This paper investigates European location patterns during a period of economic integration, seeking to identify the distinct roles played by the different geographical levels. The evolution of localization in Europe proved much more complicated empirically than the predictions based on Krugman’s hypothesis. Using Eurostat regional data for the period 1985–2001, the paper shows that while manufacturing employment trickled down among regions, after the completion of the European Single Market a slight agglomeration occurred, but only across national boundaries. National specialization has emerged particularly in the European Union founding Member States. Moreover, there is evidence of an increasing polarization of the North–South divide closely connected with the growing concentration of high-technology sectors

This article contributes to the knowledge about the evolution of industrial districts specialized in traditional manufacturing industries as a result of internationalization processes. On the basis of a longitudinal field study, the article describes the change in firms' delocalization that occurred in one of the main fashion-led Italian footwear districts during the period 2001–2009. After the well-known preferential destination towards Central and Eastern Europe, a second, more recent wave of delocalization over long distances is reported. Despite intensified delocalization, subcontracting relationships remain deeply embedded, and the updated evidence indicates a possible homecoming to the original area.

The article investigates the spatial patterns of the COVID-19 infection in Italy and its determinants from March 9 to June 15, 2020, a time interval covering the so called ‘first wave’ of COVID pandemics in Europe. The results, based on negative binomial regressions and linear spatial models, confirm the importance of multiple factors that positively correlate with the number of recorded cases during the first wave of the pandemic. Economic forces, including urban agglomeration, industrial districts, concentration of large companies (both before and after the beginning of the ‘lockdown’) and a North-South gradient are the most significant predictors of the strength of COVID-19 infection. These effects are statistically more robust in the spatial models than in the a-spatial ones. We interpretate our results in the light of pitfalls related to data reliability, and we discuss policy implications and possible avenues for future research.