synthetic indicator
only includes the new information from each
variable.
The properties established by the P2 distance
synthetic indicator make it the ideal measure for
the purpose set out in the current research. Said
properties are expressed using the following
names: Existence and Determination, Monotony,
Uniqueness
quantification,
Invariance,
Homogeneity,
Transitivity,
Exhaustiveness,
Additivity, Invariance compared to the base
reference, Conformity, and non-arbitrariness in
the importance attached to the simple indicators.
For a brief description of these, see Zarzosa and
Somarriba [22]. Any interested readers who wish
to delve deeper into these properties may consult
the previously cited references, Pena [1] and
Zarzosa [37].
Selecting and Devising Simple Indicators
Since the approach to measuring social welfare in
the present research involves using indicators, we
need to select and devise a system of simple or
partial indicators which allows us to gauge the
actual opportunities that people have in the
various areas or aspects of social welfare [5]. More
than 100 indicators were designed and analyzed
at the initial stage, data generally corresponding
to 2007. The sources used were: Caja España [38],
INE (The Spanish National Statistics Institute,
2008), Jiménez-Ridruejo and López [39] and La
Caixa [40]3.
At the second stage, 72 partial indicators were
chosen. The table 1 shows the 31 simple or partial
indicators obtained as a result of a third process
of selection [41,42]. A full description of the
indicators, and of the criteria used, is provided in
Zarzosa and Somarriba [22]. The small set of
indicators is felt to include most of the available
information concerning regional welfare in Spain
which is to be used in the synthetic indicator. As
is common when applying the social indicator
approach, simple indicators are grouped in
various domains, also known as fields,
components, dimensions, areas, etc. In this case,
3
The methodology used to calculate the regional results requires obtaining
previously provincial results. Unfortunately, the need to have provincial data
for all the simple indicators means that information provided by surveys such as
the EU-SILC (Eurostat, 2010) and INE (2010) cannot be used, therefore forcing
us to reject subjective information. Despite this drawback, studying welfare at
such a level of territorial desegregation, albeit using objective information, is of
great interest with regard to decision-making and developing policies aimed at
enhancing citizens’ well-being.
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Table 1: Variables chosen to calculate the synthetic DP2 indicator, arranged in dimensions4
Income and
Education
Work
Social protection
wealth
GDP
Illiterate males
Male unemployment
Unemployment benefit
Higher academic
Fatal work accidents
Average non-contributory
qualifications pension
males
Physical
Culture
Social cohesion
Health
Culture and
environment
leisure
Bank
Film screens
Admission to hospitals
Infant mortality -women
branches
Film-goers
due to alcohol and drug
Perinatal mortality
Registered
Hotel places
dependence
Female deaths caused by
vehicles
Overnight stays
Arrests
diseases which could be
Motorways
in hotels
Violent deaths
avoided through primary
Burned forest Campsite places
(murders)
prevention
land
Female deaths caused by
diseases which could be
prevented through medical
health care
Hospital beds
Psychiatric visits
AIDS patients
Traffic accident injuries
New technologies
Broad-band lines
Municipal indexes
Economic activity
index
Tourist index
Mercociudad
Spanish ranking
of cities
Source: Own.
Table 2: Ranking of spanish regions, applying the DP2 synthetic welfare indicator. 2007
Ranking
Spanish autonomous regions
Ordered synthetic welfare indicator
1
Madrid
32,85
2
Navarra
32,44
3
Baleares
32,21
4
Cataluña
31,48
5
Rioja
31,21
6
País Vasco
28,41
7
Cantabria
27,87
8
Aragón
26,79
9
Comunidad Valenciana
26,00
10
Castilla y León
25,33
11
Canarias
23,59
12
Murcia
22,51
13
Asturias
22,25
14
Galicia
22,17
15
Castilla La Mancha
21,99
16
Andalucía
21,10
17
Extremadura
19,85
Source: Own
Table 3: Descriptive measures of the DP2 synthetic welfare indicator for spanish regions grouped in terms of the
synthetic indicator values. 2007.
Spanish autonomous regions
Distance/Total
Distance
High Welfare (5)
0,13
Medium Welfare (5)
0,24
Low Welfare (7)
0,29
Spain (17)
1,00
Mean
Difference to
the Spain Distance/Mean
Mean
32,04
5,68
26,88
21,92
26,36
0,52
-4,44
Variation coefficient
0,06
0,02
0,12
0,04
0,14
0,05
0,49
0,16
0
Source: Own.
4
The factor referred to as demography does not appear in this table, since no simple indicator of said factor was selected. A full description of all the indicators
analysed, as well as the sources used, is provided in Zarzosa and Somarriba (2012).
Pilar Zarzosa Espina| July.-Aug. 2012 | Vol.1 | Issue 4|165-171
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we principally drew on the National Institute of
Statistics (INE-Spanish acronym) classification
[43], such that the following are taken into
account: demography, family income and wealth,
education, employment, social protection, health,
physical environment, culture and leisure, social
cohesion, new technologies and municipal indices.
Social Welfare
Regions
in
Spanish
Autonomous
Applying the DP2 method to the simple indicators
contained in Table 1 gives the results shown in
Table 2, wherein the various autonomous regions
are ranked in order of their welfare indicator
value. Following the definition given in the base
reference, the greater the value of the synthetic
indicator, the greater the level of welfare. An
imaginary region which reflected the worst
situation for all the simple indicators would have
a value of zero in the synthetic welfare indicator.
