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Quality of life and economic convergence acrossSpanish regions, 1980–2001Andrés J. Marchante a & Bienvenido Ortega aa Departamento de Economía Aplicada (Estructura Económica) , University of Malaga ,Campus El Ejido, E-29071, Málaga, SpainPublished online: 23 Jan 2007.

To cite this article: Andrés J. Marchante & Bienvenido Ortega (2006) Quality of life and economic convergence acrossSpanish regions, 1980–2001, Regional Studies, 40:5, 471-483, DOI: 10.1080/00343400600757460

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Quality of Life and Economic Convergenceacross Spanish Regions, 1980–2001

ANDRES J. MARCHANTE and BIENVENIDO ORTEGADepartamento de Economıa Aplicada (Estructura Economica), University of Malaga, Campus El Ejido, E-29071 Malaga, Spain.

Emails: marchante@uma.es and ortega@uma.es

(Received September 2004: in revised form May 2005)

MARCHANTE A. J. and ORTEGA B. (2006) Quality of life and economic convergence across Spanish regions, 1980–2001, Regional

Studies 40, 471–483. Evaluations of regional welfare conventionally rely on gross value added (GVA) per capita as an indicator of

living standards. This paper attempts to readdress the regional welfare issue by using alternative composite indicators in the context

of the United Nations Development Programme (UNDP) Human Development Indices. Evidence is presented regarding to what

extent such alternative measures of living standards are converging. With this aim, a database for the Spanish regions (NUTS II)

was constructed for 1980–2001 and an augmented version of the Human Development Index (AHDI) was estimated incorporat-

ing sub-indices of health, education and the resources required for a decent standard of living, including an indicator of social

exclusion. The regional convergence of three per-capita income measures, and the raw data of seven indicators of quality of

life, were also studied. The empirical analysis suggests that whereas regional GVA per-capita disparities have remained constant,

convergence was achieved in five quality-of-life indicators (AHDI, infant survival rate, adult literacy rate, mean schooling years,

and 100 minus the rate of long-term unemployment) and in two alternative economic measures (total personal income less current

grants and gross personal disposable income).

Regional convergence Living standards Human Development Index (HDI) Spain

MARCHANTE A. J. et ORTEGA B. (2006) La qualite de la vie et la convergence economique a travers les regions d’Espagne, entre

1980 et 2001, Regional Studies 40, 471–483. Normalement, les estimations du bien-etre regional dependent de la Valeur Ajoutee

Brute (VAB) par tete comme indice des niveaux de vie. Employant d’autres indices composes dans le cadre des indices du devel-

oppement de l’homme UNDP, cet article cherche a remettre en question le bien-etre regional. On presente des preuves quant a

l’importance de la convergence de telles autres mesures du niveau de vie. Dans ce but, on construit une banque de donnees pour la

periode allant de 1980 a 2001 et pour les regions d’Espagne (NUTS II), et on estime une version augmentee de l’indice du devel-

oppement de l’homme (AHDI) qui inclut des sous-indices de la sante, de l’education et des ressources necessaires a un bon niveau

de vie, y compris un indice de l’exclusion sociale. On etudie aussi la convergence regionale de trois indices du revenu par tete et les

donnees brutes provenant de sept indices de la qualite de la vie. L’analyse empirique laisse supposer que, tandis que les ecarts de la

VAB regionale sont constantes, on reussit la convergence des indices de la qualite de la vie (AHDI, le taux de survie infantile, le

taux d’alphabetisation des adultes, le taux de scolarite moyen, et le taux de chomage a long-terme) et de deux autres mesures

economiques (revenu individuel global moins les primes en vigueur et le revenu disponsible individuel global).

Convergence regionale Niveaux de vie Indice du developpement de l’homme Espagne

MARCHANTE A. J. und ORTEGA B. (2006) Lebensqualitat und wirtschaftliche Konvergenz in allen spanischen Regionen (1980–

2001), Regional Studies 40, 471–483. Bewertungen des regionalen Fursorgewesens sind normalerweise auf den pro-Kopf

Bruttomehrwert ( gross value added ¼ GVA) als Anzeichen des Lebensstandards angewiesen. Dieser Aufsatz versucht, die Frage

des regionalen Wohlfahrtswesens mit alternativen Sammelindikatoren im Zusammenhang der UNDP Indexe menschlicher

Entwicklung neu anzugehen. Es werden Beweise des Ausmaßes vorgelegt, in dem alternative Maßtabe des Lebensstandards

sich an einander annahern. In dieser Absicht wurde eine Datenbank fur die spanischen Regionen (NUTS II) fur den Zeitraum

1980–2001 angelegt, und eine erweiterte Fassung des Human Development Indexes (AHDI) berechnet, einschliesslich der

Unterabteilungen des Gesundheits- und Bildungswesens, sowie der fur einen akzeptablen Lebensstandard erforderlichen

Mittel, einschliesslich Anzeichen gesellschaftlicher Benachteiligung und die regionale Konvergenz dreier pro-Kopf Einkommens-

maßnahmen und die unaufbereiteten Daten von sieben Indikatoren der Lebensqualitat untersucht. Die empirische Analyse legt

nahe, daß obschon regionale GVA Pro-Kopf Disparitaten gleichgeblieben sind, Konvergenz in funf Lebensqualitatsindikatoren

(Sauglingsuberlebensquote, Analphabetenquote der Erwachsenen, durchschnittliche Dauer des Schulbesuchs, und die 100 –

Quote langfristiger Erwerbslosigkeit (AHDI, infant survival rate, adult literacy rate, mean schooling years, und 100 minus the rate of

long-term unemployment) erreicht worden ist, und zwar mit Hilfe zweier alternativer Wirtschaftsinitiativen (personliches Gesamtein-

kommen, abgesehen von laufenden Zuschussen, und personlich verfugbare Bruttoeinkommen).

Regionale Konvergenz des Lebensstandards Index der menschlichen Entwicklung Spanien

Regional Studies, Vol. 40.5, pp. 471–483, July 2006

0034-3404 print/1360-0591 online/06/050471-13 # 2006 Regional Studies Association DOI: 10.1080/00343400600757460http://www.regional-studies-assoc.ac.uk

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MARCHANTE A. J. y ORTEGA B. (2006) La calidad de vida y la convergencia economica en las Comunidades Autonomas en

Espana (1980–2001), Regional Studies 40, 471–483. Las valoraciones del bienestar regional normalmente se basan en el Valor

Anadido Bruto (VAB) per capita como un indicador de los niveles de vida. En este artıculo pretendemos volver a analizar la cues-

tion del bienestar regional mediante indicadores alternativos compuestos segun los Indices de Desarrollo Humano del PNUD.

