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VICENTE ROYUELA and JORDI SURIN ACH

CONSTITUENTS OF QUALITY OF LIFE AND

URBAN SIZE

(Accepted 20 December 2004)

ABSTRACT. Do cities have an optimal size? In seeking to answer this question,

various theories, including Optimal City Size Theory, the supply-oriented dynamic

approach and the city network paradigm, have been forwarded that considered a

citys population size as a determinant of location costs and benefits. However, the

generalised growth in wealth that has been experienced over the last 50 years in

developed countries has changed what have traditionally been seen as mans needs.

Thus, Ingleharts post-materialist approach and Maslows theory of human needs

force us to re-examine the traditional costs and benefits of cities. Here, we assume

that costs and benefits enter the utility function of households through the quality of

life concept. The relation between the constituents of quality of life and traditional

and new theories of city size are considered here. Finally, we test these relations

empirically in a specific dynamic, local framework: the city of Barcelona (Spain) inthe period 19912000.

INTRODUCTION

Economic studies have long been concerned with seeking to under-

stand why people prefer living in cities (Christaller, 1933; Losch,

1940; von Thu nen, 1826), although until Alonso (1964) no systematic

micro-economic analysis of the question had been undertaken.

Today, some three billion people worldwide live in an urban centre

(a population of more than 1000 people) and by 2030 that number isset to increase to five billion. Another clear indicator of this phe-

nomenon is that the percentage of people living in cities in North

America, South America, Europe, and Japan stands at between 75

and 85%. There are, currently, 17 megacities around the globe: 11 of

which are located in Asia, while the ones experiencing the most rapid

growth are located in the tropics. The United Nations Population

Division predicts the addition of four new megacities to this total by

2015, namely Tianjin, Istanbul, Cairo, and Lagos. According to

Social Indicators Research (2005) 74: 549572 Springer 2005DOI 10.1007/s11205-004-8210-0


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forecasts from the World Resources Institute (1994), the percentage

of people living in cities is expected to rise even further in the

forthcoming decades.

People tend to concentrate in urban areas as they seek to satisfy

their needs, and territorially speaking this can be best achieved by

living in cities. In classic economics, the location of an individuals

residence is studied in a static framework, in which the structure of

the city is linear and where there is just one centre (the central

business district). In this traditional model, urban size is considered

to be the result of the equilibrium between production benefits and

location costs. As these benefits and costs are, by definition, the same

for all cities, every urban centre should be the same size.

In taking this classical analysis a stage further, Henderson (1985)

pointed out that cities produce different goods according to their size,

which gives rise to externalities. As a result, different urban sizes

develop reflecting these externalities related to the higher productivity

that the agents enjoy by being nearer to other producers or other

market agents. The inhabitants of larger cities enjoy additional

benefits as a consequence of being resident there. However, there are

certain amenities that are affected negatively as urban size increases:lower levels of environmental quality and increasing congestion,

among others. Here, again, an equilibrium between benefits and costs

means that there is an optimal urban size.

It should be noted that increasing city size contradicts optimal

city size theory, which holds that the advantages of agglomeration

are weakened as a citys physical dimensions expand. According to

this theory, medium-sized towns can be expected to grow in size, since

the advantages associated with their physical dimensions are still

greater than their location costs. Richardson (1972) called this into

question, arguing that there are other determinants influencing urban

agglomeration economies, in addition to physical size. This criticismwas incorporated in Capello and Camagni (2000), who assumed: (a)

the influence of a citys physical size on its optimal size; but also took

into consideration (b) the neoclassical and Christallerian city, com-

plemented with the supply-oriented dynamic approach (Camagni

et al., 1989), when analysing the different functions of each city, and

(c) the network city paradigm (Camagni, 1993; Camagni and de

Blasio, 1993) when seeking to explain why small or medium-sized

cities might have higher-order functions.1

VICENTE ROYUELA AND JORDI SURIN ACH550


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Here, also, we incorporate all three approaches, in particular the

fact that a citys benefits and costs are influenced by its size. However,

we recognise that this representation is simplistic, as many other

forces have a role to play. In order to obtain a holistic view, we

consider a key concept: quality of life. Theoretically, we understand

that all inhabitants in a region choose where they will live by seeking

to maximise utility, a function in which the concept of quality of life

is explicitly included (Giannias et al., 1999; Clark et al., 1988). In

building a theoretical framework for this study, we explain the con-

cept of quality of life in terms of Maslows theory of human needs.

This leads to a reformulation of the way in which amenities and

disamenities are considered in order to test the effect of city size.

Finally, our objective is to test the influence of the components of

quality of life on the city size theory in a local framework. Thus, we

assume that city size is related to flows of migration, and that these

occur more frequently within metropolitan areas than between them.

In a relatively short period of time lets say 10 years a smaller

territorial area would be more appropriate. Moreover, in the local

framework of Spain, local migration is much more frequent than long

distance flows, although clearly the critical factors that influence thesemovements are not the same as those influencing movements between

metropolitan areas. This said, however, our procedure is not invali-

dated, but rather enables us to conduct our future studies in a range

of other territorial dimensions. In any case, we assume that our

analysis do not pretend to analyze city growth in the world, and that

our estimates are strictly limited to Barcelona and similar locales.

