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Ovidius University Annals Economic Sciences Series ISSN 1582 – 9383 © 2010 Ovidius University Press 2 OVIDIUS UNIVERSITY ANNALS ECONOMIC SCIENCES SERIES Volume X, Issue 1 2010 EDITORIAL BOARD EDITOR in CHIEF: Professor, PhD. Elena Cerasela Spătariu, Scientific Secretary, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Members: Professor, PhD. Jacky Mathonnat, Vice Recteur de L’Universite D’Auvergne, Clermont 1, Clermont – Ferrand, France; Professor, PhD. Grigore Belostecinic, Rector, ASEM Chişinãu, Republic of Moldova; Professor, PhD. Antonio Garcia Sanchez, Faculty of Business Studies, Univesitad Politecnica de Cartagena e Murcia, Spain; Professor, PhD. Maurice Chenevoy, Directeur de l’ Institute Universitaire Profesionalise, Universite D’Auvergne, Clermont 1, Clermont – Ferrand, France; Professor, PhD. Victor Ploae, Prorector, “Ovidius” University of Constanta, Romania; Professor PhD. Constantin Roşca, Executive Director of AFER; Professor, PhD. Viorel Cornescu, University of Bucharest; Associate Professor, PhD. Costel Nistor, Dean, Faculty of Economic Sciences, “Dunãrea de Jos” University of Galati, Romania; Professor, PhD. Tiberius Dãnuţ Epure, Dean, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Professor, PhD. Ion Botescu, Pro-dean, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Associate Professor, PhD. Simona Utureanu, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Associate Professor, PhD. Marian Ionel, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Associate Professor, PhD. Sorinel Cosma, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Associate Professor, PhD. Ramona Gruescu, Faculty of Economics and Business Administration, University of Craiova, Romania Lecturer, PhD student, Victor Jeflea, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania. iii EDITORIAL SECRETARIES: Teaching Assistant, Dorinela Cusu, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Lecturer, PhD. Cristina Duhnea, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Teaching Assistant, PhD. Student Gabriela Gheorghiu, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Lecturer, PhD. Cătălin Ploae, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania; Lecturer, PhD. Margareta Udrescu, Faculty of Economic Sciences, “Ovidius” University of Constanta, Romania. iv Ovidius University Anals, Economic Sciences Series Volume X, 2010 Portuguese Regional Unemployment Patterns: A k-Means Cluster Analysis Approach NUNES Alcina BARROS Elisa Instituto Politécnico de Bragança [email protected] [email protected] Abstract regions. That knowledge is of crucial The k-means cluster analysis technique is an importance to develop public labour market important ally in the study of economic policies specifically targeted to the regional patterns in a multivariate framework. Aware of unemployment profiles. its analytical importance this paper adopts This type of analysis begins to be developed such method of study to identify groups of in some economies since it is believed to be of Portuguese administrative regions that share core importance for understanding the similar patterns regarding the characteristics phenomenon of unemployment at the of unemployed registered individuals. The aggregate level. Moreover, according to regional distribution of the unemployed authors like Marelli [4] economies that observe individual characteristics is of core substantial changes in its GDP (a fast growth importance for the development of public of the GDP levels, for example) do it at the policies directed to fight the unemployment expense of some regions over others. The phenomenon, especially in times of crisis. consequence is the promotion of the observed Preliminary results show a clear division of regional disparities that could be observed, the territory into four regions – north and namely, throughout different profiles of south and urban and rural areas - that stresses regional unemployment. The so-called the importance of designing well-directed European Union enlargement countries had public labour policies. experienced this phenomenon which led to the production of various studies on the Keywords: regional unemployment, labour phenomenon of regional unemployment [1, 2 market, regional dissimilarities. and 5]. In this research work, it will be aimed to JEL Classification: J64 bound regions of the country - using the district as the territorial unit - according to a 1. Introduction specific profile of registered unemployment. So, it will be applied a classification analysis A study of the regional dissimilarities in a where the territorial units are grouped into labour market can not be limited by a simple classes, according to their similarities observed descriptive analysis of the associated through the set of explanatory variables phenomena. It should establish standards for presented. The aim is to detect the presence of spatial comparison of the territories subject to homogeneity among different districts based analysis in order to develop public policies of on a multivariate statistical method – the k- both central and regional scope. These policies means clusters analysis methodology – for the should be the most appropriate to fight the year 2009. To reach the mentioned goal it will associated unemployment problems. The be taken into account a set of variables made generation of employment public policies, used public by the institution that administrates the to fight the persistent phenomenon of registers of unemployed individuals in the unemployment, have deserved a particular Portuguese economy – the Instituto de attention in the Portuguese economy over the Emprego e Formação Profissiona (IEFP). last decades, however, little is known about the The article is presented as follows. The next profile of the registered unemployed section presents a description of the registered individuals along the national administrative unemployment rate in Portugal taking into 454 Ovidius University Anals, Economic Sciences Series Volume X, 2010 account a set of characteristics that describe 3. Brief introduction to k-means cluster the unemployed individuals registered in the analysis national employment services. In Section 3, it is explained, briefly, the methodology selected The seminal work of Tryon [7] introduced for the empirical analysis presented in Section the cluster analysis. Such methodology is 4. This section presents and describes the composed by a set of multivariate statistical results obtained there. Section 5 presents the methods that include different classification average cluster unemployment profiles and and optimization algorithms which intend to section 6 concludes. organize information concerning multiple variables and shape homogeneous groups. 2. Registered unemployment data In other words the cluster analysis develops tools and methods that, given a data matrix The unemployed individuals registered in containing multivariate measurements on a the Portuguese public employment services of large number of individuals (or objects), aim to the IEFP present a given set of distinctive build up some natural groups. The groups or characteristics that make each of them different clusters should be as homogeneous as possible from the others. Such features are related with and the differences among the various groups the gender, age, formal education, as large as possible. The cluster analysis does unemployment spell and relation to a first or not make conjectures about the number of new employment. The data concerning these groups or its structures - the groups are based characteristics are openly available in a on the similarities among the groups monthly period base. The data concerning the characterized by different ways of calculating month of December collects information about the "distance". the stock of registered unemployed individuals Being the adopted variables quantitative at the end of the respective year. variables, the application of the Euclidian Distance method is advised [3]. The distance is Table 1. Description of the registered defined as the square root of the sum of the unemployed individuals in Portugal squared differences between the values of i and j for all the selected variables Characteristics 2009 vp1,2,. .., : Male 236.791 Gender p Female 267.984 2 dij Xiv X jv <25 years 64.116 v1 25-34 years 119.441 Besides the settlement of the distance Age 35-54 years 229.054 among observations, computation method is >=55 years 92.164 still necessary to settle the computational Less than 1º CB 27.408 method to calculate the distance among 1º CB 142.665 groups. Non-hierarchical methods choose in 2º CB 96.529 advance the k number of groups which will Education 3º CB 99.976 comprise all the observations. Then all the observations could be divided by the Secondary 94.442 predefined k groups and the best partition of Superior 43.755 the n observations will be the one that Position First Employment 37.556 optimizes the chosen criteria. One of the relating labour New Employment 467.219 processes that could be applied is the k-means market interactive partition method. The method Unemployment Less than 1 year 329.358 follows the next steps: starts by dividing an duration More than 1 year 175.417 initial partition of individuals by the number of Total 504.775 clusters previously defined; computes for each cluster the respective
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