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Territorial disparities in the labour market:

An analysis applied to the Portuguese municipalities

by

Ana Sofia Mesquita Silvano Vaz

Dissertation for the degree of Master in Human Resource Management

from the School of Economics and Management at

the University of Porto

Supervised by:

Maria Manuel Pinho

September, 2014

i

Short biography

Ana Sofia Mesquita Silvano Vaz was born on the 9th

of August of 1988, in

Porto, Portugal.

After getting a degree in Economics, from the School of Economics and

Management, at the University of Porto, Ana was enrolled in the Master in Human

Resource Management, from the same school, in 2012. After completing the academic

part, she started working on her dissertation project.

Professionally, alongside the final year of bachelor, Ana undertook a traineeship

in Human Resources, at the firm Randstad, which fostered the curiosity to deepen the

knowledge in the area and, consequently, the enrolment in the masters mentioned

above. Since May 2013, Ana integrates the customer service department in the company

Microdigital Lda, providing support to accounting and management firms in the area of

payroll, billing and accounting.

ii

Abstract

In the present research work, we sought to deepen the theme of the local labour

markets, regarding their specificities, such as commuting, skills mismatch and the

output-unemployment relationship. This study is crucial, since the sustainable and

balanced development, among different regions and labour markets of a country, is only

possible through the knowledge of these peculiarities of the local labour markets, which

allow the delineation of educational, economic and social policies, adapted to each case.

The aim of this thesis was also to deepen the results of the 2011 Census taking

into consideration that, in Portugal, the ageing population and the desertification of the

inner areas, together with the polarization of the metropolitan areas located on the coast,

are the main challenges to a sustainable economic development, at the regional level.

Through a factor and a cluster analysis it was possible to group the Portuguese

municipalities into homogeneous groups, concluding on topics such as demographic

sustainability, attractiveness, commuting, qualification, school leaving and integration

of foreign population, on a municipal scale, and so on territorial specificities regarding

local labour markets in Portugal.

Keywords: human capital, labour force, local labour market, mobility, skills mismatch,

output-unemployment relation, factor analysis, clusters analysis

JEL Codes: C38, J24, J49, J61

iii

Resumo

No presente trabalho de investigação, procurou-se aprofundar a temática dos

mercados de trabalho locais, ao nível das suas especificidades, como a mobilidade, a

desadequação de competências e a relação produto-desemprego. Este estudo é crucial

uma vez que a garantia de um desenvolvimento sustentável e equilibrado entre as

diferentes regiões e mercados de trabalho de um país só será possível através do

conhecimento dessas particularidades dos mercados de trabalho locais, que permitirão a

delineação de políticas de natureza educativa, económica e social, adaptadas a cada

caso.

O objectivo desta tese foi também aprofundar os resultados obtidos com o

momento censitário de 2011, tendo em consideração que, em Portugal, o

envelhecimento da população e a desertificação das regiões interiores, a par com a

polarização das áreas metropolitanas localizadas no litoral, são os principais desafios

que se colocam ao desenvolvimento económico sustentado ao nível regional.

Através de uma análise fatorial e de uma análise de clusters, foi possível agrupar

os municípios portugueses em grupos homogéneos, concluindo sobre temáticas como a

sustentabilidade demográfica, a atratividade, a mobilidade, a qualificação, o abandono

escolar e a integração de população estrangeira à escala municipal e, consequentemente,

sobre as especificidades dos mercados de trabalho locais em Portugal.

Palavras-chave: capital humano, força de trabalho, mercado de trabalho local,

mobilidade, desadequação de competências, relação produto-desemprego, análise

fatorial, análise de clusters

Códigos JEL: C38, J24, J49, J61

iv

Table of contents

Introduction ....................................................................................................................... 1

1. Labour force and human capital ................................................................................... 3

2. Local labour markets .................................................................................................... 7

2.1. Notion and definitions ...................................................................................... 7

2.2. The challenges of local labour markets ............................................................. 9

2.2.1. Labour mobility ........................................................................................... 11

2.2.2. Skills mismatch ............................................................................................ 12

2.2.3. The output-unemployment relation .............................................................. 14

3. The literature on the Portuguese labour markets........................................................ 16

4. An empirical assessment of the Portuguese labour markets ...................................... 19

4.1. Data and variables ........................................................................................... 19

4.2. Factor analysis ................................................................................................. 25

4.3. Cluster analysis ................................................................................................ 47

5. Conclusions and future developments ....................................................................... 53

References ....................................................................................................................... 56

Appendix. Cluster membership table .............................................................................. 61

v

List of Tables

Table 1. List of the selected indicators ........................................................................... 20

Table 2. Linear correlation matrix .................................................................................. 24

Table 3. KMO and Bartlett’s test .................................................................................... 26

Table 4. Communalities .................................................................................................. 27

Table 5. Total variance explained matrix ....................................................................... 28

Table 6. Rotated component matrix ................................................................................ 30

Table 7. Component score coefficient matrix ................................................................. 46

Table 8. Number of cases in each cluster ....................................................................... 48

Table 9. ANOVA ............................................................................................................ 50

Table 10. Final cluster centres ........................................................................................ 50

List of figures

Figure 1. Scree plot ......................................................................................................... 29

Figure 2. Demographic sustainability: highest and lowest values .................................. 32

Figure 3. Demographic sustainability per municipality .................................................. 33

Figure 4. Attractiveness: highest and lowest values ....................................................... 34

Figure 5. Attractiveness per municipality ....................................................................... 35

Figure 6. Qualification: highest and lowest values ......................................................... 37

Figure 7. Qualification per municipality ......................................................................... 38

Figure 8. Commuting: highest and lowest values ........................................................... 39

Figure 9. Commuting per municipality........................................................................... 40

Figure 10. School leaving: highest and lowest values .................................................... 41

Figure 11. School leaving per municipality .................................................................... 42

Figure 12. Foreign population: highest and lowest values ............................................. 43

Figure 13. Foreign population per municipality ............................................................. 44

Figure 14. Cluster membership per municipality ........................................................... 49

1

Introduction

Human capital includes the knowledge and the skills of an employee, acquired

through education, training or experience at work and has a crucial role in the

production of economic value. Moreover, the aggregate level of human capital has a

major contribution to the productivity of the other factors of production. The investment

in human capital is, in this sense, fundamental to increase the productivity and hence

economic growth.

The main objective of this research work is to provide an assessment of the

specific problems of local labour markets in Portugal, at the municipality level. The

main problems faced by local labour markets are the absence of mobility (although the

mobility of the human resources tends to be more intense among the regions of a given

country than between regions of different countries taking into account that the

administrative and cultural barriers do not assume such importance locally) which,

together with the mismatch of skills, actively contributes to the existence of another

recurring problem in this type of market: unemployment.

This thesis is organized as follows. Sections 1, 2 and 3 present a literature

review on the main specific challenges faced by local labour markets that are pinpointed

within the literature. The literature review is based on work from national authors

complemented with studies by Statistics Portugal and studies of foreign authorship.

In this sense, Section 1 discusses the relevance of the investment in human

capital to the economic activity growth and enumerates some of the possibilities of

measuring the contribution of the labour force to production. The second section is

dedicated to the definition and specificities of local labour markets, like the mobility,

the skills mismatches and the relation between output and unemployment. Section 3

enumerates some of the territorial characteristics in Portugal that are relevant to the

empirical study carried out in Section 4. Regarding the empirical part, developed in

Section 4, we proceed to the analysis of 30 indicators with the aim of discovering the

latent problems in labour markets at the municipal level in Portugal. For this study we

use data obtained from the 2011 Census. This section provides an analysis of a set of

indicators that portray the Portuguese labour market, in relation to aspects such as the

commuting that occur between local labour markets, academic qualification and

2

economic activity. In order to carry out this study, we develop a factor analysis on the

set of indicators and then a cluster analysis. Finally, we draw some conclusions about

the work developed and present future lines of work and investigations that may be

explored.

3

1. Labour force and human capital

Taking as a starting point the definition of labour, it can be said that it

represents, in an economic perspective, a productive factor through which goods and

services are produced. According to Sakalas and Liepė (2011: 900), human capital is, in

a qualitative perspective, the “accumulation of knowledge, acquirement of appropriate

skills, special abilities and competence for employees, all of which is the engine of

country's economic growth, creating a competitive advantage”. In a quantitative

approach, human capital can be reflected in labour force and labour productivity. The

labour force, according to Bandeira (2006: 14), “is determined each time by the

qualifications of the assets available to work in various sectors and occupations” and

labour productivity is represented by the amount of goods produced per unit of labour

(for example, a worker or an hour of work).

From the worker’s perspective, the investment in human capital increases his

productive value, and consequently increases the probability of finding a job and

receiving a higher wage, once that “being the education system the main instrument to

qualification and education, the levels of education and schooling of the population (...)

constitute a key in economic output” (Bandeira, 2006: 14). From the point of view of

the firm, the realization of this investment brings benefits mainly in productivity, which

could mean an increase in the competitiveness. In this sense, the investment in human

capital is the first step to enter into the labour market once it raises the labour force

productivity and creates a competitive advantage for workers. At the same time, several

theories point to the close relationship between human capital accumulation and

productivity growth. Maason, O'leary and Vecchi (quoted in Olimpia, 2012: 326)

concluded that human capital has a positive effect in productivity growth, especially

when high-skilled labour is used. Some studies, such as the one developed by De La

Fuente (2011), highlight the importance of investing in education to the productivity

growth. Becker (1993) developed the human capital theory which explains the level of

the labour productivity by the level of education of the workers. Other authors also

consider a complementary relationship between human capital accumulation and labour

productivity Olimpia (2012) conducted a study in the European economies that

estimates the human capital stock based on different levels of educational costs

4

concluding that labour productivity has a positive correlation with the human capital

stock for these countries (for instance, the change in labour productivity can be

explained in a proportion between 85% and 95% by the change in human capital stock,

considering the other factors constant).

So far, we discussed the labour force in a qualitative approach, so the following

paragraphs seek to determine how it should be measured in a quantitative dimension.

According to Borjas (2013), labour force can be given by the set of employed and

unemployed people in the market and its participation rate can be split, according to

Bandeira (2006), between gross rate and the global rate. The first measure is the ratio of

the labour force (total employed and unemployed) and the total population, while the

global rate is obtained by considering only the active population (formed by the group

of employed and unemployed people that are available to work, or in other words, that

actively seek work) and is therefore a more concise indicator. Once the stock of labour

is a broad concept, which involves several dimensions and a complex composition,

labour force cannot be consider as a homogeneous factor of production. In this context,

labour productivity can be measured in different ways.

The starting point is setting the statistical way to measure labour productivity

through some indicators like output per unit of labour input. The output reflects the

goods or services produced by the workforce and the input considers the team, skills

and effort of it.

The Organisation for Economic Co-operation and Development (OECD)

proposes two different perspectives to measure labour productivity (Freeman, 2008).

The first one is the OECD Productivity Database, which is an annual publication of

labour productivity growth for the OECD countries, where the labour productivity is

measured in hours (ratio between real GDP at constant prices and the hours worked).