Demostramos en que medida convergen tales medidas alternativas del nivel de vida. Para este fin se ha construido una base de

datos para las Comunidades Autonomas en Espana (NUTS II) para el periodo 1980 a 2001 y se ha calculado una version ampliada

del Indice de Desarrollo Humano (IDHA) incorporando subındices de salud, educacion y los recursos necesarios para tener un

nivel de vida decente, incluyendo un indicador de la exclusion social. Tambien estudiamos la convergencia regional en cuanto

a tres mediciones de la renta per capita y los datos no procesados de siete indicadores de calidad de vida. El analisis empırico

indica que, si bien las desigualdades del VAB regional per capita han permanecido constantes, se obtuvo una convergencia en

cinco indicadores de la calidad de vida (IDHA, ındice de supervivencia infantil, tasas de alfabetizacion en adultos, anos medios

de escolarizacion, y 100 menos la tasa porcentual de desempleo a largo plazo) y dos indicadores economicos alternativos

(Renta Primaria de las Familias y el Renta Familiar Bruta Disponible).

Convergencia regional Nivel de vida Indice de Desarrollo Humano Espana

JEL classifications: I31, O18

INTRODUCTION

In a recent analysis based on an opinion poll surveycarried out in 2002 by the National Centre for Socio-logical Research (CIS), ALVIRA and GARCIA (2003,p. 151) conclude:

there are considerable differences between perceived

quality of life and income. More Catalonians perceive

their living standards to be higher than the residents of

Madrid do. Aragon and Castilla-Leon are lower in the

regional welfare ranking than they should be considering

their income. Andalusia is also a special case: it has one

of the lowest income levels in Spain, but 38 per cent of

its inhabitants believe that it is one of the three best

regions to live in. Personal income and perceived welfare

are not equivalent factors.

Although the above refers to personal income, it can beapplied with even more reason to gross value added(GVA) per capita, because regional inequalitiesdepend on the macroeconomic aggregate under con-sideration. It is important to note that inequalities willbe smaller in terms of per-capita gross personal disposa-ble income (GPDI) than in terms of per-capita GVA as aresult of public-sector redistribution.

Economists have been aware of the limitationsassociated with using GVA per capita as a standardof living indicator for decades. NORDHAUS andTOBIN (1972) emphasized the importance of consid-ering other factors that affect welfare and which canbe related to national income, such as leisure andinequality; shortly afterwards, SEN (1985) stated thatthe ultimate goal of society should be to increase theindividual’s standard of living. Sen is critical of theperspective of opulence, which places too muchemphasis on income when evaluating well-being.According to him, the successes and failures in thestandard of living are matters of living conditionsand not of the gross picture of relative opulence thatthe gross national product tries to capture in realnumbers. Thus, reservations regarding the use of

GVA per capita as an indicator of economic well-being are certainly not new, although they continueto exist (KHAN, 1991).

Not only has GVA per capita been questioned as astandard of living indicator, but also its lack of precisionhas been criticized. According to BOLDRIN andCANOVA (2001), the Hamburg region in Germanyhas a high GVA per capita, but half its metropolitanpopulation lives in the nearby areas of Schleswig-Holstein and Lower Saxony, commuting to work on adaily basis. Due to this, the Hamburg GVA is overesti-mated by around 20% in relation to its actual popu-lation, whereas the GVA of Schleswig-Holstein andLower Saxony are underestimated. This situation issimilar to major metropolitan areas in Spain, such asMadrid (one of the highest GVA per-capita values inSpain in recent years), in relation to the Castilla-Leonand Castilla-La Mancha regions. Thus, convergence inGVA per capita across statistical areas (regions) definedin this way cannot be considered a reasonable objectivewith any degree of certainty.

Given these considerations, for many years work hasfocused on approaching real convergence across econ-omies not only in relation to economic indicators, butalso in terms of other social and quality-of-life indi-cators (e.g. GIANNIAS et al., 1999; HOBIJN andFRANSES, 2001; O’LEARY, 2001; MAZUMDAR, 2002,2003; OBSBERG and SHARPE, 2002). Greater economicdevelopment associated with ongoing regionally andsocially balanced growth is the goal of any industrializedcountry whose ultimate aim is to improve livingconditions. In this respect, it would seem appropriateto use supplementary indicators in addition to GVAper-capita results.

In this context, most studies on regional convergencein Spain have focused on the analysis of convergence ofeconomic indicators, with GVA per capita being themost used indicator (e.g. GOERLICH and MAS, 2001;MANCHA, 2001; DE LA FUENTE, 2002). These studiesdraw similar conclusions for the period starting in

472 Andres J. Marchante and Bienvenido Ortega

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1980: there is a lack of regional convergence and similarGVA per-capita growth rates, a circumstance in whichinequalities are unlikely to disappear.

The purpose of the present paper is, therefore, topresent a different perspective on the evolution ofliving standards in Spanish regions since 1980 by con-structing a Human Development Index (HDI) basedon the method used by the United Nations Develop-ment Programme (UNDP). However, since Spain isan industrialized country, the HDI is augmented withnew components as suggested in prior work. Thepresent research thus adopts an approach similar tothat used by CRAFTS (1997) in a study on the impli-cations of economic development for living conditionsin Organization for Economic Co-operation andDevelopment (OECD) Western European andEastern Asian Member States. The present paper alsofollows HORREL (2000) in his analysis of achievementsrelated to living standards in the UK in the 20th century.With the construction of this composite index, thedirect analysis of quality-of-life indicators, and threedifferent per-capita income measures (GVA, total per-sonal income minus current grants (TPI) and gross per-sonal disposable income (GPDI)), the present paperattempts to extend the current view regarding theregional convergence of the standard of living acrossSpanish regions. Consequently, the main new contri-butions of the study refer to the period analysed anduse of an extensive range of economic and quality-of-life indicators.

Bearing these considerations in mind, the paper isorganized into four further sections and a statisticalAppendix. The next section presents the methodused to implement the augmented Human Develop-ment Index (AHDI). The results obtained with thissocio-economic indicator in the last 20 years of the20th century are presented in the third section. Thefourth section provides the empirical results obtainedfrom the two approaches usually employed in theanalysis of convergence of economic indicators andalternative social indicators (b- and s-convergences).Finally, the fifth section presents some final consider-ations. The Appendix contains information on thestatistical sources from which the database wasconstructed.