In order to strengthen the territorial scope of our analysis, two

contrary economic forces can be considered to be operating spa-

tially: relative advantage and absolute advantage. The former, a

frequent assumption in an international framework, is important

when labour is not mobile and when parity between currencies canfluctuate. In a national framework, however, these two factors are

considered unimportant, and as such, the absolute advantage takes

on greater significance. Yet, migration between metropolitan areas

is not a common phenomenon in the case of Spain, where various

fiscal mechanisms operating at the national level mean that the

absolute advantages of the regions are eliminated. Consequently,

the importance of absolute advantage is much more marked at the

local than at the regional level.

CONSTITUENTS OF QUALITY OF LIFE AND URBAN SIZE 551


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Thus, the main objectives of this study are :

to incorporate quality of life theory within the relationship of

amenities and disamenities that influence city size;

to test this empirically within a local framework

CITY SIZE, AMENITIES AND DISAMENITIES,

AND QUALITY OF LIFE

As discussed above, urban size can be seen as being the result of

market forces that seek to maximise utility levels for residents and

profits for firms. In the optimal city size theory, optimal size is

computed as the result of maximum difference between a location

cost curve and the aggregate agglomeration advantage curve (Fig-

ure 1). Both utility and profits are affected by a diverse set of con-

flicting amenities and disamenities. If the equilibrium between them is

higher than that in other locations, reasonable individuals will choose

to live there. By contrast, if this equilibrium is negative or lower than

that in other locations, people will move out. This is the mechanism

underlying the growth or decline of a city. It may be the case that a

city has benefit curves due to their function in the territorial system

(Figure 2, depicts the neoclassical supply-oriented dynamic ap-

proach).

Thus, it can be seen that size influences the number of amenities

and disamenities in a city, which in turn influences city size. Similarly,

it can be seen that every cost or benefit may be characterised by an

optimal point in relation to city size. On just this issue, Burnell and

Benefits

Costs

B, C

D

Figure 1. The optimal city size theory.



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Galster (1992) raise an interesting question: At what population

may the disamenities of large size begin to outweigh the productivity

advantages?.

This questions has typically been addressed by regressing different

measurements of benefits and costs on linear, or more complex,

representations of city size:

Costs f Size, Other factors 1

Benefits f Size, Other factors 2

It should be noted that these costs and benefits have traditionally

been considered as economic factors with undoubted significance at

the territorial level. Yet, non-economic factors are also important

in the making of decisions concerning location. Thus, it can be

seen that many advanced industrial societies have been able to

increase their level of material well-being dramatically. This has

given rise to the need to take into consideration post-materialist

values, which view economic factors merely in relative terms within

a much more complex vision of what drives peoples decision

making (Inglehart, 1990)2

. Thus, economic factors, such as distancefrom the central business district, may simply be another factor

that needs to be taken into consideration when a household is

pondering where to locate its residence. It is at this juncture, and

in order to be able to comprehend fully the definition of quality of

life, that the concept of human needs should be introduced. Thus,

we can make an assumption: man is constantly striving to better

himself, which assumes that certain needs have already been sat-

isfied as a basis for seeking to satisfy other needs. And these new

B3

Costs

Bi, C

D

B2

B1

Figure 2. Neoclassical supply-oriented dynamic approach.



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social needs have to be interpreted as a new means of satisfying the

eternal needs we face in a new environment. Finally, the doubt

remains, however: Do these needs really include everything that we

express as needs?

Maslows (1975) theory of human needs identifies five different

kinds of needs, ordered from the objective to the subjective: (1)

physiologic needs, (2) health and security, (3) ownership and love,

(4) the need to be loved, and (5) self-realisation. In line with this

theory, once we have satisfied the more basic, objective needs, we

are then ready to try to fulfil our more spiritual needs. However, the

linear nature that Maslow gives to his classification has been called

into question by more than one author (Doyal and Gough, 1994),

while some have sought to classify needs in line with Marxist

thinking (Heller, 1978), and others have forwarded their own clas-

sifications. Thus, there is no consensus concerning the nature, or the

definition, of human needs. Therefore, following Royuela and Su-

rin ach (2003), here, we take for granted the fact that if mans

intention is to optimise these needs, we should be concerned with

considering the overall number of needs. It is here where the con-

cept of quality of life arises.Following Liu (1978), we understand quality of life in its social

sense, that is: The optimal level of quality of life is produced only by

combining both the physical and psychological inputs (). There-

fore, the quality of life that each individual perceives is assumed to be

directly dependent on his capability constraints to exchange and to

acquire, while the major concern for a society is how to improve an

individuals capability by shifting the constraint curve outward to the

right. Additionally, following Dasgupta and Weale (1992), quality

of life not only considers the constituents of well-being (health,

welfare, freedom of choice, basic liberties) but also the determinants

of well-being (availability of food, clothing, potable water, educationfacilities, health care and income in general). Thus, social welfare is

not only considered from the perspective of each individual but also

from that of society seen as a collective group; the opportunities

enjoyed by this group are at least as important as those enjoyed by

the individual.

Quality of life is a multidimensional concept. According to Wish

(1986), there may be many vectors to consider, and we will need to

study all of them if we are to obtain a global definition of the quality



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of life. On the basis of this assumption, instead of computing func-

tions (1) to (2), we are concerned with the following function:

Quality of Life Component i f Size, Other factors 3

where the Quality of Life Component iincludes all the constituents of

quality of life. The variables denoted as Other factors are those that

enable different functions to be taken into consideration for each city

and the network city paradigm. Thus, as a first step, we understand

that all the constituents of quality of life may be related to city size or

city function theories. Clearly, this is not always the case. Climate, for

instance, can be seen as a constituent of well being, but it is not

directly dependent on city size or city function or the place of the city

in the global network. Below, we test the relation between each

constituent of quality of life and these theoretical variables.