The second perspective is developed by the OECD System of Unit Labour Cost and

Related Indicators and measures the contribution of labour to the production by the

costs approach. The unit labour costs are measured by the quotient of total labour costs

and real output. The number of hours worked is more used because it is a more precise

indicator for the calculation of productivity that may even serve for the purposes of

5

international comparison of the contribution of labour to productivity and economic

growth, although more difficult to measure.

These alternative measures not only allow for the monitoring of the evolution of

labour force but also for the consideration of the effect of the part-time work, which is a

very important way of work nowadays.

A study for France, in the period between 1987 and 1998, where labour

productivity was measured using different methods, concluded that the productivity,

based on the total hours worked, rises faster than when other methods are used

(Schreyer and Pilat, 2001).

Despite being the perspective with more precision to measure the workforce, it

also has disadvantages: beyond the calculation complexity that this method requires,

since it focuses on the number of hours worked, this approach is also limited because it

does not reflect the qualitative dimension of work that is related to the skills present in

the workforce (Schreyer and Pilat, 2001).

In Portugal, the official measure of the employed, active and inactive population

is provided by the Labour Force Survey, which is published by Statistics Portugal on a

quarterly basis, through a combination of direct and telephone interview, from a

representative sample of the population (Teixeira et al., 2012). At the same time, the

National Accounts System seeks to bring together a range of relevant indicators, which

includes, among other aspects of economic activity, the employment. In the group of the

National Accounts’ macroeconomic aggregates, there is a section dedicated to

employment, where we can find indicators as the number of jobs, the number of

employed individuals, the full-time equivalent number of employed individuals and the

number of hours worked.

After analyzing the different methods to assess the workforce, and since it is not

recommended to measure the contribution of labour to production by counting the total

number of employees in the market, since it does not reflect the work time per person

employed and not even the multiple job holding or the quality of labour (Schreyer and

Pilat, 2001), the ideal would be to use two complementary methods of measurement

that consider firstly the existence of intangible factors such as the skills of the workers

and other numeric variables like the hours actually worked.

6

Considering that the labour market is increasingly divided into local labour

markets, with specific characteristics and problems, it is important to study how this

subdivision can contribute to the market equilibrium, where demand for labour

promoted by employers equals the labour supply of workers.

7

2. Local labour markets

2.1. Notions and definitions

Christaller (quoted in Rushton, 1971: 140) developed, in 1933, the theory of

central places, which seeks to explain the distribution pattern, size and number of

existing towns and cities, whereas they exist for purely economic grounds and are

distributed spatially following a homogeneous pattern. The result of the proposed theory

is the assumption of a space where people exchange goods and services (Cabugueira,

2000). Taking the theory developed by Christaller as an inspiration, the labour market

can be divided spatially.

The spatial division of labour is, according to Rodrigues (quoted in Campos,

1995: 130-131), related to “growing movements of capital relocation” which is

facilitated by “the strengthening of infrastructure of transportation and communication”,

since it is a function of “the qualifications of the workforce labour available in each

region”, which explains in part the differences in development between regions.

According to Cabugueira (2000), the causes for the differences in development between

regions can be primary, of which the lack of mobility of the production factors (labour

and capital), the economic structure of the region and its location are examples, and

secondary, such as demographics aspects and the existence of external economies.

Labour markets may be national or local, with these last ones interacting for

different qualifications and skills. In this sense, Weller (2007) highlights the

multidimensional and multi-scale character of labour markets, considering the existence

of multiple markets, classified as inherently “local”. In these, the cultural and social

differences affect the way markets are regulated, particularly with regard to labour

market policies. This aspect goes according to the argument defended by Green and

Owen (cited in Campbell, 2000: 658), who consider that the specificities regarding the

nature and extent of local markets gives rise to the need to formulate specific policies in

order to attract / retain employees with the required skills. According to Adams et al.

(2000: 2), “national policies may be effective at reducing the level of unemployment

nationally, but they do not take into account the diverse nature of local labour markets”.

In order to reduce potential inter-regional disparities in the labour market, there are two

8

categories of employment policies: active policies and passive policies. Passive

employment policies are directly related to the scourge of unemployment, and consist

mainly in the allocation of unemployment benefits as a source of income during the

search for a new job (Centeno and Novo, 2008).

The active labour market policies are related, as noted, by OECD (Martin, 2000)

to the public employment services and administration (including job placement and

administering unemployment benefits) and the labour market training. The labour

market training has two dimensions: the first one includes training for the unemployed

and the second training for employed adults (Martin, 2000). Active labour market

policies also play a facilitating role with regard to mobility between local labour

markets, promoting a better connection between supply and demand for labour and

mitigating negative aspects of transition such as the rigidity of the housing market. At

the international level, the public service of local employment is also addressed in the

“LEED Programme” (OECD, 2013) that supports the implementation of public policies

to encourage entrepreneurship and self-employment and to attract emerging industry

sectors for the region in question. In general, “the regulation of the labour market

should be designed in order to facilitate the adjustment of employment to economic

conditions for business, and to protect workers from unexpected fluctuations in income

during periods of unemployment” (Centeno and Novo, 2012: 8).

All the settings above about the labour market refer to its division into local

markets, so it is also important to present some of their meanings. Goodman (1970)

presents a definition of local labour market considering that in this type of market the

geographic distribution of demand and supply of labour is different: the demand for

labour can be expressed by the location of workplaces and supply by the location of

residential areas. Moretti (2010) proposes a model of balance of local labour market

where firms and workers have freedom of movement between markets, but with the

condition that labour has finite mobility, limiting the elasticity of local labour market

supply. According to Goldner (1955), the supply of labour also depends on the location

of the workers preferences, which in turn depend on the time of travel between home

and work and on the leisure time.

9

Kerr (quoted in Goodman, 1970: 182) defines the local labour market as an

“area of indistinct geographical and occupational limits within which certain workers

customarily seek to offer their services and certain employers to hire them”.

Hunter and Reid (quoted in Goodman, 1970: 182) consider the local labour

market as “a geographical area located around a central city (or cities a few kilometres

away) where there is a concentration of demand for labour and where workers can

change their jobs without changing residence”.

Robinson (quoted in Goodman, 1970: 183) considers a definition of the labour

market by the employer's perspective: “geographical area containing those members of

the labour force, or potential members of the labour force that a firm can induce to enter

its employ under certain conditions, and those other employers with whom the firm is in

competition for labour”.

The definition of the labour market at a local level is related to certain issues

that do not arise so clearly in an analysis at the national level. In the following section,

we try to address some of these specificities that should be taken into account, in the

design of the local labour market policies, in order to minimize its negative effects.

2.2. The challenges of local labour markets

Considering the definitions of local labour market mentioned previously, there

are certain characteristics in its nature that are worthwhile to discuss. Firstly, its name

“local” refers to a peculiarity of this market: the spatial segmentation. According to

Centeno and Novo (2012: 1), segmentation in a general way is “the result of constraints

that depart the labour market from an efficient balance (...) where workers and

employers come together to maximize productivity and where the equilibrium wage

promotes productivity growth”. Once the segmentation of labour market means a

subdivision in local markets, Weller (2007) considers that this phenomenon has social,

technical and spatial character, allowing the description of workers and jobs, as well as

the relationships established between them in these different markets.

Topel (1986) reports another specificity: the sensitivity of wages to local market

conditions. The author considers that an increase in the local demand for labour raises

10

wage levels in that locality and also that wages are more flexible in response to

temporary changes in local market conditions than to permanent changes.

As issues of local labour markets, Campbell (2000) highlights the phenomenon

of localized long-term unemployment and located unequal employment growth. Jackson

and Jones (1973) also address both the problem of local unemployment and wage levels

in these markets, whereas the relationship between the two variables is influenced by

changes in the spatial dimension of the labour market and the elasticity of labour in

what concerns to its adaptability to other occupations due to the economic conditions,

which often cause the need to change jobs. The authors believe that the unemployment

rates observed in certain geographical areas also reflect the economic conditions of a

country, with the wage changes observed in the different phases of an economic cycle

affecting primarily sectors such as manufacturing, which are more cycle sensitive. The

activity sectors are also referred by Cabugueira (2000) once, if the local market operates

in sectors that are stagnating or declining, the unemployment will be a reality and the

wages will decrease. The degree of mobility of capital and labour may be another

problem of local labour markets (Cabugeira, 2000), because in general there is greater

mobility at a national level. Christaller (quoted in Briney, n.d.), in the development of

the theory of central places, indirectly addresses this issue, stating that for a site to

remain active there must be a minimum number of people.

Considering that an “unemployment policy cannot only be considered from a

national perspective”, once its success “depends on the regional labour market

conditions” (Binet and Facchini, 2013: 421), the “Leed Programme” (OECD, 2013)

aims to identify possible proposals for the unemployment decrease. The first

recommendation is that regional competitiveness of markets and the available

workforce (in terms of composition and performance) “must take into account

demographic changes (ageing population) and the particularities of the unemployment

rate”.

Campbell (2000) also seeks to highlight the importance that developing a policy

of active local labour market has regarding the reduction of unemployment and the

creation of new job opportunities. A good policy, according to the author, leads to the

reintegration of the long-term unemployed into the working force. Campos (1995)

11

believes that local development is possible through the combination between

endogenous factors such as resource mobilization, and exogenous, like the conditions of

labour relocation for the ability to attract capital.

In order to obtain external financing, it is necessary to take into account that

investors consider obtaining productivity gains through the degree of technological

innovation and have a special interest in areas with flexible employment and low wage

levels (Santos et al. quoted in Campos, 1995).

2.2.1 Labour mobility

Goodman (1970) defines the labour market as a multitude of various sub-

markets that are distinguished by, for example, the demographic structure of the

population, which is influenced by workflows. Mobility is seen as “a mechanism of

adjustment of the economy with potentially positive impact on reducing unemployment

and increasing employability”, which implies a territorial change of the workplace

(Gomes and Almeida, 2010: 1). Also Kerr (quoted in Goodman, 1970) considers that

the labour market is structured and its main divisions are in terms of geographical area,

occupations and business sector criteria with labour moving spatially considering these

three factors. Geographic mobility cannot be regarded as random, once it is influenced

by various conditions, including the economic and social context, which is the source of

most of the flows of workforce (Peixoto, 1998).

Carlsen et al. (2013) developed a study about the relationship between regional

mobility and schooling. The results suggest that low mobility, especially for workers

with less education, may contribute to the increase in the overall unemployment for this

educational level, thus contributing to accentuate regional differences in unemployment.

Concerning the relationship between wages and mobility, the authors consider two

hypotheses: if the work is static, local real wages should increase enough to induce local

workforce to fill new jobs; if the labour is mobile, an increase in local labour demand

causes an increase in the flow of workers moving into this market, reducing the increase

needed in new jobs salaries. Labour mobility will thus limit the increases in real wages.

12

A particular type of mobility is the commuting, that can be translated into daily

home-work flows and so does not require a change of the place of residence. The issue

of commuting is more associated with local labour markets because these tend to be

more open, once the cultural and administrative barriers tend to be weaker than at the

national level.

The study of the population’s commuting seeks to determine which the social

and economic territorial level transformations, caused by this phenomenon, are.