REGIONAL STANDARD OF LIVING: A

COMPOSITE INDICATOR

The HDI measures the average achievements of asociety in relation to three basic dimensions of humandevelopment (UNITED NATIONS DEVELOPMENT

PROGRAMME, 2003):

. Long and healthy life.

. Knowledge.

. Decent standard of living.

According to the UNDP guideline, an HDI wasconstructed for the Spanish regions (NUTS II) for1980–2001. However, several changes were made tothe aforementioned UNDP index, due to the following:

. The restraints imposed by the availability of statisticaldata for all the Spanish regions since 1980.

. The need to consider the high level of developmentof Spanish regions in comparison with the underde-veloped countries included in UN Human Develop-ment Reports. The paper has consequently includedadditional variables in the HDI, constructing whatthe authors call an AHDI and selecting themaximum and minimum values of the different indi-cators according to the level of development of theSpanish economy.1

Taking these considerations into account, the differentAHDI sub-indicators used were as follows.

Health index

The health index (achievement A1j), which is con-structed from the life expectancy at birth indicator(whose achievement index is called LIFE), the infantsurvival rate indicator (whose achievement index iscalled INFS) and the probability at birth of survivingto age 60 years indicator (achievement index SURV),is calculated according to the following expression (forregion j and year t):

A1j(t) ¼1

3LIFE j(t)þ

1

3INFS j(t)þ

1

3SURV j(t) (1)

The LIFE index measures achievements in health andnutrition. Low achievement normally indicates thatpart of the population lives in poor conditions andthat there is a deficient healthcare system.

Infant survival rate, which is calculated as 1000minus the infant mortality rate per 1000 births (childrenunder 1 year old), has been considered as an additionalvariable following the recommendations of ANAND andSEN (1994) for the construction of an HDI for devel-oped countries. This indicator is complementary tolife expectancy at birth because it also measures asociety’s healthcare and nutritional conditions. It isalso useful as an indicator of the possible persistence ofcontagious diseases to which children are moreexposed. In fact, according to MAZUMDAR (1999),the infant survival rate index (INFS) is more sensitiveto well-being than LIFE and responds more readily tomany healthcare-related interventions.

The SURV index is an indicator of vulnerability todeath at a relatively early age. It has been included inthe AHDI because it is a way of taking morbidity intoaccount. Additionally, this indicator is among thoseconsidered in the construction of the UN HumanDevelopment Indices. Unfortunately, a specific indi-cator of quality of life, such as health-adjusted life

Quality of Life and Economic Convergence across Spanish Regions 473

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expectancy (HALE), is not available at the regional levelin Spain.

Note that the UN Human Development Reportsgive more relevance to the LIFE index than the INFS.However, in this augmented version of the HDI, thepresent authors consider that in developed countriesall indicators are equally important in the developmentof the health dimension.

Education index

Education index (achievement A2j) is constructed fromthe adult literacy rate indicator (whose achievementindex is called ADLI) and the mean years of schoolingof the working-age population indicator (MYSachievement index). It has been calculated accordingto the following expression:

A2j(t) ¼2

3ADLI j(t)þ

1

3MYS j(t) (2)

In the latest Human Development Indices, the edu-cation index is usually calculated from the adult literacyrate and the combined primary, secondary and tertiarygross enrolment ratio. In the present case, due to theregional data available, this ratio was replaced withthe mean years of maximum attained schooling in theworking-age population. This variable (mean years ofschooling) represents a society’s human capital and isindicative of its level of cultural and productive progress.On the other hand, the adult literacy rate has been cal-culated as 100 minus the illiteracy rate of the working-age population.2 The adult literacy rate is thus a directmeasure of the level attained in one of the basichuman rights: a minimal education. In addition, theseare the same indicators and weights as used in the1994 Human Development Report.

Income index

Following the latest Human Development Reports,income index (achievement A3j) is measured accordingto GVA per capita, although in the present work theauthors were not able to adjust it by a suitable regionalpurchasing power parity index for the period. More-over, considering that unemployment has an impacton well-being far above the simple loss of incomeeffect (CLARK and OSWALD, 1994), the present paperalso included in the income component of the AHDIthe variable 100 minus the rate of long-term unemploy-ment (lasting 12 months or more) as an indicator ofsocial inclusion. In this way, there was an attempt tomeasure, within the context of Human DevelopmentIndices, all the dimensions of human development notreflected in the health and education sub-indicators.

Thus, the income index was measured by theweighted average of two indicators:

A3j(t) ¼2

3GVAPC j(t)þ

1

3INC j(t) (3)

The complementary indicator to the GVA per-capitaindex (GVAPC) is calculated as 100 minus the rate oflong-term unemployment with INC representing itsachievement index. In relation to the weights assignedto each indicator, it was considered that for developedcountries, GVAPC has more relative importance thanINC. Therefore, it was considered that from the pointof view of population living standards, the substitutionrate between GVAPC and INC is not equal to 1 andone cannot assign equal weights to both indicators.

Finally, the AHDI for region j and year t is obtainedfrom the following expression:

A j(t) ¼1

3A1j(t)þ

1

3A2j(t)þ

1

3A3j(t) (4)

However, in order to assess whether overall perform-ance of the AHDI is influenced by the weights givento each sub-indicator, an alternative measure of theAHDI is proposed according to:

Aj(t) ¼ 0:54A1j(t)þ 0:12A2j(t)þ 0:34A3j(t) (5)

The weights assigned to each index were calculated bytaking the Monthly Barometer Poll conducted by theCIS in December 2003 into account. In this survey,public opinion in Spain was examined regarding thoseaspects of a person’s life that provide greatest satisfaction.Thus, an attempt was made to include in the AHDI theindividual’s valuations of the three dimensions of humandevelopment being considered. In this way, the weightsgiven to each index correspond to the results of categor-izing these aspects within the three dimensions con-sidered in the HDI.

With regards to components A1j and A2j, theachievement index for each indicator (i) under con-sideration (LIFE, INFS, SURV, ADLI and MYS), forregion j and year t, as a value between 0 and 1, wasderived by applying the following general formula foreach year (MAZUMDAR, 1999):

Achievementij(t) ¼Xij(t)� Xi min

Xi max � Xi min

(6)

where Xij(t) is the raw value of indicator i in region jand year t; Ximin is the minimum value selected for indi-cator i; and Ximax is the maximum value selected forindicator i.

