Agglomeration effects in the labour market: an empirical analysis for Italy


  • Marusca De Castris Università degli Studi Roma Tre, Roma
  • Guido Pellegrini Università di Roma “La Sapienza”



Extensive and persistent geographic variability of the unemployment rate within the same region has been attributed to various causes. Some theories identify the “thickness” of markets as the source of positive externalities affecting labour market by improving the ability to match the skills requested by firms with those offered by workers. A recent paper by Gan and Zhang (2006) empirically confirms this hypothesis for the US labour markets. Agglomeration can be defined as aggregation of people, basically measured by city size, or as aggregation of firms, measured by cluster size (employment or number of plants). However, the population location and the industrial location are by far more similar in United States than in Europe and in Italy. Our paper aims to evaluate the effects of agglomeration on the local unemployment rate. The new methodological contribution of the study is the identification of both urban and industrial cluster agglomeration effects, using a wide set of control variables. Adjusting the system for the effects of sectorial and size shocks, as well as those relating to geographic structure and policy interventions, the results of our analysis differ from that for the United States. The study stresses the presence of negative and significant urbanisation externalities. We obtain, instead, positive effects concerning the geographic agglomeration of firms, and their thickness, in a specific area. Furthermore, positive and significant effects can be found in local systems with features of a district. Finally, the model distinguishes the negative effects of urban agglomerations (in terms of population density) from positive firm’s agglomerations (in terms of density of local units).


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How to Cite

De Castris, M., & Pellegrini, G. (2007). Agglomeration effects in the labour market: an empirical analysis for Italy. Statistica, 67(4), 331–350.