According to Gomes (2013: 2), the commuting has impact “on the management of

networks and transport systems, in the labour and housing markets, in the

environmental quality of the territories and in the management of water and waste

infrastructure, influencing the configuration of territories, in the relations and dynamics

space and even in the populations quality of life“. The most common changes are at the

level of infrastructure and public transport system, which normally are developed to

reduce the time of travel of the population. In this sense, “commuting assumes a

strategic importance in the dynamics of the territory and in the quality of the

population’s life as well as in the definition and the implementation of public policies in

planning urban and regional development” (Gomes, 2013: 1). In this sense, it can be

stated that commuting between home and work “promotes relations between the

representative spaces of different functions, among which a stronger and sometimes

problematic link is established, due to the remoteness and the travelling time that, in

practice, translates into higher economic costs and lower quality of life of citizens”

(INE, 2003: 3).

2.2.2 Skills mismatch

The skills mismatch is another problem of local labour markets and may have

different causes: people with low qualifications (usually obtained when there is only

frequency of compulsory education without recourse to lifelong training to upgrade

knowledge) or with no relevant abilities for the modern labour market, especially in the

case of older workers whose skills may be outdated. In local labour markets the variety

of career opportunities may be limited, often because the business combination is

13

restricted and more homogeneous. This event along with the information asymmetry

that may exist on the real skills firms need leads to another common problem: the fact

that young people do not choose fields of study that are needed by firms. Carlsen et al.

(2013) concluded that European economies have large variations in unemployment rates

by region and by level of education, and that disparities in regional unemployment rates

decrease as the level of qualification increases. According to Lucas (quoted in Moretti,

2010), if in a local labour market workers choose to increase their level of education,

the firms in that location will increase their investment in production, expecting to hire

these workers and thus benefit from their greater competence.

In order to keep workers present in the labour market, with relevant and updated

qualities, lifelong learning assumes a great importance. According to the European

Lifelong Learning Initiative (Watson, 2003: 3), lifelong learning is a “process which

stimulates and empowers individuals to acquire all the knowledge, values, skills and

understanding they will require throughout their lifetimes and to apply them with

confidence, creativity and enjoyment, in all roles circumstances, and environments”. If

the process of lifelong learning does not exist, employers recruit migrants, attracting

graduates from other regions, or they move to where the potential labour supply is

larger and diverse. So, the existence of skills mismatches at the local level may be more

easily softened by workers and firms mobility.

At the same time, it is often difficult for educational institutions to meet all the

needs of employers and therefore there may be little connectivity between skills demand

and supply in certain activities.

The unemployment caused by the mismatch of competencies may have its origin

in factors such as “changes in market structure, information asymmetry caused by

inefficient job matching processes such as employment agencies, and lack of workforce

flexibility in terms of geographic mobility, wages and skills” (Adams et al., 2000: 2).

According to Binet and Facchini (2013:432), “if wages are not flexible enough or firms

do not adapt their jobs to workers characteristics, a mismatch will tend to persist”.

The analysis of the skills mismatch on the supply side, so far analyzed, should

be complemented with an analysis of the demand side, as claimed by Adams et al.

(2000). The authors consider that, on the demand side, the unemployment, in local

labour markets, is due to characteristics of the employer such expectations of hiring,

14

wages and working conditions offered, the sector in which he operates, his reputation,

inadequate recruitment practices, the nature of vacancies and the time it takes to fill

them.

2.2.3 The output-unemployment relation

The relation between output and unemployment has as its primary reference the

Okun's Law (dated 1962), which is an empirical finding stating an inverse relationship

between the unemployment rate and real output (Adanu, 2002). The law “presupposes

essentially a macroeconomic correlation between the level of economic activity in the

goods market and that in the labour market over the business cycle” (Kangasharju et al.,

2012: 1).

According to Okun, for each percentage point reduction in the unemployment

rate or gap in relation to the natural unemployment rate, real GDP grows (or deviates

from potential output) in the order of 3%. The question is whether the Okun's law can

also be checked at the regional level, once the local labour markets are generally more

open. In this sense, the economic fluctuations must have greater intensity, to cause a

given change in the unemployment rate. According to Blackley (1991), the spatial

variation in Okun’s law can be related to aspects such as the distribution and

participation of the labour force, the average weekly hours per worker, the labour

productivity and tax policy decisions. The author consider that “states which experience

large fluctuations in unemployment, in response to output changes, are more dependent

upon manufacturing production and have older, slower growing labour forces” and

“tend to be states with high personal income tax burdens” (Blackley, 1991: 642).

Binet and Facchini (2013) developed a study about the regional unemployment

disparities, estimating, for this purpose, the Okun coefficient for 22 regions of France,

between 1990 and 2008. For 8 of the regions studied, the Okun's law has not been

verified. The failure to ensure the law can be caused by the existence of regional

parrticularities in the economic policy, in the conditions of the labour market or in the

population’s structure. Also Christopoulos (2004) conducted a study to estimate the

Okun's law for Greece at a regional level. For regions where the Okun's law is checked,

15

the author proposes the adoption of economic policies related to demand management

in order to reduce the gap between unemployment and output growth. Binet and

Facchini (2013) refer to the need for policy-specific intervention to reduce

unemployment in regions where the Okun’s law is not verified and Christopoulos

(2004) considers that, in this case, policies oriented to the structural change of the

labour market should be adopted. Briefly it can be assumed that the relationship

between unemployment rate and real output growth proposed by Okun should be taken

into account to draw intervention policies at regional level to reduce the asymmetries.

According to Christopolous (2004), in regions where the Okun’s law is confirmed, in

order to reduce the level of unemployment in these regions, efficient demand

management policies should be adopted.

The fight against unemployment will bring additional benefits to the local

economy, once that, according to Okun, if the unemployment rate declines, an increase

in the hours worked and in the labour productivity is expected (Adanu, 2002).

Given the higher volatility of the economic activity at a local level

(Christopoulos, 2004), it is important to analyse the Okun's law taking into account the

specificities of local labour markets, in order to devise policy interventions that increase

the labour productivity (that in these markets, given its size, is always more limited

when compared with the national productivity) in these markets.

16

3. The literature on the Portuguese labour markets

This section seeks to briefly address some of the particularities of the local

labour markets in Portugal. Regarding its regional structure, one of the first features that

should be noted is the gradual concentration of economic activities in the mainland

coast, which has been more clearly observed since the beginning of the second half of

the twentieth century (Campos, 1995). In this sense, Guerreiro and Caleiro (2005)

developed a research where they tried to study some variables to conclude on the

economic distance between the Portuguese regions. The analysis of the per capita

purchasing power of Portuguese municipalities allowed the conclusion that Lisboa and

Porto are seen as central places, especially because these two regions hold a higher

purchasing power and are surrounded by more underprivileged municipalities. The two

metropolitan areas centred in Lisboa and Porto also have a large population

concentration (Moreira and Rodrigues, 2004). For instance, the resident population in

the metropolitan area of Porto increased 8% between 1991 and 2001 (Varejão et al.,

2008). In the same period, the population growth of the metropolitan area of Lisboa was

6% (INE, 2013). Unlike these two metropolitan areas, the inner territory has low

population density, as a result of depopulation that has suffered in recent years. The

Alentejo, for example, presented a population decrease of 2.5% between 2001 and 2011

(INE, 2013).

This trend means an increasing abandonment of the inner region and a possible

excessive agglomeration in coastal regions. In fact, “the national population dynamics

shows the asymmetry of urban development” characterized by the “concentration of

population in the coast between Viana do Castelo and Setúbal and the Algarve

coastline” (INE, 2013: 29); specifically, in the Algarve, the population growth has

increased 14.1% between 2001 e 2011 (INE, 2013: 28). This growth is due, in part, to

the increase of foreign residents, representing, in 2011, about 12% of the regional

population (INE, 2012). The immigration to the Algarve may be related to the fact that

the region provides a good climate, a strong tourism activity and a good quality of life,

not requiring very high purchasing power. In this sense, it is expected that the labour

market in the Algarve region consists of a large labour supply of foreign residents.

17

Another tendency that can be observed, in recent decades, is the emergence of

new urban centres of average size in the inner region, such as Bragança, Vila Real,

Viseu, Castelo Branco, Évora and Beja (INE, 2013: 30). As for the autonomous regions,

the highest population density in Madeira is verified in the northern part, and in the

Azores the highest values are observed in the island of São Miguel and Terceira (INE,

2013: 26).

The bipolarization of population and economic activities around the two

Portuguese metropolitan areas is leading to the worsening of the asymmetries between

the various local labour markets.

Another trend that has been observed over the past decades, in Portugal, is the

ageing population increase. In the decade between 1991 and 2001, for example, “the

proportion of young people decreased from 20% to 16%, while the elderly increased

from 13.6% to 16.4%” (Carrilho and Gonçalves, 2004). According to data obtained

from the 2001 Census, the Azores and the sub-regions of the North hold a higher

proportion of young people comparing to the oldest and in Alentejo and Centro occurs

the reverse situation (Carrilho and Gonçalves, 2004). In the regions that experience a

growing ageing population, the labour supply is more limited in quantitative and

qualitative terms. In regions where we witness the reverse situation, the labour supply is

larger and more diverse, so firms have greater option of choice when it comes to

recruitment.

In Portugal the labour market is becoming more flexible, thanks to the increase

in short-term contracts, which may mean greater social inequality when compared with

the prevalence of permanent contracts, which guarantee more social protection and

higher compensation on retirement. However, the increase of labour market’s flexibility

also has advantages, since it allows a faster adjustment between work supply and

demand (the needs of workforce can be met faster and with less costs to the firms, once

the employment contracts with shorter duration allow the employers to dismiss workers

without higher costs when compared with permanent contracts). Also the emergence of

new forms of work contributes to the increase of labour disparities, such as temporary

work, which is often associated with more instability.

18

All the above factors (population concentration, ageing population and labour

flexibility) contribute to the segmentation of the labour market, not only in a space

perspective but also economically. In this sense, Pereira (1997) refers the existence of

forty labour markets at a sub-regional level in Portuguese mainland, which are nothing

more than groups of homogeneous municipalities classified as employment areas. These

groupings allow the drawing of some conclusions about the commuting taking place in

the Portuguese mainland. According to Pereira (1997), most home-work movements

occur at an internal level in these employment areas. At the municipal level, 29% of the

population moved, in 2011, to another municipality for work or study, reflecting the

trend of “spatial contiguity in intercity mobility conditions” (INE, 2013: 64). In this

sense, “the intensification of migration flows between places of residence and work

promotes and enhances a high relationship between neighbouring municipalities”

(Pisco, 1997: 6). Based on commuting observed in the early 90s, Pisco (1997) also

predicts the existence of “geographical units of employment”, which are municipalities

that act as poles of attraction, since they offer better job opportunities. In Portugal, the

author predicts the existence of 33 geographical units of employment, with relevance

for Lisboa, Porto and Aveiro.

The access to unemployment benefits is seen as being rather limited, due to

various constraints such as the requirement of a minimum of contributions, which

results in increased difficulty for workers who are considered at risk in situations with

short-term contracts duration to ensure their survival in periods between the end of the

contract and the finding of a new job (Centeno and Novo, 2012).

As for labour productivity, Lopes (2004) conducted a study for the NUTS 3

level regions in order to test the hypothesis of regional convergence for this variable in

the period between 1990 and 1999. This analysis considered both the productive

structure and the sector structure of each region studied and allowed reinforcing the idea

of the existence of regional convergence in manufacturing and in the services sector.