However, the income indicator should reflect thedecreasing returns produced when income is trans-formed into human capabilities. Thus, raw values of

474 Andres J. Marchante and Bienvenido Ortega

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the GVA per capita are adjusted by calculating its logar-ithm since a reasonable level of human developmentdoes not require unlimited income. The achievementindex for this indicator (GVAPC) is therefore calculatedfor region j and year t according to the followingexpression:

Achievement j(t) ¼log Xj(t)� log Xmin

log Xmax � log Xmin

(7)

whereas, in this case, Xj(t) is the GVA per capita inregion j and year t. The achievement index for thesocial inclusion indicator (INC) was also measured byapplying expression (6).

Following MAZUMDAR (1999) and HORREL (2000),there are two possible ways to select the upper and lowerbounds (goalposts) in expressions (6) and (7). Accordingto the first option, the maximum and minimum valuesshould be selected from the observed values in the data-base being used. Horrel, however, suggests selecting thebest practice, i.e. the value of the best positioned region.In the present study, the lower bounds of each indicatorwere taken directly from the observed (lowest) values inthe data employed. For the upper bounds, Horrel’s pro-posal was followed:3 the maximum values published inthe 2003 Human Development Index for life expect-ancy at birth, the maximum possible values for theinfant survival rate, probability at birth of surviving toage 60 years, adult literacy rate, mean years of schoolingand 100 minus the long-term unemployment rate.Finally, the GVAPC for Norway in 2001 was usedas the reference value, adjusted for the purchasing

power parity differences between Norway and Spain(Table 1).4

CHANGES IN THE STANDARD OF LIVING

OF THE SPANISH REGIONS: A

DESCRIPTIVE ANALYSIS

According to data in Table 2, six regions (Aragon,Cataluna, Madrid, Navarra, Paıs Vasco and La Rioja)have present levels above the Spanish average in GVAper capita and the AHDI in the years considered.Madrid and Navarra rank first and they swap placesdepending on the indicator. Andalucıa and Extrema-dura are at the bottom of the ranking regarding bothGVA per capita and AHDI.

In the intermediate positions, there are some signifi-cant changes in the ranking depending on the variable.Thus, among the regions with a GVA per capita closeto the Spanish weighted average, the Baleares arehigher in rank than Cantabria regarding GVA percapita; but regarding the AHDI, the opposite is thecase. Similar situations are observed among theregions with GVA per capita below the Spanishaverage, except in Andalucıa and Extremadura. Infact, Galicia and Murcia show changes in rankingdepending on each variable. Galicia is better positionedfor the AHDI than for GVA per capita, whereas this isthe reverse for Murcia.

Accordingly, this first calculation shows that there arenoticeable differences in the performance obtained forsome regions depending on the measure used to calcu-late the standard of living. Therefore, the link betweeneconomic prosperity and human development is neither

Table 1. Maximums and minimums in the sample of the indicators and upper bounds for the indices

Sample

Upper bounds

for the indices

Minimum Maximum

Region Year Value Region Year Value

Life expectancy at birth (years) Canarias 1980 74.24 Madrid 2001 81.40 85.00

Infant survival rate (‰) La Rioja 1981 983.99 Cantabria 2001 998.13 1000.00

Probability at birth of

surviving to age 60 (%)

Baleares 1985 83.27 Castilla-La

Mancha

2001 91.47 100.00

Adult literacy rate (%) Extremadura 1980 83.51 Cantabria 2001 99.85 100.00

Mean years of schooling (years) Extremadura 1980 2.89 Madrid 2001 8.81 17.00

100 minus the rate of long-term

unemployment (12 months

or more) (%)

Andalucıa 1994 81.49 Baleares 2001 98.90 100.00

Per-capita gross value added

(1990 constant E)

Extremadura 1980 3336 Navarra 2001 12 186 15 725

Note: The figure chosen as the upper bound for real per-capita gross value added (GVA) is derived from the estimations for Norway in 2001 (this

was the first country in Human Development Index (HDI) terms for that year according to the United Nations Development Programme

(UNDP)). This has been estimated while bearing in mind that, according to Eurostat in 2001 and in terms of purchasing power parity, the

per capita GVA in Norway was 144.4% of the European Union average, while for Spain this figure was estimated at 84.3% (E9180 at 1990

constant euros per capita).

Source: INE, IVIE and authors’ own work.

Quality of Life and Economic Convergence across Spanish Regions 475

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automatic nor obvious. Two regions with a similar GVAper capita can have very different achievements in theAHDI. However, not all regions have translated gainsin economic prosperity into a better standard of livingfor their inhabitants to the same degree. Nevertheless,one finds that the initial ranking of regions in theAHDI remained practically unaltered in 2001(Spearman’s coefficient of correlation = 0.956). Theseresults suggest that convergence in living standardsshould be analysed by taking into account, in additionto GVA per capita, other economic and social indicatorsthat reflect more realistically the new opportunities andareas of well-being enjoyed by Spanish society in recentdecades.

Table 3 shows the correlations between the threealternative income measures considered and theachievement indices of the AHDI. Note that the esti-mated correlation coefficients between per-capitaGVA, both versions of the AHDI and the income com-ponent of the AHDI are quite high. On the other hand,the correlation coefficients between the GVA per capitaand the education and health indices of the AHDI arelower. The fact that similar results are obtained foralternative income measures may reveal that the use ofTPI or gross personal disposable income (GPDI)instead of per-capita GVA in the construction of theAHDI should not significantly alter the results presentedin Table 2. In addition, an almost unitary coefficient of

correlation is estimated between the two versions of theAHDI considered. This finding is particularly signifi-cant as it indicates that the sensitivity of the achieve-ments of the AHDI to the weighting scheme chosenis reduced.

CONVERGENCE IN THE STANDARD OF

LIVING ACROSS SPANISH REGIONS: AN

ECONOMETRIC ANALYSIS

As stated above, in recent years a large number of studieshave focused on analysing whether there is a continuousprocess of convergence in income levels and well-beingbetween different European Union countries andregions. A large variety of analytical methods havebeen used, from measures describing inequality andpolarization over time, sample variance, standard devi-ation, Lorenz curve or the Gini index, to more recentmethods such as econometric models which, based ondifferent approaches, attempt to shed some light onthe subject. Finally, a different approach is associatedwith the study of mobility, focusing on whether therehave been positioning changes in income levels indifferent economies.