CITY SIZE, AND QUALITY OF LIFE IN THE BARCELONA

METROPOLITAN AREA

The Local Environment

As Wish (1986) points out, even within the city, especially in the

largest urban areas, there are acute differences. We analyse these in a

local framework, within municipalities. Our study is undertaken in

the province of Barcelona (NUTS III in the European administrative

classification, and the largest province in the region of Catalonia,

NUTS II), which is one of Spains most developed regions, located in

the north-east of the country, and bordering France. The province of

Barcelona had a population of 4,805,927 inhabitants in 2001 and is,

together with Madrid, Spains most populated and urbanised prov-

ince. It has 314 municipalities, organised in 11 administrative groups,

named comarques. These municipalities are the basic unit of mea-surement in our study. Describing territorial groups is a key element

in this study; elsewhere, we have used different territorial groups

defined as urban systems and urban subsystems (see Arts et al.,

1999). These aggregations were developed following commuting and

service area criteria.

Our local framework of 314 municipalities can be grouped in three

territorial dimensions: urban subsystems (of which there are 48), ur-

ban systems (24), and comarques (11). The 24 urban systems and their



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subsystems (if the former can be partitioned), together with their

respective sizes, are shown in Table I. Figure 3 also shows the dis-

tribution of the population among these urban subsystems, giving a

Gini index of 0.54. This indicates that a substantial part of the total

population is concentrated in a small number of municipalities: the

city of Barcelona accounted for 31% of the total population of the

province in the 2001 census. There are also differences in terms of

urban development. Some systems and subsystems (those nearest

Barcelona) are best described as urban areas or simply cities, while

others (those furthest from Barcelona) can be considered rural areas.

The province is similar to other areas in Europe, in which a large city

has a relatively wide area of influence: its suburbs, its surrounding

towns, industrial clusters, and so on.

The main characteristic used in defining the systems or subsystems

is not their homogeneity in terms of size, but the fact that they clearly

form separate areas on the basis of commuting and services criteria. 3

The Data

In Royuela et al. (2003), the quality of life of these 314 municipalitieslying in the Barcelona province is analysed. Here we use the same

extensive database4, and 18 basic quality of life components (see

Table II). In the aforementioned study, a weighted (a priori) arith-

metic average index of partial indicators is developed, which ex-

presses the relative standardised position of each individual

(municipality, subsystem or system) having combined the variability

of all the variables with a Paasche-type temporal aggregation. Here,

rather than focusing on the composite index, we deal with its con-

stituents and determinants. The 18 indices are constructed on the

basis of a number of basic variables, weighted in accordance with the

opinions of policymakers (as in Drewnowski, 1974). This databaserefers to the period between 1991 and 2000. Finally we have to

mention that several important dimensions of quality of life (such as

crime) are not considered here due to the lack of complete data for all

municipalities. We also assume that there are not subjective mea-

surements of well being. These factors would improve without any

doubt our database and consequently our final results.

The function of each city was controlled using a dummy variable

equal to 1 for cities that provide a minimum level of basic services,



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TABLE I

List of urban systems and subsystems within the Barcelona province

Urban systems (and their subsystems

where the former are divisible)

Size (1996

inhabitants)

Number of

municipalities

System of IAlt Penede` s 73,196 27

Subsystem of Sant Sadurn 14,093 4

Subsystem of Vilafranca 59,103 23

System of IAnoia 86,964 33

System of Bages 152,586 35

Subsystem of Manresa 122,895 27

Subsystem of Bages Nord 29,691 8

System of Baix Llobregat Nord 123,778 12

Subsystem of Esparraguera-Olesa 31,864 3

Subsystem of Martorell 73,582 8

Subsystem of Sant Andreu de la Barca 18,332 1

System of Baix Montseny 22,792 9

System of Barcelona 1,508,805 1

System of Bergueda` 38,606 31

System of Besos 413,106 8

Subsystem of Badalona 231,514 4

Subsystem of Sant Adria` del Beso` s 33,361 1

Subsystem of Masnou 25,056 2

Subsystem of Santa Coloma de Gramenet 123,175 1System of Cerdanyola, Montcada i Ripollet 106,474 3

Subsystem of Cerdanyola 50,503 1

Subsystem of Montcada i Reixac 27,068 1

Subsystem of Ripollet 28,903 1

System of Cornella` 82,490 1

System of Delta del Llobregat 135,310 5

Subsystem of Gava` 41,090 2

Subsystem of Castelldefels 38,509 1

Subsystem of Viladecans 55,711 2

System of Garraf 90,435 6

System of Granollers 173,168 23

Subsystem of Pla de Granollers 159,659 19

Subsystem of Congost 13,509 4

System of Maresme Nord 59,537 7Subsystem of la Riera de Calella 33,843 4

Subsystem of la Tordera 25,694 3

System of Maresme Sud 213,771 18

Subsystem of la Riera dArenys 28,799 5

Subsystem of Mataro 145,570 10

Subsystem of la Riera de Premia` 39,402 3

System of Mollet-Parets 70,331 10

Sistem of Osona 122,923 51

Subsystem of Osona Nord 19,422 9



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such as health and education services. Two different levels of higher

function cities were controlled. Thus, we selected 48 as basic func-

tional cities, with 24 as central cities. These dummies were considered

as being cumulative so that we might take into consideration a

threshold effect. Finally, the network city paradigm was modelled

using an indicator of installed telephone cells in 1996, as in Capello

and Camagni (2000). We believe that in this particular year it would

TABLE I

Continued

Urban systems (and their subsystems

where the former are divisible)