Regarding the labour productivity at the aggregate level, there is indication of regional

convergence, directly related to situations of geographical mobility of labour between

sectors of activity, which causes changes in regional employment structures.

19

4. An empirical assessment of the Portuguese labour markets

The existence of spatial asymmetries in the labour market may have, as noted

previously, factors of diverse nature in its origin. In general, the existence of these gaps

acts as an obstacle to a balanced economic growth. It becomes therefore crucial to

identify them, in order to delineate local based practices to combat the causes of

negative spatial disparities.

In this chapter, we try to analyse, in a first stage, a set of 30 indicators reported

to the same timeline, in this case 2011, to clarify the spatial disparities in the Portuguese

labour market, at the municipal level, regarding the 308 Portuguese municipalities. We

perform a principal components factor analysis in order to extract the main latent

dimensions related to the labour market present in the data. In the next stage, a cluster

analysis is performed on the principal components extracted from the factor analysis.

The goal is to finally obtain groups of municipalities that are homogeneous regarding

the main features of the labour market.

4.1 . Data and variables

The first step was to collect a set of relevant indicators. The relevance of the

data is dependent upon its association with the specific issues regarding local labour

markets previously discussed – labour mobility, skills mismatch and the output-

unemployment relation. It is also important to mention that the data must be available at

the municipal level and refer to a single year, in this case 2011, a census year. In order

to leave aside the influence of the municipal dimension, the indicators are defined in

relative terms (essentially, in proportion or per capita terms). Given these conditions,

30 indicators were selected. Table 1 lists the selected indicators and the metadata. The

source of indicators 1 to 28 is the 2011 population census while indicator 29 is provided

by the Study on the Local Purchasing Power; as for indicator 30, the source are the Lists

of Personnel of the Ministry of Economy and Employment.

20

Table 1. List of the selected indicators

Label Name Measurement

unit Description

Ind01

Average time spent on

commuting of employed or

student resident population

Minutes

(Total of persons in class j x midpoint of class j) / Employed

or student resident population [Considered classes

(respective weight): none (0); up to 15 minutes (7,5); 16 to

30 minutes (23); 31 to 60 minutes (45,5) and more than one

hour (90)]

Ind02




using collective mode of

transport

Minutes

(Employed or student resident population using individual

transport in class j x midpoint of class j) / Employed or

student resident population using collective mode of

transport [Considered classes (respective weight): none (0);

up to 15 minutes (7,5); 16 to 30 minutes (23); 31 to 60

minutes (45,5) and more than one hour (90)]

Ind03




using individual mode of

transport

Minutes

(Employed or student resident population using individual

transport in class j x midpoint of class j) / Employed or

student resident population using individual mode of

transport [Considered classes (respective weight): none (0);

up to 15 minutes (7,5); 16 to 30 minutes (23); 31 to 60

minutes (45,5) and More than one hour (90)]

Ind04 Ageing ratio No.

Resident population with 65 and more years old / Resident

population aged between 0 and 14 years old

x 100

Ind05 Social diversity ratio No.

Reflects a measure of the degree of diversity of a territorial

unit. It varies between 0 (maximum specialization) and 1

(maximum diversity). For each territorial unit, the

calculation of this indicator is established by the following

steps: 1) the product between the weight of each

socioeconomic group in the territorial unit’s population and

the natural logarithm of this weight; 2) the sum of the values

obtained for every socioeconomic group; 3) the division by

the natural logarithm of the number of socioeconomic

groups considered; 4) the adoption of the symmetrical value

obtained.

Ind06 Renewal index of the population

in active age No. Resident population aged between 20 and 29 years old /

Resident population aged between 55 and 64 years old x 100

Ind07 Potential sustainability ratio No. Resident population aged between 15 and 64 years old /

Resident population with 65 and more years old

x 100

Ind08 Commuting mobility of

employed population %

(Employed resident population outside the territorial unit +

Employed non-resident population in the territorial unit) /

Employed resident population x 100

Ind09

Proportion of employed

population outside the territorial

unit

% Employed resident population outside territorial unit /

Employed resident population x 100

Ind10

Proportion of student population

using pedestrian mode in

commuting

% Student resident population accessing education by

pedestrian way / Student resident population x 100

Ind11

Proportion of non-resident

population employed in the

territorial unit

% Non-resident population employed in the territorial unit /

Employed population in the territorial unit x 100

Ind12

Proportion of resident

population with higher

education completed

% Resident population with 21 and more years old with higher

education completed / Resident population with 21 and

more years old x 100

Ind13


population aged between 18 and

24 years old with the lower

secondary education 3rd cycle

completed not attending

educational system

%

Resident population aged between 18 and 24 years old with

the lower secondary education 3rd cycle completed not

attending educational system / Resident population aged

between 18 and 24 years old x 100

21

Label Name Measurement

unit Description

Ind14



34 years old with at least higher

education completed

% Resident population aged between 30 and 34 years old with

at least higher education completed / Resident population

aged between 30 and 34 years old x 100

Ind15



15 years old not attending the

educational system

% Resident population aged between 6 and 15 years old not

attending educational system / Resident population aged

between 6 and 15 years old x 100

Ind16


population with 15 and more

years old without any level of

education completed

% Resident population with 15 and more years old without any

level of education completed / Resident population with 15

and more years old x 100

Ind17 Proportion of resident

population of foreign nationality % Resident population of foreign nationality / Resident

population x 100

Ind18

Proportion of employed or

student resident population with

average time spent on

commuting below 31 minutes

% Employed or student resident population with average time

spent on commuting below 31 minutes / Employed or

student resident population x 100

Ind19




transport in commuting

% Employed or student resident population accessing

job/education by collective transport way / Employed or


Ind20



using individual mode of


% Employed or student resident population accessing

job/education by individual transport way / Employed or


Ind21


population that comes in the

territorial unit (commuting)

% Resident population that works or studies in the territorial

unit but resides in a different territorial unit / Resident

population in the territorial unit x 100

Ind22


population that comes out the

territorial unit (commuting)

% Resident population that works or studies in a different

territorial unit / Resident population in the territorial unit x

100

Ind23


population that works or studies

in other municipality

% Resident population that works or studies in other

municipality / Resident population that works or studies x

100

Ind24 Proportion of car usage on daily

journeys %

Employed or student resident population using the car as

driver or as a passenger on daily journeys / Employed or


x 100

Ind25 School leavers rate % Resident population aged between 10 and 15 years old who

left school without attaining lower secondary education /

Resident population aged between 10 and 15 years old x 100

Ind26 Activity rate of resident

population % Active population / Resident population x 100

Ind27 Illiteracy rate % Resident population with 10 and more years old who does

not know to read or write / Resident population with 10 and

more years old x 100

Ind28 Unemployment rate % Unemployed population / Active population x 100

Ind29 Per capita purchasing power Portugal = 100 Synthetic indicator expressing the purchasing power level in

a municipality, with regard to the national average.

Ind30 Average monthly earning Euros

Regular net amount in cash or in kind paid to the worker in

a month, by virtue of time spent working or work supplied

during normal and extra working hours. Includes payment

of hours paid but not worked (holidays, bank holidays and

other paid absences from work). Data refers to full time

employees with full remuneration.

22

The values for the average monthly earning for the municipalities of Azores,

which were not available, were estimated on the basis of the growth rate between 2009

and 2011 (the 2010 data are not available as well) of the indicator for the Portuguese

mainland. The growth rate was subsequently applied to the value of the indicator for

each Azorean municipality in 2009, in order to estimate the value for 2011.

The next step was the development of an exploratory analysis. The linear

correlation analysis aims at studying the relationship between variables, i.e., whether

the variables move in the same or in opposite directions or are not correlated at all.

Considering this definition as a starting point, we proceeded with the construction of the

linear correlation matrix, encompassing the 30 indicators presented in Table 1 and their

values for the 308 Portuguese municipalities. There was no need for treatment of

missing values, since the values of average monthly earning for the municipalities of

Azores were estimated as described above.

After the analysis of the linear correlation matrix (Table 2), we chose to exclude

some of the 30 indicators originally selected for reasons of various kinds. From the 9

excluded indicators, 7 are related to the commuting of the population, which reflects the

fact that this dimension was oversized in the initial set of indicators. In fact, previously,

we had concluded that commuting was related to 14 of the 30 initial indicators.

Indicators 2 and 3, which refer to the average time spent on commuting of

employed and student resident population considering the type of transport, have a

correlation of 0.80 and 0.89, respectively, with the indicator 1, which suggests strong

conceptual redundancy, and, so, were excluded from the analysis.

It was also decided to exclude the indicator 8, which is related to the commuting

of employed population since it is very concise and there are other indicators that have

the same information in a less comprehensive manner such as indicators 9 and 11.

Once the indicator 10 refers to a specific group, which is the student population

and it is not directly relevant for the analysis in question, we decided for its exclusion as

well.

It was further decided to exclude indicator 14 as the information is more

restricted compared to indicator 12 and the linear correlation between the two indicators

is 0.91.

23

In what concerns indicator 16, which focuses on the population without

schooling, which is an essential factor in the analysis of the labour market, it was

decided by its exclusion since it has a strong correlation (0.96) with the indicator 27,

which deals with the same theme, but without restricting the analysis to an age group.

Regarding indicator 18, which refers to the average time spent on commuting

below 31 minutes, it shows a strong linear correlation with the indicator 1, which

provides more embracing information and does not consider a limit for the population’s

average time of commuting. So, we decided for the exclusion of indicator 18.

Finally, indicators 22 and 23 were excluded from the analysis, once they present

a strong linear correlation with indicator 9, in the order of 0.97, which reflects the

conceptual redundancy of these three indicators.

