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Advisor(s)
Abstract(s)
Using a cross-section database that observes the Portuguese labour
market in two different phases of the business cycle, the present paper aims to
address the issue of the segmentation of the Portuguese labour market taking into
account the heterogeneity resulting from different unemployment characteristics
observed along the Portuguese geographical space and applying two optimization
clustering methods: the k-means and the spectral methods. The k-means is a
traditional optimisation clustering method applied to cluster data observations.
Spectral clustering is an alternative method based on the computation of the
dominant eigenvectors of a matrix related with the distance among data points. The
results obtained by the two methods are not identical but are very close and show
that, apart the economic phase of the cycle, Portugal presents two very different
profiles of registered unemployment. One of them can be considered problematic
because it presents a higher percentage of unemployed women, long duration
unemployed and unemployed with low levels of formal education—these are the
groups that present more difficulties in the labour market and for which is more
difficult to find a job after losing one. The segmentation of the labour market is a
reality and the labour market is not adjusting to the business cycle.
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Citation
Balsa, Carlos; Nunes, Alcina; Barros, Elisa (2015). Clustering techniques applied on cross-sectional unemployment data. In Bourguignon, Jean-Pierre [et al.] (Eds.) Dynamics, Games and Science. springer. 1, p. 71-88. ISBN 978-3-319-16117-4