Moreover, to facilitate analysis, a cartogram has
been created showing the geographic location of
the various regions, grouped in terms of the
synthetic indicator values. Regions are grouped in
terms of the percentage increase on the minimum
value of the synthetic indicator: High welfare
(>50), medium welfare (between 20 and 50) and
low welfare (<20).
Fig. 1: Classification of spanish autonomous regions,
following the DP2 synthetic welfare indicator. 2007
The darker the colour, the lower the level of
regional welfare. The above figure reveals a clear
geographical pattern in which regions located in
the north-east of the peninsula enjoy a higher
level of welfare.Given the properties of synthetic
indicator DP2, it is possible to interpret in
cardinal terms the distances between each region.
Extremadura, for instance, which has the lowest
synthetic indicator value, is therefore 19.85 units
away from the undesired imaginary region (zero
synthetic indicator value) and 13 units away
(distance) from the best region, which is Madrid.
Pilar Zarzosa Espina| July.-Aug. 2012 | Vol.1 | Issue 4|165-171
All the remaining interregional distances may be
determined likewise.
A series of descriptive measures has been
estimated for each of the groups to facilitate
analysis:
As can be seen, the distance values in the three
groups differ, the largest dispersion occurring
between provinces with the smallest welfare.
The explanatory factors of Spanish disparities in
social welfare, are analyzed in Zarzosa and
Somarriba [22].
Chronological Analysis
The results were compared with those obtained
with 1991 data [44]. The classification obtained
using the same criteria, was as follows:
-High welfare: Navarra, Madrid, Aragón and País
Vasco.
-Medium welfare: Cantabria, Castilla y León,
Cataluña,
Rioja,
Asturias,
Comunidad
Valenciana, Baleares, Castilla La Mancha and
Galicia.
-Low welfare: Murcia, Extremadura, Canarias
and Andalucía.
The figure 2 reflects the geographic location.
Fig. 2: Classification of spanish autonomous regions,
following the DP2 synthetic welfare indicator. 1991
As can be seen, in the period 1991-2007, before
the economic crisis, the situation worsens
considerably. In 1991 the group of low level of
welfare contained only 4 autonomous regions,
located in the south of the country. However, in
2007 the autonomous regions with low welfare are
7.In addition, in 1991 the 3 groups had
approximately the same dispersion. However, in
2007 the dispersion is different in the 3 groups,
the regions with less welfare have more
dispersion. The global dispersion also increases
throughout the period, with regard to the
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Table 4: Descriptive measures of the DP2 synthetic welfare indicator for Spanish regions grouped in terms of the
synthetic indicator values.
values. 1991
Spanish autonomous regions
Distance/Total
Distance
Mean
High Welfare (4)
Medium Welfare (9)
Low Welfare (4)
Spain (17)
0,26
0,26
0,20
1,00
40,58
34,71
27,86
34,48
variation coefficient (0,14-0,16). The total distance
of each group with regard to its mean increases in
the group with lower welfare and decreases in the
other two groups, especially within the greater
welfare. That is, throughout the analyzed period,
worsens the situation of the regions with the
worst level of welfare.However, the total distance
with regard to the Spain mean decreases in the
analyzed period (0,54-0,49). In addition, the
difference to the Spain mean decreases in the two
extreme groups. That is, although the global
disparity (variation coefficient) increases, but the
disparity within groups of high and medium
welfare decreased.
Conclusions
The findings to emerge in the present research
are based on a specific set of simple indicators
taken from recently available information for all
Spanish provinces. These indicators have been
chosen and filtered, providing us with a final total
of 31 objective indicators to work with, given the
absence of any subjective information for a
territorial desegregation of this nature.Using this
set of indicators, we calculate a provincial
Synthetic Welfare Indicator (SWI) using distance
DP2, the methodology for which is briefly
explained in the article. Based on the results to
emerge from this indicator, the following
conclusions may be drawn.The various statistical
criteria used indicate that the degree of disparity
in welfare levels amongst Spanish provinces is
extremely moderate. The
Difference to
the Spain Distance/Mean
Distance/Mean
Mean
6,1
0,14
0,23
0,14
-6,62
0,11
0
0,54
Variation coefficient
0,05
0,05
0,05
0,14
greatest dispersion, both in absolute as well as
relative terms, is apparent in provinces
evidencing the highest welfare.In terms of
geographical distribution, we see how provinces
located in the North and North-East of the
peninsula enjoy the highest levels of welfare.The
current work should be seen as an initial
approach towards measuring social welfare
amongst Spaniards, and as an attempt to provide
further, albeit small, insights into research which
seeks to provide information aimed at offering a
basis for improving citizens’ welfare. Tal vez coger
algo de la ultima frase y añadir que los rtdos de
este trabajo se han obtenido para un período en el
cual aún no se habían experimentado claramente
las consecuencias de la actual crisis económica y,
aún así, la situación ya había empeorado
considerablemente.In summary, based on the
results to emerge in the present research, the
following conclusions may be drawn.In terms of
geographical distribution, we see how autonomous
regions located in the North-East of the peninsula
enjoy the highest levels of welfare.Before the
economic crisis, in the period 1991-2007, the
situation worsens because increases the number
of autonomous communities with low social
welfare and also increases the level of regional
disparity. The deterioration is higher for Spanish
regions with lower welfare. Unfortunately, we can
assume that the current economic crisis, which
was felt from 2007, will exacerbate these regional
disparities in the level of social welfare.
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