24

Table 2. Linear correlation matrix

Label Ind01 Ind02 Ind03 Ind04 Ind05 Ind06 Ind07 Ind08 Ind09 Ind10 Ind11 Ind12 Ind13 Ind14 Ind15 Ind16 Ind17 Ind18 Ind19 Ind20 Ind21 Ind22 Ind23 Ind24 Ind25 Ind26 Ind27 Ind28 Ind29 Ind30

Ind01 1.00

Ind02 0.80 1.00

Ind03 0.89 0.65 1.00

Ind04 -0.21 -0.16 -0.02 1.00

Ind05 -0.17 -0.16 -0.01 0.29 1.00

Ind06 0.04 -0.01 -0.08 -0.73 -0.33 1.00

Ind07 0.19 0.16 -0.03 -0.81 -0.48 0.86 1.00

Ind08 0.56 0.52 0.42 -0.27 -0.27 0.08 0.29 1.00

Ind09 0.75 0.60 0.66 -0.33 -0.24 0.15 0.03 0.71 1.00

Ind10 -0.08 0.22 0.01 -0.02 0.01 0.10 0.21 -0.04 -0.09 1.00

Ind11 0.41 0.46 0.25 -0.23 -0.27 0.04 0.34 0.94 0.55 -0.02 1.00

Ind12 0.32 0.38 0.13 -0.41 -0.08 0.15 0.37 0.48 0.22 0.03 0.50 1.00

Ind13 -0.21 -0.24 -0.19 -0.24 -0.11 0.51 0.14 -0.22 -0.16 0.25 -0.22 -0.40 1.00

Ind14 0.28 0.33 0.12 -0.19 0.01 -0.06 0.00 0.43 0.20 -0.04 0.46 0.91 -0.56 1.00

Ind15 -0.05 0.00 -0.06 0.16 0.06 0.01 -0.72 -0.08 -0.14 0.25 -0.04 -0.03 0.22 -0.05 1.00

Ind16 -0.26 -0.34 0.01 0.76 0.38 -0.49 0.09 -0.43 -0.35 -0.02 -0.43 -0.69 0.06 -0.52 0.12 1.00

Ind17 0.14 0.22 0.05 -0.16 0.03 -0.03 -0.15 0.12 0.06 0.17 0.15 0.31 -0.08 0.19 0.08 -0.28 1.00

Ind18 -0.97 -0.78 -0.88 0.17 0.14 -0.02 0.23 -0.51 -0.70 0.01 -0.38 -0.29 0.18 -0.26 0.04 0.23 -0.19 1.00

Ind19 0.65 0.26 0.52 -0.18 -0.17 0.22 0.09 0.29 0.41 -0.23 0.18 -0.01 0.17 -0.05 -0.01 -0.03 -0.06 -0.62 1.00

Ind20 -0.15 -0.09 -0.25 -0.20 -0.04 -0.02 0.18 0.10 0.08 -0.57 0.14 0.29 -0.40 0.30 -0.22 -0.35 0.04 0.20 -0.48 1.00

Ind21 0.16 0.24 0.02 -0.21 -0.20 0.06 0.37 0.78 0.15 0.04 0.83 0.62 -0.22 0.56 -0.02 -0.43 0.15 -0.13 0.03 0.12 1.00

Ind22 0.70 0.60 0.58 -0.41 -0.25 0.21 0.19 0.71 0.97 -0.09 0.57 0.29 -0.16 0.25 -0.16 -0.45 0.11 -0.66 0.34 0.15 0.18 1.00

Ind23 0.70 0.59 0.62 -0.22 -0.19 0.05 0.09 0.69 0.97 -0.11 0.54 0.18 -0.20 0.18 -0.14 -0.28 0.07 -0.66 0.34 0.10 0.12 0.97 1.00

Ind24 -0.14 -0.11 -0.24 -0.18 -0.03 -0.03 0.01 0.08 0.05 -0.58 0.12 0.30 -0.44 0.32 -0.19 -0.34 0.03 0.20 -0.45 0.98 0.13 0.12 0.07 1.00

Ind25 -0.04 0.00 -0.03 0.13 0.10 0.06 0.74 -0.07 -0.13 0.20 -0.03 -0.04 0.21 -0.06 0.87 0.12 0.02 0.03 0.03 -0.19 -0.01 -0.15 -0.14 -0.16 1.00

Ind26 0.23 0.30 0.01 -0.80 -0.40 0.54 -0.69 0.38 0.33 0.13 0.38 0.59 0.02 0.40 -0.11 -0.85 0.36 -0.21 -0.06 0.32 0.37 0.46 0.28 0.30 -0.13 1.00

Ind27 -0.22 -0.26 0.07 0.75 0.38 -0.47 0.21 -0.40 -0.33 0.09 -0.40 -0.62 0.06 -0.46 0.16 0.96 -0.24 0.18 -0.04 -0.41 -0.39 -0.42 -0.26 -0.39 0.16 -0.79 1.00

Ind28 0.18 0.16 0.23 -0.22 -0.14 0.18 0.33 0.09 0.18 0.35 0.01 0.00 0.17 -0.08 0.17 -0.06 0.08 -0.16 0.19 -0.35 -0.03 0.10 0.10 -0.36 0.14 0.11 0.02 1.00

Ind29 0.27 0.38 0.08 -0.39 -0.15 0.14 0.30 0.55 0.15 0.13 0.60 0.89 -0.32 0.77 0.01 -0.68 0.36 -0.26 -0.06 0.26 0.75 0.22 0.11 0.26 -0.01 0.63 -0.60 0.03 1.00

Ind30 0.25 0.31 0.07 -0.32 -0.07 0.14 0.23 0.48 0.14 0.13 0.54 0.64 -0.17 0.54 0.05 -0.52 0.31 -0.25 0.04 0.15 0.57 0.21 0.12 0.15 0.08 0.49 -0.46 -0.03 0.75 1.00

25

In conclusion, the linear correlation analysis led to the exclusion of 9 out of the

30 initial indicators, due to conceptual redundancies supported by significant linear

correlation coefficients. Thus, the database was restricted to 21 indicators, which will be

the input for the factor analysis addressed in the following section.

4.2 Factor analysis

The principal components factor analysis aims at reducing the initial number of

variables, aggregating them into new synthetic variables called factors that seek to

explain a large proportion of the total variance of the original variables (Maroco, 2007).

Therefore, the method used to carry out the study of the different indicators was to

consider the existence of n variables (in this case 21) and p factors, with a relationship

between them translated into p < n. This aggregation allows synthesizing information

on principal components, which are no more than a set of new ortogonal variables (INE,

2004a; INE, 2004b). In order to better understand the relationship between the initial

variables and the principal components, the varimax rotation method was used, with the

aim of maximizing the variation between weights of each principal component. This

method performs a change of coordinates, seeking to maximize the correlations between

the variables and the principal components. The quartimax method, also widely used,

minimizes the number of factors necessary to explain a variable. This method of

rotation generates a general factor on which most variables are loaded to a high degree.

Such a factor structure is usually not helpful to the purpose of this study and this is why

a varimax rotation was used instead of a quartimax one.

Regarding the study here presented, population weights on the municipal level

were not used for the factor analysis because its application is justified only for a

smaller scale territorial dimension (INE, 2004a; INE, 2004b) and not to the municipal

level (INE, 2013). As for missing values, given their pre-treatment, regarding the

average monthly earning for the municipalities of the Azores, described in section 4.1.,

they do not exist in the database.

As a starting point, we will consider the results obtained after the exploratory

analysis of the correlations matrix, which led to a narrower set of initial indicators. A

26

principal components factor analysis was then applied to the smaller set of 21

indicators.

As the Bartlett's test, conducted to determine the homogeneity or heterogeneity

of variances between variables, led to a null p-value, we can conclude that the variables

are significantly correlated. As for the Kaiser-Meyer-Olkin (KMO) statistic, it indicates

the ratio of the data variance that can be considered common to all variables and, for

this reason, can be attributed to a common factor. Thus, for the factor analysis, a KMO

value greater than 0.5 is considered acceptable. The value obtained was 0.740, as we

can see in Table 3, which suggests that the performance of a factor analysis is

appropriate.

Table 3. KMO and Bartlett’s test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy

0.740

Bartlett’s Test of

Sphericity

Approx Chi-Square 6534.450

df 210

Sig. 0.000

The communalities, displayed in Table 4, represent the proportion of the

variance of each variable that can be explained by the principal components. For the

analysis, it is generally consider as acceptable to extract values greater than 0.5.

According to Table 4, indicators 5 and 28 have communalities of 0.447 and 0.391

respectively. In spite of the values for these two indicators being below 0.5, they are

relevant for the characterization of local labour markets, in particular the unemployment

rate, and therefore it was decided to keep them in the analysis. In this sense, except for

these two indicators, the percentage of each variable variance explained by the factors is

greater than 50%, since the initial variables are standardized and thus have a variance of

one.

27

Table 4. Communalities

Label Name Initial Extraction

Ind20 Proportion of employed or student resident population using individual mode of

transport in commuting 1.000 0.942

Ind24 Proportion of car usage on daily journeys 1.000 0.941

Ind25 School leavers rate 1.000 0.932

Ind15 Proportion of resident population aged between 6 and 15 years old not attending

the educational system 1.000 0.928

Ind07 Potential sustainability ratio 1.000 0.917

Ind29 Per capita purchasing power 1.000 0.913

Ind21 Proportion of resident population that comes in the territorial unit (commuting) 1.000 0.893

Ind01 Average time spent on commuting of employed or student resident population 1.000 0.886

Ind26 Activity rate of resident population 1.000 0.869

Ind04 Ageing ratio 1.000 0.858

Ind09 Proportion of employed population outside the territorial unit 1.000 0.838

Ind06 Renewal index of the population in active age 1.000 0.834

Ind11 Proportion of non-resident population employed in the territorial unit 1.000 0.826

Ind27 Illiteracy rate 1.000 0.809

Ind12 Proportion of resident population with higher education completed 1.000 0.797

Ind13 Proportion of resident population aged between 18 and 24 years old with the

lower secondary education 3rd cycle completed not attending educational system 1.000 0.745

Ind19 Proportion of employed or student resident population using collective mode of

transport in commuting 1.000 0.722

Ind30 Average monthly earning 1.000 0.678

Ind17 Proportion of resident population of foreign nationality 1.000 0.663

Ind05 Social diversity ratio 1.000 0.447

Ind28 Unemployment rate 1.000 0.391

Considering the Kaiser criterion, which suggests the selection of the components

with eigenvalues greater than 1, there are six principal components that, in total, explain

80.1% of the total variance of the initial data (Table 5).

28

Table 5. Total variance explained matrix

The scree plot shows the relationship between the number of extracted

components and the eigenvalues associated with them. Regarding the number of factors

Component

Initial eigenvalues Rotation sums of squared loadings

Total % of variance Cumulative % Total % of variance Cumulative %

1 6.338 30.182 30.182 4.213 20.060 20.060

2 3.388 16.132 46.314 4.067 19.368 39.428

3 2.572 12.245 58.559 2.843 13.536 52.964

4 2.138 10.179 68.738 2.401 11.431 64.395

5 1.264 6.017 74.756 1.961 9.340 73.735

6 1.130 5.383 80.139 1.345 6.404 80.139

7 0.883 4.207 84.346

8 0.743 3.540 87.885

9 0.617 2.936 90.822

10 0.388 1.849 92.671

11 0.360 1.714 94.385

12 0.263 1.253 95.638

13 0.232 1.104 96.742

14 0.179 0.851 97.594

15 0.124 0.591 98.185

16 0.113 0.539 98.724

17 0.092 0.440 99.164

18 0.063 0.299 99.463

19 0.052 0.250 99.713

20 0.043 0.207 99.920

21 0.017 0.080 100.000

21 100.000

29

to retain, it must be considered, as a reference point, the inflection of the curve and

components with eigenvalues greater than 1. According to the interpretation of the scree

plot (Figure 1) the inflection occurs from component 4. In this sense, we could have

opted for the extraction of only 4 components, but we decided for the extraction of 6,

following the Kaiser criterion.

Figure 1. Scree plot

The rotated component matrix displays the loadings after rotation, representing

the correlation between each component and each variable (Table 6). Variables with

high absolute values of loadings are important to describe the component. The

correlation between the indicators and the components can be positive or negative, if the

rise of one of the variables follows the increase or decrease of the other, respectively.