This section investigates the convergence of the stan-dard of living across Spanish regions in 1980–2001using two well-known approaches (b- and

Table 2. Indices of real gross value added (GVA) per capita and the augmented Human Development Index (AHDI) for theSpanish regions

Real GVA per capita AHDIa

1980 2001 1980 2001

%b Ranking % Ranking % Ranking % Ranking

Andalucıa 76.9 16 121.4 16 47.2 16 139.8 17

Aragon 102.6 8 176.7 6 118.9 7 196.7 7

Asturias (Principado de) 99.4 9 139.3 12 111.1 9 178.7 10

Baleares (Islas) 109.0 6 165.5 7 97.3 11 184.9 9

Canarias 89.5 13 141.9 11 67.3 14 154.0 14

Cantabria 102.6 7 157.3 8 131.4 3 198.6 5

Castilla y Leon 96.4 11 154.9 9 116.2 8 194.5 8

Castilla-La Mancha 78.0 15 130.2 14 66.3 15 156.1 13

Cataluna 116.4 5 190.1 4 127.9 4 197.9 6

Comunidad Valenciana 98.2 10 154.4 10 101.0 10 176.5 11

Extremadura 58.4 17 107.2 17 37.1 17 141.7 16

Galicia 79.7 14 129.3 15 87.9 12 173.0 12

Madrid (Comunidad de) 129.6 2 209.0 2 137.4 1 212.9 2

Murcia (Region de) 92.4 12 132.1 13 70.3 13 149.9 15

Navarra (Comunidad Foral de) 131.9 1 213.4 1 134.8 2 216.2 1

Paıs Vasco 128.4 3 207.0 3 122.4 6 204.1 3

Rioja (La) 119.0 4 188.8 5 123.7 5 199.5 4

Spain 100.0 – 160.3 – 100.0 – 179.5 –

Notes: aAHDI was calculated according to expression (4) by taking, for each component, the minimum value observed in the sample as the lower

bound, and as the upper bound the values in Table 1.bSpain in 1980 ¼ 100.

Source: INE, IVIE and authors’ own work.

476 Andres J. Marchante and Bienvenido Ortega

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s-convergences). In this way, it employs different econ-omic indicators and the AHDI with the aim that theconvergence process of each variable should be judgedaccording to the same criteria.

b-Convergence

The analysis of unconditional b-convergence wascarried out according to BAUMOL (1986) and BARRO

and SALA-I-MARTIN (1999) as follows:

D ln Yit ¼ aþ b ln Yit�1 (8)

where i (¼1, . . ., N ) is the regional unit, t (¼1, . . ., T )is the year, and Y is GVA per capita or another econ-omic, social or quality-of-life indicator. Parameter a isa constant term across regions; parameter b capturesthe effect of the initial level of each indicator on itsrate of growth as:

b ¼ 1þ exp (�b) (9)

where b is the speed of convergence to a common equi-librium level.

In addition to the variables included in equation (8),the estimates encompass time-specific effects, whichimplies that the level of Y is not stationary and theyallow for the estimation of equation (8) using paneldata. Therefore, model (8) is specified for the estimationaccording to equation (10):

D ln Yit ¼ aþ b ln Yit�1 þXT�1

t¼1

dt þ uit (10)

whereP

dt is a set of dummy variables representing eachyear (i.e. 1, . . ., T – 1 to avoid singularity) and uit is the

random disturbance term, serially uncorrelated, withN(0, s2) identically and independently distributed, s2

being constant.The present paper is only interested in analysing the

unconditional b-convergence of each indicator to findout whether the gap between the rich and poor regionsis closing. It also applies this analysis to economic as wellas social indicators, for which there is no theorysuggesting additional variables that possibly influencetheir growth paths. Hence, to compare the estimationresults, one must be limited to the unconditionalb-convergence analysis according to equation (10).Consequently, it is assumed for economic as well associal indicators that rejecting the null hypothesis b ¼ 0versus ,0 in equation (10) implies b-convergence (i.e.that relatively poor regions have improved the mostand are catching up with the leading regions).

This analysis employs three different economicindicators: GVA, TPI and GPDI, all of which are on aper-capita basis. GVA is an indicator of regional globaleconomic activity and obviously includes corporatesavings and public-sector incomes (dividends, interestand other incomes). This aggregate also ascribesincomes to the region where they are generated,whereas the TPI and GPDI aggregates concern personaleconomy since they represent the total income obtainedby households and private non-profit institutionsserving households, independently of the regionwhere they are produced. Thus, given the aims of thispaper, TPI and GPDI are more relevant when analysingthe economic welfare of the population in each region.

Table 4 shows the unconditional b-convergenceestimation results (from equation 10) and Table 5 theircorresponding speeds of convergence (b from equation9) and the half-life for GVA, TPI and GPDI. There is anabsence of convergence in per-capita GVA, whereas in

Table 3. Matrix of correlations for the Spanish regions, 1980–2001

Per-capita

real gross value

added

(GVA)

Per-capita

total personal

income minus

current grants

(TPI)

Per-capita

gross personal

disposable

income

(GPDI)

Augmented

Human

Development

Index

(AHDI)a

Health

index

(A1)

Education

index

(A2)

Income

index

(A3)

Per-capita total

personal

income minus

current

grants (TPI)

0.973 (0.000) 1 – – – – –

Per-capita GPDI 0.962 (0.000) 0.985 (0.000) 1 – – –

AHDIa 0.917 (0.000) 0.906 (0.000) 0.930 (0.000) 1 – – –

Health index (A1) 0.640 (0.000) 0.655 (0.000) 0.701 (0.000) 0.750 (0.000) 1 – –

Education index (A2) 0.754 (0.000) 0.739 (0.000) 0.760 (0.000) 0.880 (0.000) 0.458 (0.000) 1 –

Income index (A3) 0.891 (0.000) 0.867 (0.000) 0.861 (0.000) 0.845 (0.000) 0.499 (0.000) 0.625 (0.000) 1

AHDIb 0.888 (0.000) 0.884 (0.000) 0.913 (0.000) 0.960 (0.000) 0.885 (0.000) 0.722 (0.000) 0.823 (0.000)

Notes: Pearson’s bivariate correlation coefficient was calculated for the 374 available observations, corresponding to the 17 regions. The p-value of

the coefficient is given in parentheses.aAHDI was calculated according to expression (4): weight 0.3 for each component.bAHDI was calculated according to expression (5): weight 0.54 for A1, 0.12 for A2 and 0.34 for A3 component.

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TPI and GPDI, the evidence shows the opposite is true:in both cases, the null hypothesis of no convergence(b ¼ 0 versus , 0) is rejected. Thus, the estimationssuggest the existence of unconditional convergence inboth per-capita TPI (with an average speed of conver-gence of 1.07% per annum) and per-capita GPDI(with a rate somewhat higher at 1.14% per annum).These speeds of convergence are lower than usual (2%per annum), but reveal that the convergence process,although slow, has not stopped in the last twodecades, in contrast to research using per-capita GVAas the indicator.5 Moreover, current grants do nothelp to increase the rate of reduction of regional dispar-ities in the period: the rates of convergence of both TPIand GPDI per capita are not statistically different.