Size (1996

inhabitants)

Number of

municipalities

Subsystem of Vic 78,299 36

Subsystem of Manlleu 25,202 6

System of El Prat de Llobregat 63,255 1

System of la Riera de Caldes 29,193 7

System of Rub - Sant Cugat 101,295 2

Subsystem of Rub 54,085 1

Subsystem of Sant Cugat 47,210 1

System of Sabadell 283,954 10

Subsystem of Barbera` del Valle` s 42,542 2

Subsystem of Sabadell 223,530 6

Subsystem of Castellar 17,822 2

System of Sant Boi 84,477 3

System of Terrassa 177,824 6

System of la Vall Baixa de Llobregat 415,430 9

Subsystem of Esplugues i Sant Just 60,116 2

Subsystem of Sant Feliu de Llobregat 35,797 1

Subsystem of lHospitalet 255,050 1

Subsystem of Molins 37,662 4

Subsystem of Sant Joan Desp 26,805 1

Source: Arts et al. (1999).

0

5

10

15

20

10-25 25-40 40-75 75-150 150-250 +250

Thousands inhabitants

Numberofsubsystems

Figure 3. Population distribution among subsystems.



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TABLE II

Quality of life components and their variables

WI= Wealth index

+ per capita available family wealth

+ Average tax return per taxpayer

+ Average tax paid per taxpayer

+ per capita value added

+ Value added growth in last five years

LI= Labour index

+ Labour activity rate

+ Rate of unemployment

+ Gini Index of economic activity concentration

) GI of workers (15 sectors) ) GI of social security contributors (10 sectors)

+ Labour formation index

+ Number of classes + Number of studentsELI= Educational level index

+ Average no of years studied per person

MotI = Motorization index

+ Number of vehicles per 1000 inhabitants

DI = Demographic index

) Mortality rate

+ Birth rate

+ Average age level index

) Average age level in the municipality

)Average age level in the comarque

HAI= Housing access index

+ Rate of house rental

+ No of houses completed last year per 1000 inhabitants

+ Rate of new subsidised houses

) House price index in the largest city in the system

MigrI= Migration index

+ Rate of immigration in the municipality

+ Rate of immigration in the comarque

+ Population growth of the municipality

SII= Sex inequality index

+ Sex inequality in education levels

+ Sex inequality in education labour activity

OCI= Obligatory commuting index

+ Outside commuting index+ 1-rate of workers who commute to the Barcelona urban area

+ 1-rate of students who commute to the Barcelona urban area+ Distance from the

nearest capital (as service centre)

CongI= Congestion index) Automobile density

SOASI= Social and old age services index

+ Number of old-age residences per 1000 old age inhabitants

+ Number of old-age cultural centres per 1000 old age inhabitants+ Number of old-age day residences per 1000 old age inhabitants



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TABLE II

Continued

HC= Housing characteristics

+ Index of housing conditions

+ Houses size per inhabitant

+ Rate of one-family houses

+ Housing services index (water, phone, etc.)

PTI= Public transport index

) 1-Rate of public transport users among workers

) 1-Rate of public transport users among students

+ Train services+ Number of urban buses per potential users

EFI= Educational facilities index

+ Educational services index

+ Pre-school school units + High school units

+ Primary school units + Special education units

+ Students per school unit index

) Pre-school school

) Primary school

) High school

+ University index

+ University courses per 10,000 inhabitants between 19 and 24

+ Universitys diversity of supply

HFI= Health facilities index

+ Pharmacies per 1000 inhabitants+ Hospitals per 1000 inhabitants

+ Hospital beds per 1000 inhabitants

+ Outpatients health centers

+ Number of workers in the health sector per 1000 inhabitants

CEI= Climate and environment index

Environment index

+ Air quality index in Catalonia

Climate index

) Yearly temperature range

+ Average temperature

CFMMI= Cultural facilities and municipal media index

Cultural facilities index

+ Theatres and theatre diversity

+ Museums and museum diversity+ Bookshops and bookshop diversity

+ Municipal archives and municipal archive diversity

+ Cinemas and cinema diversity

+ Art galleries

+ Sport centres and sport centre diversity



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have been a good indicator of the network paradigm. The basic

descriptive statistics of all these variables are shown in Tables III

and IV.

The Estimation Results

Using this data, we then proceeded to compute equation (3) for each

constituent of quality of life. The functional form considered was a

translog function so that we could also consider cross-effects betweenthe key variables:

Quality of Life Component i g a1Size a2Function 1

a3Network b1(1/2)Size2 b2Function 2

b3(1/2) Network2 d1Size*Function 1

d2Size*Function 2 d3Size*Network

d4Network*Function 1 d5Network*Function 2 et

Where Function)1 and Function)2 are two dummy variables

related to basic functional cities and central cities, respectively;Network describes the number of telephone cells installed per 100

inhabitants in 1996; Size refers to the municipal population in 1996;

and the Quality of Life Component i is the measure that corresponds

to each quality of life dimension identified by Royuela et al. (2003).

All variables (except these dummies) are measured in logs. The esti-

mation took into consideration the possibility of heteroscedasticity

given the wide range of in size of the municipalities. Thus, the

weighted least-squares method was used in order to estimate the

TABLE II

Continued

Municipal media index

+ Written media

+ TV and radio

+ Municipal bulletins

MFSI= Municipal financial state index

) Debt: payable passive/total active

) Taxes over total revenues

)

Taxes per capitaSource: Royuela et al. (2003).