30

Table 6. Rotated component matrix

Label Name

Component

1 2 3 4 5 6

Demographic

sustainability Attractiveness Qualification Commuting

School

leaving

Foreign

population

Ind07 Potential sustainability ratio

0.937 0.147 -0.026 0.120 0.042 -0.006

Ind06 Renewal index of the population in active age

0.895 -0.016 -0.144 -0.020 0.065 -0.087

Ind04 Ageing ratio -0.870 -0.172 -0.064 -0.144 0.119 -0.182

Ind26 Activity rate of resident

population 0.743 0.413 0.214 0.103 -0.091 0.285

Ind27 Illiteracy rate -0.686 -0.422 -0.320 -0.150 0.124 -0.143

Ind05 Social diversity ratio -0.488 -0.155 0.014 -0.208 0.054 0.373

Ind21


population that comes in

the territorial unit (commuting)

0.056 0.927 -0.002 0.013 -0.026 -0.173

Ind29 Per capita purchasing

power 0.215 0.874 0.156 0.038 0.006 0.279

Ind11

Proportion of non-resident

population employed in the territorial unit

0.067 0.791 0.049 0.387 -0.007 -0.207

Ind30 Average monthly earning 0.180 0.767 0.040 0.031 0.077 0.222

Ind12

Proportion of resident population with higher

education completed

0.216 0.757 0.227 0.130 -0.022 0.330

Ind24 Proportion of car usage on daily journeys

0.115 0.102 0.954 -0.048 -0.063 0.005

Ind20


student resident population using individual mode of


0.134 0.096 0.950 -0.054 -0.096 0.007

Ind13

Proportion of resident population aged between

18 and 24 years old with

the lower secondary education 3rd cycle

completed not attending

educational system

0.489 -0.310 -0.516 -0.268 0.198 -0.179

Ind28 Unemployment rate 0.218 -0.075 -0.432 0.218 0.153 0.283

Ind01

Average time spent on commuting of the

employed or student

resident population

0.044 0.204 -0.132 0.898 -0.028 0.130

Ind09


population outside the territorial unit

0.198 0.116 0.095 0.878 -0.069 -0.024

Ind19

Proportion of employed or student resident population



0.130 0.001 -0.521 0.642 -0.018 -0.147

Ind25 School leavers rate -0.030 0.016 -0.099 -0.038 0.959 -0.004

Ind15

Proportion of resident population aged between 6

and 15 years old not

attending the educational system

-0.046 0.018 -0.122 -0.053 0.951 0.056

Ind17

Proportion of resident population of foreign

nationality

0.063 0.240 -0.020 0.021 0.022 0.775

31

Considering the correlation previously described, the name of each component

was assigned taking into account the combination of the indicators that are most

correlated with it. In this section, it was also possible to proceed to a graphical analysis,

considering the five municipalities with the highest and lowest values for each

component.

Given that the first component relates mostly with variables that reflect

demographic characteristics, the first component was named “Demographic

sustainability”.

The demographic sustainability is correlated positively with four variables: the

potential sustainability ratio, that presents a loading of 0.937, the renewal index of the

population in active age (0.895), the activity rate of resident population (0.743) and the

proportion of resident population aged between 18 and 24 years old with the lower

secondary education 3rd

cycle completed not attending the educational system (0.489).

This component is negatively correlated with the ageing ratio (-0.870), the illiteracy rate

(-0.686) and the social diversity ratio (-0.488). Given the increasing ageing population

in Portugal, and once demographic sustainability is based on a balanced population

development (in terms of age, gender and other demographic factors), it would be

expected that this indicator had a negative relationship with the component in question.

The same can be applied, for example, to the illiteracy rate, which represents the

proportion of the resident population aged 10 or older who cannot read or write in the

total resident population. This means that this indicator varies negatively with the

illiteracy of the population.

In terms of the labour market, this analysis is particularly relevant, since it

reinforces the need of intervention to combat the population ageing and illiteracy,

factors that contribute negatively to the sustainability of the human resources and,

consequently, to the growth of the workforce in quantity and in quality, since, as seen in

section 1, the investment in human capital is the starting point for a qualified labour

force.

As shown in Figure 2, the five Portuguese municipalities that have a higher

value for the demographic sustainability are Ribeira Grande, Lagoa (Açores), Paços de

32

-4

-3

-2

-1

0

1

2

3

4

Rib

eira

Gra

nd

e

Lag

oa

(Aço

res)

Paç

os

de

Fer

reir

a

Lou

sad

a

Câm

ara

de

Lo

bo

s

Vil

a V

elh

a de

Ró

dão

Idan

ha-

a-N

ova

Ole

iro

s

Pen

amac

or

Alc

outi

m

Ferreira, Lousada and Câmara de Lobos. The first two municipalities are located in

Região Autónoma dos Açores, Paços de Ferreira and Lousada belong to the Norte

region and Câmara de Lobos to Região Autónoma da Madeira.

As for the municipalities with the lowest values, we have Vila Velha de Ródão,

Idanha-a-Nova, Penamacor, Oleiros and Alcoutim. The first four municipalities belong

to the Centro region and Alcoutim to the Algarve region.

Figure 2. Demographic sustainability: highest and lowest values

As we can see in Figure 3, in terms of the level 2 NUTS regions, the Portuguese

regions with the highest demographic sustainability are Região Autónoma dos Açores

and Norte that, as stated in Section 3, were, at the time of the 2001 Census, the regions

with less population ageing. In the Norte region, it can also be seen that the

municipalities with the highest demographic sustainability are located in the coast,

which could be explained by the high population concentration that occurs on the

33

Portuguese coast. The Norte region includes, for example, the metropolitan area of

Porto, endowed with a strong concentration of economic activities, as mentioned above.

Figure 3. Demographic sustainability per municipality

34

-2

-1

0

1

2

3

4

5

6

7

8

Lis

bo

a

Port

o

Oei

ras

Sin

es

São

Jo

ão d

a M

adei

ra

Vil

a F

ranca

do

Cam

po

Cab

ecei

ras

de

Bas

to

Bai

ão

Cel

ori

co d

e B

asto

Cin

fães

The regions with the highest concentration of municipalities with negative

values of demographic sustainability are Centro and Alentejo. In the inner Norte, there

are also many municipalities with negative values of demographic sustainability, as we

can see in the map of Figure 3. Also in Centro and Alentejo, municipalities with

negative values in this component are mainly located inside. This phenomenon may be

explained, in part, by the increasing abandonment of the inner regions of Portugal, as

mentioned in the previous section.

The second component reflects, in a balanced way, aspects of mobility and

economic activity, describing more dynamic labour markets, and so can be called as

“Attractiveness”.

Figure 4. Attractiveness: highest and lowest values

This component is positively correlated with the proportion of resident

population that commutes into the territorial, which presents a loading of 0.927, the per

capita purchasing power (0.874), the proportion of non-resident population employed in

35

N

0.2 ; 7.5-0.2 ; 0.2-0.6 ; -0.2-1.4 ; -0.6

the territorial unit (0.791), the average monthly earning (0.767) and the proportion of

resident population with higher education completed (0.757).

Figure 4 shows that the five municipalities with the highest attractiveness are

Lisboa, Porto (these two being the core of the two metropolitan areas in the country),

Oeiras, Sines and São João da Madeira. The municipalities with less attractiveness are

Vila Franca do Campo, Cabeceiras de Basto, Baião, Celorico de Basto and Cinfães. The

last four municipalities belong to the Norte region.

Figure 5. Attractiveness per municipality

36

For the attractiveness component, according to Figure 5, the overall pattern

shows a low attractiveness in the inner North of the Portuguese mainland (evidenced by

the depopulation in the inner regions) and a high attractiveness in the coast, especially

in the two metropolitan areas. It is worthwhile to notice that, as mentioned in Section 3,

the metropolitan areas of Lisboa and Porto reveal a high purchasing power (Guerreiro e

Caleiro, 2005) and a high average monthly earning. These two metropolitan areas also

have a great economic activity and, consequently, a large population’s concentration.

We can also verify that both the autonomous regions present a general pattern of

negative attractiveness. This may be due to the concentration of economic activity in the

mainland, which makes the autonomous regions less attractive in terms of firms and

employment.

The third component is positively correlated with the proportion of car usage on

daily journeys that presents a loading of 0.954 and with the proportion of employed or

student resident population using individual mode of transport in commuting (0.950).

This component, that will be named as “Qualification”, presents a negative correlation

with the proportion of employed or student resident population using collective mode of

transport in commuting (-0.521), with the proportion of resident population aged

between 18 and 24 years old with the lower secondary education 3rd

cycle completed

not attending the educational system (-0.516) and with the unemployment rate (-0.432).

As for the municipalities, according to the values shown in Figure 6, we can

conclude that Condeixa-a-Nova, Batalha, Vale de Cambra, Anadia and Mira are the

more qualified. All the municipalities, with the exception of Vale de Cambra, belong to

the region of Centro. The municipalities with less qualification are Nordeste, Mourão,

Câmara de Lobos, Lisboa and Corvo. Two of these five municipalities belong to the

Região Autónoma dos Açores (Nordeste e Corvo) and Câmara de Lobos belongs to the

Região Autónoma da Madeira.

37

-4

-3

-2

-1

0

1

2

3

Co

nd

eixa-

a-N

ov

a

Bat

alh

a

Val

e d

e C

ambra

An

adia

Mir

a

No

rdes

te

Mou

rão

Câm

ara

de

Lo

bo

s

Lis

bo

a

Co

rvo

Figure 6. Qualification: highest and lowest values

According to Figure 7, the Portuguese municipalities with more qualification are

located in Centro. The regions with the highest concentration of municipalities with

negative values of qualification are Norte, Alentejo, Região Autónoma da Madeira and

Região Autónoma dos Açores. The lower skills in the autonomous regions explain the

negative values of qualification in these regions, along with the absence of a collective

mode of modernized transportation system between the islands. In terms of the local

labour market, it is expected that the islands in the autonomous regions, according to

this analysis, present high rates of unemployment, low investment in human capital

(with high school dropout rates) and consequently labour force with low training.

38

Figure 7. Qualification per municipality

The fourth component is related to the average time spent on commuting of

employed or student resident population with a loading of 0.898, the proportion of

employed population outside the territorial unit (0.878) and to the proportion of

N

0.7 ; 2.70.1 ; 0.7-0.7 ; 0.1-3.7 ; -0.7

39

-5

-4

-3

-2

-1

0

1

2

3

4

5

Bar

reir

o

Moit

a

Od

ivel

as

Sei

xal

Am

ado

ra

Port

o S

anto

San

ta C

ruz

das

Flo

res

Sin

es

Vil

a do

Po

rto

Co

rvo

employed or student resident population using collective mode of transport in

commuting (0.642). The three indicators are positively related to commuting, revealing

intense daily movements, so the fourth component will be named as “Commuting”.

The municipalities that present higher commuting are Barreiro, Moita, Odivelas,

Seixal and Amadora. They all belong to the region of Lisboa, which matches the

territory of the Metropolitan Area of Lisboa. The municipalities with less commuting

are Porto Santo, Santa Cruz das Flores, Sines, Vila do Porto and Corvo. Three of these

municipalities are located in the Região Autónoma dos Açores.

Figure 8. Commuting: highest and lowest values

The Portuguese regions with high values of commuting (Figure 9) are the

coastal areas of Lisboa, Centro and Norte. In the Região Autónoma da Madeira it can be

verified that some municipalities also have high commuting. This can be explained by

the fact that this region has a labour market focused largely on the tourism sector.