Tables 4 and 5 also show the results obtained fromalternative quality-of-life indicators. Note that regionalconvergence in the AHDI is very high (the rate of con-vergence is estimated at 5.75% per annum). This findingreveals the sharp differences in the conclusions that can

be obtained when analysing the process of regional con-vergence in living standards when using different indi-cators, such as per-capita GVA or AHDI. It is alsointeresting to note that the results obtained for both ver-sions of the AHDI are not statistically different.

Previous research has emphasized the need to analyseconvergence for the raw data of each socio-economicindicator that was combined in the construction of theAHDI (HOBIJN and FRANSES, 2001; MAZUMDAR,2003). In fact, although the construction of a compositeindex of living standards allows for direct comparisonacross regions, their use involves some drawbacks(SAISANA et al., 2005). First, combining a wide varietyof indicators, each one measured in different units,makes the composite index difficult to interpret.Second, the choice of the indicators and weights foreach component of the composite index may be subjectto disagreement among researchers. Due to these con-siderations, the estimation results of equation (10) aregiven in Tables 4 and 5 for each social indicator separately.

Table 4. Unconditional b-convergence estimations across the Spanish regions, 1980–2001

b Coefficient

( p)

AR(1)

coefficient

( p)

LM(1)

F-statistic

( p)

Adjusted

R2

Chow

F-statistic

( p) NT

Gross value added (GVA)

at factor cost per capita

20.0079 (0.2474) – 0.1898 (0.6634) 0.2924 2.1204 (0.1463) 357

Total personal income minus

current grants (TPI)

per capita

20.0106 (0.0078) – 0.1308 (0.7178) 0.6760 1.8047 (0.1801) 357

Gross personal disposable

income (GPDI)

per capita

20.0113 (0.0178) –0.1701 (0.0603) 0.0951 (0.7580) 0.6067 0.4783 (0.4897) 340

Augmented Human

Development

Index (AHDI)

20.0559 (0.0000) 20.2736 (0.0003) 1.9117 (0.1679) 0.3727 1.9673 (0.1617) 340a

20.0542 (0.0000) 20.3564 (0.0000) 1.0917 (0.2961) 0.3149 1.7275 (0.1897) 340b

Life expectancy at

birth (years)

20.0145 (0.5183) 0.8529 (0.0000) 2.1870 (0.1403) 0.7611 0.3970 (0.5291) 340

Infant survival rate (‰) 20.2445 (0.0000) 20.3577 (0.0000) 2.0129 (0.1570) 0.3240 0.7085 (0.4006) 340

Probability at birth

of surviving to

age 60 years (%)

20.0819 (0.1301) 0.8340 (0.0000) 0.8364 (0.3612) 0.8289 1.2972 (0.2555) 340

Adult literacy rate (%) 20.0392 (0.0000) 0.3482 (0.0000) 0.6003 (0.4385) 0.3781 0.5584 (0.4555) 340

Mean years of schooling

(years)

20.0475 (0.0000) 0.3510 (0.0000) 0.0713 (0.7897) 0.6677 0.2134 (0.6441) 340

100 minus the rate of

long-term

unemployment

(12 months or more) (%)

20.0663 (0.0012) – 1.2345 (0.2674) 0.6193 88.7796 (0.0000) 357

20.0450 (0.1346) – – 0.5227 – 170c

20.0832 (0.0033) – – 0.6597 – 187d

Notes: The estimations were carried out using annual data for 1980–2001 for a panel formed by the 17 Spanish regions (NUTS II). The standard

errors and covariance are White heteroscedasticity-consistent. Only selected coefficients and diagnostics are reported. All estimations consider a

time-specific effect by the inclusion in the estimations of additional (T 21) dummy variables for each year. NT, total panel observations. LM(1),

Lagrange Multiplier test of first-order serial correlation of the residuals based on a modification of the Breusch–Godfrey test valid in the presence

of lagged dependent variables (GREENE, 1997, p. 597). An autocorrelation coefficient of order 1 (AR(1)) was estimated when a serial correlation

was detected in the base model. Chow is the test for b-coefficient stability before and after 1990. The F-statistic was obtained by using the dummy

variable approach (GUAJARATI, 1970).aAHDI was calculated according to expression (4): weight 0.3 for each component.bAHDI was calculated according to expression (5): weight 0.54 for A1, 0.12 for A2 and 0.34 for A3 component.c1980–90.d1991–2001.

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The results show that convergence in raw data wasachieved across Spanish regions in four of six selectedindicators. Only in the indicators life expectancy atbirth and the probability at birth of surviving to age60 years can the null hypothesis of absence of conver-gence (b ¼ 0) not be rejected. This might beexplained by the fact that as early as 1980, mostregions had reached high values of both indicators.At these levels, any advances can only be small, andin the case of life expectancy at birth, within biologi-cal limits. In contrast, the infant survival rate showsconvergence, in spite of the high values that this indi-cator presented in 1980. In this case, similar levels ofthis indicator have been reached across Spanishregions, with only minor differences being observed.In addition, high unconditional b-convergence wasdetected in the education indicators. The speed ofconvergence is calculated at 4.0% per annum for theadult literacy rate and 4.78% for the mean years ofschooling, having corresponding half-lives of 17 and14 years, respectively. Finally, similar findings can beobtained for the 100 minus the rate of long-termunemployment.

The results suggest that, with the exception of twoindicators, and for the reasons outlined above, theremaining four indicators reveal the existence of astrong process of b-convergence in the standard ofliving across Spanish regions.

However, according to ARMSTRONG (1995), it ispossible that the rate of convergence has varied overtime within the complete period 1980–2001. To assessthis, Chow’s test was used to study the stability of theb-coefficient in equation (10). Table 4 shows that thereis parameter stability in all cases except for the rate oflong-term unemployment. In this case, one can rejectthat the estimated parameter b in equation (10) is equalin the two subsamples considered (1980–90 and

1991–2001). Moreover, Tables 4 and 5 show thatwhereas no convergence of this indicator is detectedbefore 1990, the annual speed of convergence isestimated at 8.68% per annum after 1990.

s-Convergence

The concept of s-convergence refers to the evolutionof the cross-sectional dispersion of a variable overtime. From this perspective, convergence exists if thedispersion of the indicator under study – measured,for example, by the standard deviation of the log ofthe indicator or by the coefficient of variation (CV) –diminishes over time (BARRO and SALA-I-MARTIN,1999). In addition, following O’LEARY (2001), therate of s-convergence can be calculated as the annualpercentage of change between a measure of dispersionat a given moment in time (t + T ) and this measure cal-culated at moment (t), where a negative (positive) valueimplies s-convergence (divergence).

