Clustering techniques applied on cross-cectional unemployment data
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abstract
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 eigenvalue 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.