40

Figure 9. Commuting per municipality

The regions with the highest concentration of municipalities with negative

values for commuting are Região Autónoma dos Açores, Alentejo and Algarve. In this

sense, the commuting in Portugal can be seen, in an overall pattern, as a contrast

between the metropolitan areas and the Coimbra area (in Centro), that present higher

N

0.4 ; 3.7-0.1 ; 0.4-0.6 ; -0.1-4.2 ; -0.6

41

-3

-2

-1

0

1

2

3

4

São

Vic

ente

Idan

ha-

a-N

ova

Gav

ião

Lag

oa

(Aço

res)

Alj

ust

rel

Man

teig

as

Cas

telo

de

Vid

e

Port

o M

on

iz

Ou

riq

ue

Vil

a do

Bis

po

commuting (the use of collective mode of transport is very common once these areas

provide a comprehensive system of transport and form an integrated network of

municipalities), and the inner region, that presents a low value of commuting. In terms

of the labour market, according to section 2, the regions with lower mobility normally

have higher unemployment rates, which can be apply to the Região Autónoma dos

Açores.

Component 5 is correlated with the school leavers rate, with a loading of 0.959,

and to the proportion of resident population aged between 6 and 15 years old not

attending the educational system (0.951). Once the fifth component is constituted by

variables negatively related to the dropout of school, it will be called “School leaving”.

According to the graphical analysis presented below (Figure 10), the

municipalities with the highest school leaving are São Vicente, Idanha-a-Nova, Gavião,

Lagoa (Açores) and Aljustrel. The lowest values occur in Manteigas, Castelo de Vide,

Porto Moniz, Ourique and Vila do Bispo.

Figure 10. School leaving: highest and lowest values

42

Figure 11. School leaving per municipality

As for a regional analysis, presented in Figure 11, it can be verified the existence

of an overall pattern for the school leaving: the autonomous regions present, in general,

high values for this component, that can be explained for the existence of low incomes,

N

0.5 ; 3.7-0.1 ; 0.5-0.6 ; -0.1-2.1 ; -0.6

43

-3

-2

-1

0

1

2

3

4

5

Alj

ezu

r

Lag

os

Lou

lé

Alb

ufe

ira

Cas

cais

Fel

gu

eira

s

Lou

sad

a

Co

nst

ânci

a

São

Jo

ão d

a M

adei

ra

Pam

pil

ho

sa d

a S

erra

which leads to the early entrance in the labour market (and, as a consequence, to the

abandon of the educational system). The regions of the mainland coast present the

lowest values of school leaving.

Finally, the sixth component relates only to the proportion of resident population

of foreign nationality, with a loading of 0.775. The name chosen for this component is

“Foreign population”.

Analyzing Figure 12, we can conclude that four out of the five Portuguese

municipalities with greater foreign population (Aljezur, Lagos, Loulé, and Albufeira)

are located in the Algarve region. As for municipalities that have less foreign

population, we have Felgueiras, Lousada, Constância, São João da Madeira and

Pampilhosa da Serra.

Figure 12. Foreign population: highest and lowest values

44

Figure 13. Foreign population per municipality

The regions with more foreign population, according to Figure 13, are Algarve,

Lisboa and Alentejo. As mentioned in Section 3 and Figures 12 and 13, the foreign

population represents a significant proportion of residents in the Algarve region, which

can be explained by the weather conditions that make this region a tourism pole very

N

0.4 ; 4.7-0.1 ; -0.4-0.6 ; -0.1-2.6 ; -0.6

45

attractive for foreigners. Also Lisboa has a high value of foreign population, which can

be explained by the fact that it is the country’s capital, where the economic activity and

the services are more concentrated, which contributes to the attraction of the foreign

population, in search of an opportunity in a large labour market.

The regions that present lower expression of foreign population are Centro,

Norte, Região Autónoma dos Açores and Região Autónoma da Madeira. Once the

autonomous regions show low attractiveness (Figure 5), one expects a small presence of

foreign population in these regions.

46

Table 7. Component score coefficient matrix

Label Name

Component

1 2 3 4 5 6

Demographic

sustainability Attractiveness Qualification Commuting

School

learning

Foreign

population

Ind01


commuting of employed or student resident

population

-0.056 -0.053 0.013 0.415 0.032 0.105

Ind04 Ageing ratio -0.214 0.059 -0.006 -0.006 0.048 -0.111

Ind05 Social diversity ratio -0.121 -0.044 -0.008 -0.045 -0.001 0.338

Ind06 Renewal index of the

population in active age 0.252 -0.048 -0.045 -0.068 0.028 -0.091

Ind07 Potential sustainability

ratio 0.242 -0.037 -0.003 -0.007 0.037 -0.048

Ind09


population outside the

territorial unit

0.000 -0.100 0.122 0.428 0.049 -0.022

Ind11

Proportion of non-resident population employed in

the territorial unit

-0.061 0.252 -0.024 0.087 0.022 -0.279

Ind12


population with higher education completed

-0.016 0.165 0.008 -0.015 -0.002 0.158

Ind13


population aged between 18 and 24 years old with

the lower secondary

education 3rd cycle completed not attending

educational system

0.178 -0.033 -0.182 -0.168 0.037 -0.114

Ind15


population aged between 6 and 15 years old not

attending the educational

system

-0.003 -0.003 0.070 0.041 0.511 0.012

Ind17


population of foreign

nationality

-0.023 -0.029 -0.060 -0.007 -0.025 0.611

Ind19

Proportion of employed or student resident

population using

collective mode of transport in commuting

0.002 -0.013 -0.150 0.256 -0.021 -0.092

Ind20


or student resident population using

individual mode of


0.037 -0.084 0.385 0.055 0.073 -0.043

Ind21


population that comes in

the territorial unit

(commuting)

-0.057 0.354 -0.110 -0.129 -0.033 -0.275

Ind24 Proportion of car usage on

daily journeys 0.032 -0.082 0.391 0.061 0.092 -0.046

Ind25 School leavers rate 0.002 -0.002 0.085 0.051 0.522 -0.040

Ind26 Activity rate of resident population

0.160 0.011 0.037 -0.018 -0.027 0.159

Ind27 Illiteracy rate -0.145 -0.020 -0.088 -0.013 0.022 -0.039

Ind28 Unemployment rate 0.047 -0.066 -0.141 0.080 0.038 0.256

Ind29 Per capita purchasing

power -0.020 0.235 -0.042 -0.086 -0.005 0.099

Ind30 Average monthly earning -0.019 0.220 -0.069 -0.081 0.023 0.069

47

The component score coefficient matrix, presented in Table 7, shows the

weighting of variables for each component extracted. The scores for each component

are obtained by summing the product between the values for each standardized variable

and the score coefficients of the components.

In the following section, we proceed to the cluster analysis, in order to

complement the work developed with factor analysis.

4.3 Cluster analysis

Cluster analysis consists in grouping individuals (for example, territorial units)

in homogeneous classes for a more concise study about profiles that are common to the

individuals in a given class, but that separate these from other individuals.

The method of cluster analysis used was the K-means, consisting of a non-

hierarchical clustering technique that forces the user to set the number of clusters (K).

This method allows the reallocation of an individual to a different cluster from the one

in which it was initially included, reducing the probability of wrong assignments.

Furthermore, it has the advantage of being easier to apply to arrays of very extensive

data.

We considered, as the starting point, the six main components extracted from the

factor analysis, which we named: demographic sustainability, attractiveness,

qualification, commuting, school leaving and foreign population. We chose the result

associated with six clusters (K=6), considering that this assumption ensures a balance

between the analytical capacity and the differentiation of the municipal profiles.

48

Table 8. Number of cases in each cluster

Table 8 presents the number of municipalities in each cluster. According to the

results presented in this table and in the cluster membership table presented below, we

conclude that there is substantial heterogeneity among the six clusters in terms of size,

with the dimensional range varying between 6 and 127 cases per cluster.

According to the analysis of the cluster membership table (Appendix) and the

map shown in Figure 14, we can conclude that the municipalities that belong to cluster

1 are located, mainly, in the Norte region, particularly in the Tâmega region, and in the

Região Autónoma dos Açores. As for the municipalities that comprise the cluster 2,

they are located in the inner of the Norte region, and in the regions of Centro and

Alentejo. The six municipalities that form the cluster number 3 are metropolitan centers,

located in the Norte and in the regions of Lisboa, Centro and Alentejo. The fourth

cluster, by its size and heterogeneity of location, does not obey to a general criterion

and corresponds mode to the cases. Cluster 5 is mainly composed by municipalities of

the Algarve region and cluster 6 of municipalities of the metropolitan areas.

1 31

2 58

3 6

4 127

5 36

6 50

Valid 308

Missing 0

Cluster

49

Figure 14. Cluster membership per municipality

N

Clusters123456

50

Table 9. ANOVA

The ANOVA table seeks to determine which of the variables allow for the

separation of the clusters. Thus, variables with higher cluster mean square and smaller

error mean square contribute more to the definition of clusters. Once the value of F is

given by the ratio between the cluster mean square and the error mean square, the larger

the value of F, the greater the contribution of the respective variable to set the clusters.

According to the results presented in table 9, it can be concluded that demographic

sustainability has the greatest influence in forming the clusters and the qualification has

the smaller.

Table 10. Final clusters centres

Cluster

Self-centred

territories,

unskilled

and with

demographic

potential

Territories

without

demographic

and school

potential

Attractive

and low

skilled

territories

Moderately

skilled

territories

Territories

with foreign

integration

Integrated

territories

with

demographic

potential and

low-skilled

Demographic

sustainability 1.601 -1.0569 -0.405 -0.167 0.106 0.630

Attractiveness -0.330 -0.221 4.855 -0.043 -0.085 0.049

Qualification -0.586 -0.328 -1.066 0.591 0.122 -0.717

Commuting -1.136 -0.287 -0.433 -0.073 -0.092 1.343

School

leaving 0.222 1.014 -0.240 -0.573 0.333 -0.070

Foreign

population -0.438 -0.307 -1.087 -0.183 1.802 -0.075

The final clusters centres table (Table 10) presents the mean for each variable

within each cluster, thereby enabling to describe the profile of each cluster.

Cluster Error F Sig.

Mean Square df Mean Square df

Demographic

sustainability 33.808 5 0.457 302 74.008 0.000

Attractiveness 29.648 5 0.526 302 56.396 0.000

Car usage 18.839 5 0.705 302 26.735 0.000

Commuting 27.412 5 0.563 302 48.713 0.000

School leaving 21.487 5 0.661 302 32.517 0.000

Foreign

population 27.987 5 0.553 302 50.591 0.000

51

According to the analysis of the final clusters centres table, we can notice that

the 31 municipalities that belong to the cluster 1 show strong demographic

sustainability, weak qualification and inexpressive commuting. This cluster is formed,

mainly, by municipalities that belongs to Tâmega and to the Região Autónoma dos

Açores (almost entirely), which, as it was found in the factor analysis, is endowed with

a high degree of demographic sustainability but quite significant dropout rates, which

causes a very low population qualification. In this sense, this cluster will be named as

“Self-centred territories, unskilled and with demographic potential”.