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TABLEIII

Descriptive

statistics(1)

Pop1996

fun

sub

fun

sis

Telxha

b

WI

LI

ELI

DI

MOTIHAIM

igrISSI

OCI

CONGISOASIHC

PTI

EFI

HFI

CEI

CFMMIMFSI

Min

30

0

0

125

66,7

32,1

76,7

71,3

63,4

85,67

4,3

87,1

25,3

82,1

81,2

30,12

1,1

78,0

77,3

67,3

36,6

39,4

Max

1508805

1

1

1095,2177,3

138,3

137,7

134,0

217,9

188,824

3,5

112,0

109,4

164,2

150,8

200,1168,9135,0121,1

119,9

156,5

199,3

Average

14744,30,153

0,076

439,8

92,1

95,8

90,9

99,9

106,4

100,911

1,0

99,9

88,1

155,6

120,4

115,06

0,0104,4

94,2

89,9

62,6

114,8

Median

2007

0

0

414,7

87,5

97,2

88,5

99,9

103,5

99,210

6,5

99,9

88,9

164,2

121,2

115,65

7,8104,5

93,5

90,0

60,7

101,1

StdDev

88774,8

0,36

0,27

121,0

416,31

15,70

9,80

12,76

14,25

10,2118

,16

3,26

11,48

24,51

10,94

16,2022,85

8,82

8,94

9,82

15,50

28,03

Kurtosis

258,6

1,8

8,3

5,32

53,425

0,614

3,634

)0,581

11,26019,33010

,83

1,889

3,436

5,052

0,493

5,3223,3720,7361,276)0,2943,602

0,035

Skewness

15,51,939

3,204

1,70

81,633

)0,592

1,628

)0,185

1,633

3,0752,349

)0,088

)1,053

)2,638

)0,415

)0,5341,3950,1831,036

0,1811,131

0,490

Notes:FUNSIS:dummyvariablecorrespondingtothe24centralcitiesofthe

province.FUNSUB:dummyvariablecorrespondingtothe48functionalcities;

TELXHAB:installedtelephonecells;POB_

96:1996populationofeverymunicipality;WI=

WealthIndex,LI=

LabourIndex,ELI=

EducationalLevelIndex,

DI=

Demographic

Index,MotI=

Motorization

Index,

HAI=

Housing

AccessIndex,MigrI=

MigrationIn

dex,SII=

Sex

Inequality

Index,

OCI=

ObligatoryCommutingIndex,CongI=

CongestionIndex,SOASI=SocialandOldAgeServicesIndex,HC=HousingCharacteristics,PTI=

Public

TransportIndex,EFI=

Education

alFacilitiesIndex,HFI=

HealthFacilitiesIndex,CEI=

ClimateandEnvironmentI

ndex,CFMMI=

CulturalFacilities

andMunicipalMediaIndex,MFSI=

MunicipalFinancialStateIndex.



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TABLEIV

Descriptivestatist

ics(2)correlations

Pop_96funsubfunsisTelxha

bWI

LI

ELI

DI

MOTIHAI

M

igrISSI

OCI

CONGISTASIHC

PTI

EFI

HFI

CEI

CFMMIMFSI

Pop1996

1

funsub0,313

1

funsis

0,379

0,677

1

telxhab

0,042

)0,067)0,020

1

WI

0,081

0,080

0,048

0,287

1

LI

0,044

)0,038

0,013

0,233

0,419

1

ELI

0,155

0,174

0,094

0,267

0,718

0,350

1

DI

0,002

0,222

0,095

0,239

0,509

0,453

0,587

1

MOTI)0,075)0,285)0,188

0,292

0,425

0,412

0,417

0,258

1

HAI

)0,014

0,141

0,147)0,060

0,111

0,092

0,062

0,038)0,113

1

MIgrI)0,101)0,139)0,130

0,496

0,395

0,407

0,362

0,464

0,337)0,005

1

SSI

0,004

)0,012

0,006)0,006

)0,115

0,064)0,071

0,083)0,030)0,115)0,020

1

OCI

0,173

0,490

0,388)0,346

)0,214)0,104)0,143

0,083)0,332

0,169)0,303

0,116

1

CON-

GI

)0,378)0,778)0,664

0,058

)0,033

0,024)0,127)0,219

0,284)0,0840

,138)0,035)0,363

1

SOASI)0,310)0,353)0,240)0,074

)0,210

0,004)0,211)0,277

0,098

0,148)0,084)0,062

0,011

0,482

1

HC

)0,155)0,321)0,243

0,094

0,186

0,102

0,180

0,077

0,348)0,0350

,161

0,071)0,057

0,353

0,130

1

PTI

0,291

0,468

0,310

0,044

0,145)0,067

0,242

0,333)0,236

0,028)0,032

0,011

0,191)0,547

)0,635)0,238

1

EFI

)0,083)0,258)0,139)0,276

)0,382)0,179)0,395)0,620)0,140

0,094)0,406)0,111

0,031

0,233

0,344

0,044)0,312

1

HFI

0,109

)0,092)0,076)0,250

)0,237)0,060)0,286)0,441)0,061

0,071)0,374)0,136)0,010

0,011

0,146)0,085)0,093

0,484

1

CEI

0,171

0,279

0,210

0,110

0,259)0,093

0,251

0,260

0,005

0,0190

,231

0,265

0,174)0,296

)0,431)0,066

0,36

4)0,377)0,341

1

CFM-

MI

0,402

0,245

0,181)0,018

0,115

0,208

0,147

0,239

0,104)0,018)0,063

0,133

0,224)0,273

)0,077

0,066

0,15

1)0,059

0,174

0,024

1

MFSI

)0,088)0,239)0,138)0,199

)0,321)0,086)0,275)0,356)0,079)0,046)0,199)0,043)0,0550,270

0,354

0,024

)0,2890,325

0,300

)0,325)0,120

1



16/24

translog functions of each of the 18 quality of life components. The

weighting variable was municipality size, expressed in logs. Table V

shows all our results.