The result of calculating the coefficient of variationfor each of the economic and quality-of-life indicatorsused is presented in Fig. 1 and Table 6. In the case ofper-capita GVA, the coefficient of variation was prac-tically the same in 1980 and 2001 (20.47 and 20.50%,respectively) and the annual mean CV rate of diver-gence was calculated as 0.01%. This finding is inline with that obtained above from the perspective ofunconditional b-convergence. With regards to per-capita TPI and GPDI, the results of s-convergencealso support the hypothesis of b-convergence. Inboth cases, despite any temporary disturbances, therate of s-convergence of per-capita TPI is 0.58%per annum and that of per-capita GPDI is 0.56%per annum. However, as DOLADO et al. (1994)point out, the dispersion of GPDI per capita is stea-dily lower than that of TPI. This can be accounted

Table 5. b-Convergence across Spanish regions, 1980–2001

Annual speed of

convergence (%) Half-life (years)

Gross value added at factor cost per capita – –

Total personal income minus current grants per capita 1.07 65.34

Gross personal disposable income per capita 1.14 61.33

Augmented Human Development Index (AHDI) 5.75a 12.40

5.57b 12.79

Life expectancy at birth (years) – –

Infant survival rate (‰) 28.04 2.83

Probability at birth of surviving to age 60 years (%) – –

Adult literacy rate (%) 4.00 17.68

Mean years of schooling (years) 4.87 14.59

100 minus the rate of long-term unemployment

(12 months or more) (%)

–c

8.68d–

8.34

Notes: aAHDI was calculated according to expression (4): weight 0.3 for each component.bAHDI was calculated according to expression (5): weight 0.54 for A1, 0.12 for A2 and 0.34 for A3 component.c1980–90.d1991–2001.

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for by the fact that the behaviour of the public sectorhas compensated for regional disparities by means ofdirect taxation, national insurance contributions andcurrent grants. However, one has to recall thatcurrent grants did not affect the b-convergence ratesignificantly over the period.

With regard to social indicators, the high rate ofs-convergence of the AHDI (2.43–2.71% perannum), adult literacy rate (2.3% per annum) andmean years of schooling (2.6% per annum) is note-worthy. In addition, the reduced coefficient of variationcalculated for the infant survival rate and the probability

Fig. 1. s-Convergence across Spanish regions, 1980–2001The augmented Human Development Index (AHDIa) was calculated according to expression (4): weight 0.3 for each component.

Sources: INE, IVIE and authors’ work

Table 6. s-Convergence estimations across Spanish regions, 1980–2001

CV rate of s-convergence (% per

annum)

CV

maximum

(years)

CV

minimum

(years)

CV in

1980

CV in

1990

CV in

2001 1980–90 1990–2001 1980–2001

Gross value added at

factor cost per capita

20.4695 19.3275 20.5038 –0.56 0.55 0.01 21.1525 (1981) 18.5153 (1988)

Total personal income

minus current

grants per capita

21.7619 19.9282 19.1218 –0.84 –0.37 –0.58 21.7619 (1980) 19.1218 (2001)

Gross personal disposable

income per capita

16.4350 15.5345 14.4943 –0.55 –0.61 –0.56 16.7559 (1985) 14.4943 (2001)

Augmented Human

Development

Index (AHDI)a

31.8291 20.3325 13.6920 –3.61 –2.42 –2.43 31.8291 (1980) 13.6920 (2001)

AHDIb 24.8706 16.5956 12.1792 –3.33 –2.97 –2.71 24.8706 (1980) 12.1792 (2001)

Life expectancy at birth

(years)

1.0305 0.9891 1.3554 –0.40 3.37 1.50 1.3554 (2001) 0.9707 (1982)

Infant survival rate (‰) 0.1671 0.1187 0.1110 –2.90 –0.59 –1.60 0.2191 (1987) 0.0913 (1998)

Probability at birth of

surviving to

age years 60 (%)

1.2455 1.1176 1.1605 –1.03 0.38 –0.32 1.3112 (1985) 0.9668 (1996)

Adult literacy rate (%) 5.6242 4.1905 2.9077 –2.55 –2.78 –2.30 5.6242 (1980) 2.6128 (1999)

Mean years of schooling

(years)

20.3264 13.6175 9.2234 –3.30 –2.93 –2.60 20.3264 (1980) 8.9986 (1999)

100 minus the rate of

long-term

unemployment (12

months or more) (%)

1.5754 2.9475 1.6616 8.71 –4.36 0.26 3.6705 (1995) 1.5754 (1980)

Notes: CV, coefficient of variation across regions.aAHDI was calculated according to expression (4): weight 0.3 for each component.bAHDI was calculated according to expression (5): weight 0.54 for A1, 0.12 for A2 and 0.34 for A3 component.

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at birth of surviving to age 60 years in Table 6 reveals ahigh degree of homogeneity reached in public healthperformance (due to insurance coverage) acrossSpanish regions. Finally, in the case of 100 minus therate of long-term unemployment, it is significantthat in line with findings regarding b-convergence,s-convergence has only been reached since 1990 aftera period of strong divergence.

CONCLUSIONS

This work proposes alternative indicators to GVA percapita to measure the development of the standard ofliving across Spanish regions. These proposed indicatorsare a response to the many criticisms of GDP per capitaas a measure of the standard of living of the population.This perspective is basically inspired by Amartya Sen’swork, for whom development was understood to bethe process of expansion of the actual liberties individ-uals enjoy in society. From this perspective, the import-ance of economic growth as an indicator of well-beingis relative, and wealth accumulation and what can beconsidered a ‘good life’ become dissociated. In thiscontext, this work proposes an augmented version ofthe Human Development Index (AHDI), adapted tothe Spanish economy. This kind of index has beenapplied by the UNDP for more than 10 years, and,although it has its limitations, as does any other compo-site indicator (IVANOVA et al., 1999), it is normally usedto measure the living standard and its evolution in bothdeveloping and fully developed countries. Thus, it wasconsidered to be interesting to estimate its value for allSpanish regions and its results compared with GVAper capita and alternative income measures. From thisanalysis, one can highlight the following results:

. There are significant differences in the performanceobtained for some regions depending on themeasure considered: regions with similar GVA percapita can have very different achievements in theAHDI.