The 58 municipalities which are included in cluster 2 exhibit strong school

leaving and a reduced demographic sustainability, so it will be named as “Territories

without demographic and school potential”. This cluster mainly includes municipalities

that belong to the inner region of Norte, Centro and Alentejo. As determined previously,

there is a growing desertification of the inner municipalities, which in turn causes a

reduced demographic sustainability.

The 6 municipalities included in cluster 3 have great attractiveness but low

qualification and foreign population. In this sense, it will be named as “Attractive and

low skilled territories”. The metropolitan centres are included in this cluster. According

to the factor analysis and the conclusions presented in Section 3, the metropolitan areas

of Lisboa and Porto are endowed with great attractiveness for the economic and the

population concentration that they encompass.

The fourth cluster, which aggregates the largest number of municipalities (127),

shows some degree of qualification and weak school leaving, so it can be called as

“Moderately skilled territories”. This cluster, as showed in Figure 14 and Appendix,

includes, mainly, municipalities located in Centro and Alentejo.

As for cluster 5, the 36 municipalities have, as most obvious feature, a strong

foreign population, so it will be named as “Territories with foreign integration”. As it

can be seen in Appendix 1, all the 16 municipalities of Algarve, except Alcoutim,

belong to cluster 5. As seen in Section 3, Algarve is a tourism region highly appreciated

by foreigners, who later choose to come and live there. In this sense, Algarve has a

significant rate of foreign residents mainly due to climatic factors.

52

For the 50 municipalities that make up the cluster 6 there is strong commuting,

some demographic sustainability and a weak qualification, so this cluster will be named

as “Integrated territories with demographic potential and low skilled”. These

municipalities are located, primarily, in metropolitan areas, which explains the high

commuting, as seen in Section 3.

53

5 Conclusions and future developments

The workforce is a productive factor (both in quantity and quality) offered by

individuals in exchange for compensation, usually monetary. In qualitative terms, the

supply of skilled labour is only possible if there is an investment in education and

training, seen as mechanisms of human capital enhancement.

The spatial division of the labour market and the geographical, cultural and

dimension specificities are a reality that must be considered when designing and

implementing economic and social policies, in order to ensure that they reach the same

it proposes to achieve. Aspects such as mobility that occurs between local markets, the

relationship that exists between output and unemployment and the skills mismatch must

be taken into account in the design of programs to reduce the long-term unemployment

and other problems inherent to local labour markets.

The factor analysis allowed us to draw some conclusions about Portugal with

regard to aspects such as demographic sustainability, attractiveness, qualification,

commuting, school leaving and foreign population. The regions with the highest

population sustainability are the Região Autónoma dos Açores and the Norte region,

while the regions with less sustainability are Centro and Alentejo. The most attractive

municipalities are located on the coast, specifically in the metropolitan areas of Lisboa

and Porto. The inner and the autonomous regions have negative attractiveness. Centro is

the region with higher qualification and the Norte, Alentejo and the autonomous regions

are poorly qualified. In the coastal areas of Lisboa and in the Centro, Norte and Região

Autónoma da Madeira the commuting is high, and in the Região Autónoma dos Açores,

Alentejo and Algarve it is reduced. We are witnessing a standard pattern in the country

in relation to commuting, verifying that the metropolitan areas, along with the area of

Coimbra, in the Centro region, have higher values for this factor, while the inner

regions have poor commuting. The autonomous regions have high school leaving rates

while in the mainland coast regions there are low values for this component. Finally, the

presence of foreign population in the country is stronger in the Algarve and Lisboa and

weak in the autonomous regions, in Centro and in Norte.

54

According to the cluster analysis, Portugal can be divided into six distinct

clusters, according to the figures presented for each factor, in the factor analysis. Thus,

it is possible to conclude that the Tâmega and Região Autónoma dos Açores

municipalities are self-centred, with demographic potential but with low qualification.

The municipalities of the inner Norte, Centro and Alentejo are territories without

demographic and educational potential. The metropolitan areas, while low skilled, are

very attractive territories. The fourth cluster, which reflects the mode of the cluster

analysis, includes municipalities of Centro and Alentejo, which are moderately skilled.

The Algarve region has a strong integration of foreign population. Finally, the

municipalities in the metropolitan centres of the country are classified as being

integrated territories with demographic potential but with few qualifications.

The results obtained in the factor and in the cluster analysis reinforce some

aspects that characterize Portugal, which were referred in Section 3, including the

centralization of the economic activity on the coast, especially in the metropolitan areas

of Lisboa and Porto, the ageing population along with its concentration on the coast and

consequent desertification of the inner regions and the increasing flexibility in the

labour market.

To complement the spatial dimension with the time perspective, in order to

assess a potential change in territorial disparities, it would be interesting to draw a

comparison between statistical data of censuses of 2001 and 2011 for a set of indicators

related to the labour market.

In order to have a deeper spatial analysis, it would be relevant to look at

different levels of NUTS (for example level 2) or to lower scales, focusing the study at

the level of parishes, since the relevant spatial unit may vary with the specific issue.

According to the results obtained from the factor and clusters analysis, it could

be interesting to explore the side of the social reform, in what concerns to the

development of public policies to combat the school leaving and the ageing population

in the regions where these indicators reveal high values.

It could also be useful to deepen this analysis in a firm’s perspective, in the

human resources department, with the implementation of policies at the level of

55

attractiveness and incentive compensation, in order to combat the desertification of the

inner regions of the country.

56

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Appendix. Cluster membership table

Self-centred territories,

unskilled and with

demographic potential

Territories without

demographic and

school potential

Attractive

and low

skilled

territories

Moderately

skilled

territories

Territories

with foreign

integration

Integrated territories

with demographic

potential and low-

skilled

Angra do Heroísmo Alcácer do Sal Constância Abrantes Albufeira Alandroal

Barcelos Alcoutim Lisboa Águeda Aljezur Alcochete

Barrancos Alfândega da Fé Oeiras Aguiar da

Beira Almeirim Alenquer

Calheta (Açores) Alijó Porto Albergaria-a-Velha

Alpiarça Almada

Campo Maior Aljustrel São João da

Madeira Alcanena Benavente Amadora

Corvo Almeida Sines Alcobaça Bombarral Amarante

Fafe Alter do Chão Almodôvar Caldas da

Rainha Amares

Felgueiras Alvito Alvaiázere Cascais Azambuja

Funchal Ansião Anadia Castro Marim Baião

Guimarães Arcos de Valdevez Arganil Chaves Barreiro

Horta Avis Armamar Cuba Cabeceiras de Basto

Lagoa (Açores) Beja Arouca Elvas Câmara de Lobos

Lajes das Flores Belmonte Arraiolos Faro Castelo de Paiva

Lousada Borba Arronches Lagoa

(Algarve) Celorico de Basto

Mourão Boticas Arruda dos

Vinhos Lagos Cinfães

Nordeste Calheta (Madeira) Aveiro Loulé Entroncamento

Paços de Ferreira Carrazeda de Ansiães Batalha Lourinhã Espinho

Paredes Castanheira de Pêra Braga Mafra Gondomar

Ponta Delgada Castro Daire Bragança Monchique Loures

Porto Santo Castro Verde Cadaval Montijo Machico

Povoação Chamusca Caminha Moura Maia

Ribeira Grande Coruche Cantanhede Odemira Marco de Canaveses

Santa Cruz da Graciosa Crato Carregal do Sal

Olhão Matosinhos

Santa Cruz das Flores Ferreira do Alentejo Cartaxo Peniche Mesão Frio

São João da Pesqueira Figueira de Castelo Rodrigo Castelo Branco Ponte de Sor Miranda do Corvo

São Roque do Pico Fornos de Algodres Castelo de

Vide Portimão Moita

Velas Freixo de Espada à Cinta

Celorico da Beira

Salvaterra de Magos

Mondim de Basto

Vila do Porto Gavião Coimbra São Brás de

Alportel Odivelas

Vila Franca do Campo Góis Condeixa-a-Nova

Serpa Palmela

Vila Praia da Vitória Grândola Covilhã Setúbal Penafiel

Vizela Idanha-a-Nova Esposende Silves Ponta do Sol

Mértola Estarreja Tavira Ponte de Lima

Miranda do Douro Estremoz Torres Vedras Portel

Mirandela Évora Vidigueira Póvoa de Lanhoso

63


unskilled and with


Territories without

demographic and

school potential

Attractive

and low

skilled

territories

Moderately

skilled

territories

Territories

with foreign

integration


with demographic

potential and low-

skilled

Mogadouro Ferreira do

Zêzere Vila do Bispo Resende

Monforte Figueira da Foz

Vila Real de Santo António

Ribeira Brava

Montalegre Figueiró dos

Vinhos Santa Cruz

Mora Fronteira Seixal

Murça Fundão Sesimbra

Nisa Golegã Sintra

Oleiros Gouveia Tabuaço

Pampilhosa da Serra Guarda Tarouca

Penamacor Ílhavo Terras de Bouro

Pinhel Lajes do Pico Trofa

Ribeira de Pena Lamego Valongo

Sabugal Leiria Vieira do Minho

São Vicente Lousã Vila do Conde

Sardoal Mação Vila Franca de Xira

Torre de Moncorvo Macedo de

Cavaleiros Vila Nova de Gaia

Trancoso Madalena Vila Verde

Valença Mangualde

Valpaços Manteigas

Vila Flor Marinha

Grande Vila Nova de Foz Côa Marvão

Vila Pouca de Aguiar Mealhada

Vila Velha de Ródão Meda

Vimioso Melgaço

Vinhais Mira

Moimenta da

Beira

Monção

Montemor-o-

Novo

Montemor-o-Velho

Mortágua

Murtosa

Nazaré

Nelas

Óbidos

Oliveira de Azeméis

Oliveira de

Frades

64


unskilled and with


Territories without

demographic and

school potential

Attractive

and low

skilled

territories

Moderately

skilled

territories

Territories

with foreign

integration


with demographic

potential and low-

skilled

Oliveira do

Bairro

Oliveira do Hospital

Ourém

Ourique

Ovar

Paredes de

Coura

Pedrógão

Grande

Penacova

Penalva do Castelo

Penedono

Penela

Peso da Régua

Pombal

Ponte da Barca

Portalegre

Porto de Mós

Porto Moniz

Póvoa de

Varzim

Proença-a-

Nova

Redondo

Reguengos de

Monsaraz

Rio Maior

Sabrosa

Santa Comba

Dão

Santa Maria da

Feira

Santa Marta de

Penaguião

Santana

Santarém

Santiago do Cacém

Santo Tirso

São Pedro do Sul

Sátão

Seia

Sernancelhe

Sertã

65


unskilled and with


Territories without

demographic and

school potential

Attractive

and low

skilled

territories

Moderately

skilled

territories

Territories

with foreign

integration


with demographic

potential and low-

skilled

Sever do

Vouga

Sobral de Monte Agraço

Soure

Sousel

Tábua

Tomar

Tondela

Torres Novas

Vagos

Vale de

Cambra

Vendas Novas

Viana do Alentejo

Viana do

Castelo

Vila de Rei

Vila Nova da Barquinha

Vila Nova de

Cerveira

Vila Nova de

Famalicão

Vila Nova de Paiva

Vila Nova de

Poiares

Vila Real

Vila Viçosa

Viseu

Vouzela

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