From these estimates, we can draw several conclusions about the

relationship between size and the constituents of quality of life.

Wealth Index: the relation computed is not very strong, although

the relation with size is unmistakable. Thus, agglomeration

economies play a significant role in generating higher wealth in

the larger municipalities. Labour Index: here the relation is much weaker. Additionally,

the more significant parameters of the translog function are

those that are related with the variables from the network city

paradigm. Thus, city size is much less important in attracting

labour than the fact of being connected to the network city.

Educational Level Index: this variable has a relatively strong rela-

tion with the city size paradigm. The only parameters that are sig-

nificant are those related with city size. Here the relation is

unmistakable: people with a higher level of education live in the

larger or medium-sized cities. Thus, in the long term, the greater

possibilities of attaining a higher education in these cities meansmany more educated people tend to live there.

Demographic Index: the municipalities with the highest demo-

graphic potential are those that are of medium size. In addition,

cities with a high function in the city system also present a high

concentration for this index.

Motorization Index: the proportion of vehicles per inhabitant

clearly falls with city size. The most plausible reason for this

is the greater need for private means of transportation among

people living in small municipalities. There are two explana-

tions: the greater need for transportation in order to have

access to the same amount of services, and the poorer pro-

vision of public transportation services in and around these

small municipalities.

Housing Access Index: a very weak relation was found with this

index, which expresses the ease of finding a place in which to live

and the city paradigms. Only one parameter of the translog

function is significant: the cross-effect between size and high

functions of cities has a negative effect on this index, showing a



17/24

higher level of housing prices or a lower level of new homes or

houses for rent.

Migration Index: this index has a relatively strong relation with

the controlled city paradigms. Thus, we see that medium-sized

cities with a high function in the city system receive more people

than very large or small municipalities. This index clearly

TABLE V

Estimation results from equation (3)

WI=

Wealth

Index

LI=

Labour

Index

ELI=

Educational

Level

Index

DI =

Demographic

Index

MotI =

Motorization

Index

HAI=

Housing

Access

Index

(Intercept) 0,260 0,161 0,428 0,404 0,299 0,122

1,22 )0,81 5,88*** 2,64*** 3,33*** 1,84*

POB_96 9,651 5,284 20,559 18,644 11,733 3,813

)3,55***

)0,73

)4,12***

)0,59

)4,52*** 0,3

FUNSUB 0,000 )1,500 1,500 )1,000 0,500 )2,000

)1,7* )1,96* )1,35 )0,6 )0,9 0,04

LTELX-

HAB

0,000 0,000 0,000 0,000 0,000 0,000

1,2 2,17** )0,46 )0,41 0,42 0,7

SIZE_2 0,224 0,416 0,000 0,009 0,001 0,066

2,38** 0,37 2,24** 2,25** )2,9*** )0,81

FUNSIS 0,000 0,465 0,000 0,559 0,000 0,766

2,68*** 1,22 1,05 3,85*** 0,43 )0,17

NETW_2 0,090 0,051 0,177 0,547 0,368 0,967

)1,46 )2,09** )0,11 0,59 )1,31 )0,77

SIZE_F1 0,233 0,031 0,644 0,685 0,676 0,486

)1,33 0,81 )0,92 )2,05** 0,36 0,59

SIZE_F2 0,018 0,710 0,026 0,025 0,004 0,421)1,31 )1,6 )0,94 )1,51 0,49 )1,72*

SIZE_-

NET

0,008 0,225 0,294 0,000 0,671 0,863

3,17*** 0,73 4,21*** 0,21 6,03*** 0,14

NET_F1 0,146 0,037 0,912 0,556 0,192 0,445

)2,5** )1,45 )0,92 )3,59*** )0,52 0,11

NET_F2 0,185 0,416 0,356 0,041 0,718 0,557

2,14** 2,54** 1,65 1,03 0,81 0,48

R2 0,260 0,161 0,428 0,404 0,299 0,122

Adj R2 0,233 0,131 0,407 0,383 0,274 0,090

F 9,651 5,284 20,559 18,644 11,733 3,813

Sig 0,000 0,000 0,000 0,000 0,000 0,000

Weighting

potency

of WLS

0 )1,5 1,5 )1 0,5 )2



18/24

indicates a more complex relation between city size and quality

of life components, because city size changes as people move

from one place to another.