. There is a high linear correlation between theachievements of the AHDI and each of itscomponents.

. There is a high correlation between levels of theincome measures and achievements of the AHDI.

. There are no differences in the coefficient of corre-lation between each of the alternative incomemeasures and the health and education componentsof the AHDI. Additionally, a correlation coefficientclose to the unit has been estimated between thetwo versions of the AHDI considered. Therefore,these findings may show a reduced sensitivity of theachievements of the AHDI to the income measureconsidered and to the chosen weighting scheme.

Additionally, it was attempted to extend the currentviews regarding regional convergence of the standard

of living amongst Spanish regions by analysing the con-vergence process in three economic measures (GVA,TPI and GPDI, all in per-capita terms), two versionsof the AHDI, and the raw values of each indicatoremployed in the construction of the AHDI. It isimportant to note that the analysis was limited to theunconditional b-convergence (besides s-convergence)because there is no theory for social indicatorssuggesting factors conditioning their growth paths,and the convergence process of each variable consideredshould be judged on the same criteria. Thus, we canconclude the following:

. The results indicate a lack of convergence in regionalGVA per capita, whereas per-capita TPI and GPDIkept converging, but at a lower rate than usual.Therefore, according to these economic indicators,convergence has not stopped in the last twodecades. These results suggest that incomes obtainedby the personal sector, measured either by TPI orby GPDI, might be more relevant indicators of thelevel and change in the standard of living.

. The dispersion of GPDI per capita is steadily lowerthan that of TPI, but current grants did not affectthe speed of b-convergence significantly over theperiod.

Finally, in line with the descriptive analysis givenherein, a strong process of convergence has beendetected in AHDI. The results also suggest that conver-gence was achieved (both b and s) across Spanishregions in three of six selected quality-of-life indicators(infant survival rate, adult literacy rate and mean years ofschooling of working-age population). In the case of100 minus the rate of long-term unemployment, con-vergence has been detected only since 1990.

Thus, in the last 20 years, whereas convergence interms of GVA per capita has shown stagnation,Spanish regions are converging to a high degree whenmeasured by the alternative indicators proposed in thiswork. It is worth noting the effect that public sectorredistribution has on the progress achieved in educationand health for Spanish society as a whole. The publicsector has strongly supported equality in living stan-dards. Thus, ongoing progress in education and healthhas to be guaranteed in order to reduce interregionalinequalities.

Finally, the analysis carried out in this work maybe useful in showing that a version of the compositeindex used by the UNDP provides a picture of theregions’ relative performance that differs from thatobtained using income measures. The robustness ofthese results has been confirmed by using alternativeweighting schemes in the AHDI and by analysing thecorrelation between alternative income measures toper-capita GVA and the health and education com-ponents of the AHDI. In addition, to reduce the possi-bility that this composite index may present amisleading picture, only the normalization method,

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weighting approaches and raw indicators were usedwithin the context of the UNDP human developmentindices.

Acknowledgements – The authors thank Alicia Aviles

for help in linking the GVA at factor cost series with the cor-

responding deflators; and Jose Luis Sanchez for building the

series of the mean years of schooling of the working-age

population. Thanks are also due to the participants at the

34th Annual Conference of the RSAI British and Irish

Section, University College Cork, Cork, Ireland, for valuable

suggestions on a previous draft of this paper. They are also

grateful to reviewers for valuable comments.

APPENDIX

The official statistics employed in the construction ofthe database used in this work are as follows:

. Regional Accounts in Spain, base 1995; Spanish Stat-istical Office – National Statistics Institute (INE),TEMPUS database (http://www.ine.es).

. Regional Accounts in Spain, base 1986, 1986–96linked series; INE.

. Regional Accounts in Spain, base 1980, 1980–87series; INE.

. National Accounting of Spain, bases 1980 and 1986,several issues; INE.

. Population Census, 1980, 1990/99 and 2001; INE.

. Intercensus Population Estimates; INE.

. Economically Active Population Survey, 1980–2001series; INE.

. Life Expectancy at Birth, 1980, 1985, 1990, 1995 and1998, main demographic indicators; INE.

. Infant Mortality Rate, 1980–2001 series, maindemographic indicators; INE.

. Mortality Tables of the Spanish Population, 1980/81,1985/86, 1990/91, 1994/95 and 1998/99; INE.

. BD.MORES database (Ministerio de Economıa yHacienda, i.e. the Ministry of Economy and PublicFinance; http://www.sepg.minhac.es).

. Eurostat-NewCronos database, 2001 (http://europa.eu.int/comm/index_en.htm).

. Spanish National Income and its Provincial Distri-bution (Renta Nacional de Espana y su DistribucionProvincial, Series Homogeneas), Vol. I; FundacionBBVA (w3.grupobbva.com).

. Human Capital Series, 1964–2001; Valencian Insti-tute of Economic Research (IVIE) (http://www.ivie.es).

NOTES

1. If the maximum and minimum values from the last report

had been selected, the differences among the Spanish

regions would be minimal.

2. Individuals of 16 years of age or over who meet any of the

following conditions are classified as illiterate (MAS et al.,

1995): they cannot read or write; they can read but not

write; they can read and write one or several phrases

that they have memorized; and they can only read and

write numbers or their own name. This definition of illit-

eracy has been used in the database from which this indi-

cator has been constructed. For a complete description of

the database construction, see MARCHANTE and ORTEGA

(2004).

3. The achievement indices of the indicators have also been

calculated following Mazumdar’s proposal. An almost

unitary correlation coefficient is estimated between the

two versions of the AHDI, which reveals the reduced sen-

sitivity of achievements of the AHDI to the selected upper

bounds.

4. According to the 2003 Human Development Report,

Norway was the world leader. Its per-capita GDP in

2001 was the second highest in the European Union,

after Luxembourg.

5. Observed differences between per-capita GVA and per-

capita TPI convergence seem to refute the postulates of

BARRO and SALA-I-MARTIN (1992). According to these

authors, if imperfect mobility of capital exists, much

faster regional convergence have to be observed for per-

capita output (GVA) than for TPI per-capita because

investment income obtained by rich regions in poor

regions reverts to rich ones. However, if labour mobility

exists as well, emigrants’ remittances will have the opposite

effect. Unfortunately, the information required to disen-

tangle both effects is not available; furthermore, it is not

an aim of this paper. The results are similar to those

obtained by COULOMBE and LEE (1995) for Canada.

REFERENCES

ARMSTRONG H. W. (1995) An appraisal of the evidence from cross-sectional analysis of the regional growth process within the

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