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Beschreibung:
The urban labour market is one of fundamental significance due to the possibilities and constraints that imposes on population´s wellbeing, and because its effects on national and local employment rates and wages. The urban dimension of the labour market is closely linked to the spatial proximity between residence and workplace, and therefore it depends on the urban structure. The localization of labour demand and supply and its proximity has been the source of multiple research. During the sixties the Spatial Mismatch Hypothesis (SMH), referring to the existence of a spatial gap between labour demand and supply within the cities, was formulated. The SMH means that workers and firms have different locations and, depending on the extent of the separation, it can have negative effects on the efficiency of the labour market and the city. Particularly, the mismatch increases transportation costs, reduces income, and reveals an unequal access to employment within cities. The hypothesis has been revived lately due to the interest on the rising tendency to polycentricism in big cities. Polycentricism has been considered as a path for the integration of large cities, which acts in favour of areas that are more distant from the Central Business District (CBD), improving accessibility to jobs. This paper deals with the SMH due to the importance of measuring and understanding location patterns of workers and jobs, and their interactions. We centre our attention on the location, concentration and the spatial separation of labour demand and supply as a first step to approach the spatial mismatch problem in urban labour markets. First, we aim at identifying the spatial patterns of workers and jobs; we measure by means of alternative techniques the spatial separation; finally, we investigate the differences between submarkets which are determined by the workers' level of education and the corresponding jobs according to their knowledge and technology level required. The manuscript will present the results for the biggest ...