Sex Inequality Index: this variable, which expresses the different

amounts of social capital in the municipalities, is clearly not

related with any of the controlled paradigms. Thus, it can be

TABLE V

Continued

MI=

Migration

Index

SII=

Sex

Inequality

Index

OCI=

Obligatory

Commut-

ing Index

CongI=

Congestion

Index

SOASI=

Social and

Old Age

Services

Index

HC=

Housing

Character-

istics

(Intercept) 0,467 0,063 0,481 0,815 0,301 0,394

3,14*** 10,69*** 1,86* 10,68*** 5,1*** 0,24

POB_96 24,084 1,853 25,453 120,579 11,805 17,821)1,44 )0,17 3,06*** )0,21 1,72* 1,41

FUNSUB )2,500 1,000 )2,000 3,000 1,000 )2,500

0,23 )0,19 1,7* 4,36*** 0,89 )0,09

LTELX-

HAB

0,000 0,000 0,000 0,000 0,000 0,000

)1,45 0,19 )0,12 0,02 )0,48 1,27

SIZE_2 0,002 0,000 0,064 0,000 0,000 0,810

)1,3 )0,3 )2,7*** )0,64 )1,29 )5,65***

FUNSIS 0,152 0,867 0,002 0,831 0,086 0,161

2,58** )0,48 )4,45*** )2,77*** )1,91* )2,72***

NETW_2 0,818 0,848 0,091 0,000 0,373 0,929

1,49 )0,3 0,16 )0,1 0,66 )1,37

SIZE_F1 0,148 0,846 0,902 0,986 0,632 0,205

)0,63

)1,46 2,92*** 6,65*** 2,46** 3,54***

SIZE_F2 0,196 0,767 0,007 0,522 0,198 0,000

)0,6 1,12 )1,16 )15,3*** )4,28*** 0,67

SIZE_-

NET

0,010 0,629 0,000 0,006 0,057 0,007

2,29** 0,47 )1,91* 0,4 )1,58 0,87

NET_F1 0,138 0,764 0,874 0,924 0,511 0,172

)2,61*** 0,82 4,07*** 1,48 1,5 2,05**

NET_F2 0,529 0,145 0,004 0,000 0,014 0,000

)0,12 )0,14 )1,36 )0,71 0,21 )0,11

R2 0,467 0,063 0,481 0,815 0,301 0,394

Adj R2 0,448 0,029 0,462 0,808 0,275 0,372

F 24,084 1,853 25,453 120,579 11,805 17,821

Sig 0,000 0,045 0,000 0,000 0,000 0,000

Weightingpotency

of WLS

)

2,5 1)

2 3 1)

2,5



19/24

TABLE V

Continued

PTI=

Public

Transport

Index

EFI=

Educa-

tional

Facilities

Index

HFI=

Health

Facilities

Index

CEI=

Climate

and

Environ-

ment

Index

CFMMI=

Cultural

Facilities

and

Municipal

Media

Index

MFSI=

Municipal

Financial

State

Index

(Intercept) 0,530 0,328 0,277 0,283 0,140 0,273)1,15 6,87*** 2,49** 5,64*** 0,24 )0,19

POB_96 30,933 13,389 10,538 10,849 4,480 10,297

1,25 5,54*** 2,42** )2,56** )0,28 5,04***

FUNSUB )1,000 2,500 )1,500 0,500 )0,500 )0,500

)0,81 )0,6 )1,28 )0,43 0,58 0,86

LTELX-

HAB

0,000 0,000 0,000 0,000 0,000 0,000

1,52 )0,6 0,29 )1,97** i 0,7

SIZE_2 0,249 0,000 0,013 0,000 0,808 0,849

0,01 )4,24*** )0,67 0,08 0,29 )3,63***

FUNSIS 0,214 0,000 0,016 0,011 0,777 0,000

2,09** )2,41** )1,86* 1,32 )1,94* )3,31***

NETW_2 0,420 0,548 0,203 0,670 0,564 0,389

)1,2 1,22

)0,07 1,39

)0,85

)0,12

SIZE_F1 0,130 0,551 0,770 0,049 0,395 0,486

)2,15** 1,02 0,52 )1,02 )0,3 3,69***

SIZE_F2 0,996 0,000 0,502 0,937 0,770 0,000

3,13*** 0,27 3,08*** 0,52 1,85* )0,55

SIZE_-

NET

0,038 0,017 0,064 0,187 0,053 0,001

)0,86 )4,82*** )2,6*** 3,16*** 0,2 )4,24***

NET_F1 0,230 0,224 0,942 0,166 0,396 0,903

)1,75* 2,3** 1,78* )1,16 2,06** 2,66***

NET_F2 0,033 0,307 0,603 0,309 0,766 0,000

)0,03 0,56 0,5 0,3 )1,11 )0,74

R2 0,530 0,328 0,277 0,283 0,140 0,273

Adj R2 0,513 0,303 0,251 0,257 0,109 0,246

F 30,933 13,389 10,538 10,849 4,480 10,297Sig 0,000 0,000 0,000 0,000 0,000 0,000

Weighting

potency

of WLS

)1 2,5 )1,5 0,5 )0,5 )0,5

Note: * Significant at 10%; ** Significant at 5%; *** significant at 1%. The t-statistic is

shown in italcs. POB_96: Size. FUNSUB Function_1. LTELXHAB: Network. SIZE_2_

Size2. FUNSIS: Function_2. NETW_2: Network. SIZE_F1: Size*Function_1. SIZE_F2:

Size*Function_2. SIZE_NET: Size*Network. NET_F1: Network*Function_1. NET_F2:

Network*Function_2.



20/24

concluded that sex inequalities are distributed independently of

the city paradigms.

Obligatory Commuting Index: here the index is quite well ex-

plained in terms of the city paradigms. As a citys size increases, its

inhabitants do not have to commute so much in order to travel to

work or to enjoy public or private services.

Congestion Index: This index, computed as the density of

automobiles, is much higher in big cities than in small munici-

palities. In addition, the dummy variables that control the city

functions are those that account for this congestion. It is inter-

esting to see how high function cities have more congestion than

those with more simply functions.

Social and Old Age Services Index: This variable clearly falls path

as city size increases. In addition, high function cities have, dif-

ferentially, a lower level of social and old age services.

Housing Characteristics: this function, which is relatively well

explained, presents a marked parabola that decreases in size after

reaching the mid-point. Furthermore, functional cities have a

higher level of housing characteristics than high function cities.

A relation might be established here with the higher MigrationIndex that can be found in medium-sized cities, where new

houses, with higher characteristics, have been built in recent

years.

Public Transport Index: this variable clearly increases with size

and city function. Thus, larger and more functional cities are

much better connected to public transport than smaller, less

functional cities.

Educational Facilities Index: this variable does not increase

markedly with size as one would expect. Although educational

facilities increase with city size, high function cities have a rela-

tively lower level. This is due to the fact that, although there aremore services, there are also more individuals that require these

services. This leads to a certain level of congestion.

Health Facilities Index: the situation here is similar to that re-

corded for educational services. There are more services in larger

cities, but there is also greater population pressure on them.

Climate and Environment Index: this index, which not only in-

cludes the environment but also includes the climate, presents a

positively sloped relation with city size. Thus, although one



21/24

might believe that large cities are much more heavily polluted,

we also see how people tend to concentrate spatially in places

with a good climate.

Cultural Facilities and Municipal Media Index: this index pre-

sents a weak relation with city paradigms. Although a positive

relation with city size does develop, city function plays an

uncertain role, with the functional cities presenting the highest

levels of this index.

Municipal Financial State Index: finally, the financial state of

municipalities presents a negatively sloped relation with city size.

It would seem that as municipalities increase in size, they have to

increase the amount of public services they provide without

benfitting from scale economies.

These results show a majority of well-behaved curves, with a

diversity of levels of adjustments. In addition, the positive effect of

city size is reflected in the economic index. Agglomeration econo-

mies were found to play a significant role in this metropolitan

area. A positive effect of city size was also seen in the economies

of scale and the indivisibilities of public services. This was the case

of public transportation, which means people do not to have to bethe private owners of increasing numbers of automobiles. We have

also seen how people migrate to large or medium-sized munici-

palities and that the demographic potential here is greater, with

more young people and higher birth rates.

Nevertheless, several costs were also identified as a consequence of

size. Congestion arises, of course, in terms of the density of automo-

biles, but also in terms of the provision of such basic services as edu-

cation and health. The provision of these public services by the

municipalities also serves to weaken their financial circumstances. In-

deed, what we find is that several services are insufficient in larger cities,

as is the case with social services and those for the elderly. Here, a

process of the territorial substitution of services arises, as residences for

the elderly, for instance, become concentrated at some distance from

more populated cities.

It should be noted that the city network paradigm was found only

to be of importance in the case of the labour index, but this dem-

onstrates the significance of this paradigm in its relation with eco-

nomic activity.



22/24

In addition, the neoclassical supply-oriented dynamic approach,

which emphasises city functions, has shown itself to be an important

factor. Marked differences in benefits and costs are recorded

according to the function of each city in the city system. Thus, effi-

cient city size presents itself as a more important concept than opti-

mal city size: costs and benefits depend on what the city produces and

how it produces them.

CONCLUSIONS

This study has focused its analysis on the application of three city

paradigms: optimal city size theory, the supply-oriented dynamic

approach, and the city network paradigm. We have taken into con-

sideration the costs and benefits to cities in terms of household utility

rather than applying a production function. In this context, the

quality of life concept and its constituents are particularly pertinent.

By adopting this framework, we have been able to see the influence of

each specific paradigm on each of the 18 controlled components of

quality of life.Our most significant finding is that agglomeration economies were

shown to play a significant role, especially in the economic index.

Economies of scale and the indivisibilities of public services were also

found to be significant, as were public transportation services. In the

case of costs, we have seen how congestion occurs in terms of the

density of automobiles as well as in the provision of education and

health services. The provision of public services by municipalities also

serves to weaken their financial condition. Furthermore, we have evi-

dence of a process of territorial substitution of services whereby social

services and those for the elderly are pushed out from the larger cities.

The city network paradigm played a significant role in the labourindex, which has an obvious relation with economic activity. Simi-

larly, the neoclassical supply-oriented dynamic approach, which

places an emphasis on city functions, was also shown to be an

important factor.

The next step in this research will require conducting analyses that

take into account the spatial relationships between all municipalities

and those within higher function cities.



23/24

NOTES

1 Additional approaches have been made on the analysis of the economy of cities, in

which more subjective processes are considered, for instance taking into account

conditions such as the need to provide help to elderly relatives. As an example of this

literature see Jacobs (1979, 1984).2 The accepted social materialist vision of reality is the instrumental nature of

economic activities that enables people to earn resources that are used in other

activities that give rise to satisfaction. By contrast, the post-materialist vision claims

that in societies characterised by abundance, resources are not infinite, but rather

sufficient, so that choices are made in terms of opportunity costs. Thus, a job can also

be highly valued in terms of factors other than the earnings it produces.3 Each system or subsystem has basic health or educational services that are not

shared with other systems or subsystems. So global services such as Universities and

large hospitals are not considered as defining features of the urban systems or sub-

systems. An additional exploration of this method of grouping municipalities

according to social criteria can be seen in Royuela and Roman (2004).4 We use more than 500 basic variables, referring to all 314 municipalities and, in the

main, to different time periods between 1991 and 2000. These figures indicate the size

of the database.

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Quantitative Regional Analysis Research Group

(AQR, www.ub.es/dpees/aqr/index.htm)

University of Barcelona

690 Avenida Diagonal

Barcelona, 08034

Spain

E-mail: [email protected]


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