Giulio Cainelli , Roberto Ganau , Yuting Jiang (p)

GIULIO CAINELLI, ROBERTO GANAU AND YUTING JIANG
Department of Economic and Management Marco Fanno, University of Padova

Abstract: The Great Recession had relevant effects, not only on the macro-economic performance of the European economies, but also on the geographic distribution of production activities. This latter effect involves two different dimensions. From a spatial perspective, some industries experienced a process of spatial clustering/concentration, while others spread over space. From a time perspective, geographic concentration accelerated during some years, while reduced during others. In other words, spatial agglomeration is a continuous and complex process changing simultaneously over space and time. Since geographic agglomeration is a source of local advantages for firms, these space-time processes have also effects on the nature and intensity of local externalities. The aim of this paper is to investigate the space-time spatial agglomeration processes of a population of geo-referenced local units belonging to twelve Italian manufacturing industries, characterised by high levels of spatial agglomeration. These agglomerated sectors are selected calculating the Ellison and Glaeser (1997) index of geographic concentration for the year 2007, using the 686 Italian Local Labour Markets as spatial unit of analysis. The analysis is carried out by using data drawn from the ASIA Archive (ISTAT), which provide detailed information on the Italian population of local units operating over the 2007-2012 period. The ASIA Archive provides information at the local unit level on (i) exact address, (ii) economic sector of activity and (iii) yearly average number of employees. The availability of the information on the exact address allows us to identify the geographic coordinates of each local unit: i.e. northern and eastern. After removing local units with missing or imprecise address, the final sample used in the empirical investigation consists of about 900,000 observations. Using this information, we estimate for each industry the independent spatial K-function, the independent temporal K-function and the space-time K-function. The D function which is the difference between the space-time K function and the production of separate spatial K-function and temporal K-function, allows us to identify whether the space-time clustering is more relevant than independent spatial and temporal clustering. Our preliminary findings show that the space-time dynamics of agglomeration during the Great Recession is quite different depending on the industry under investigation.

Ben Davies , David Maré (p)

We examine the relatedness of economic functions - as captured by combinations of industry and occupation - and use these to estimate industry, occupation and city complexity. We apply the Balland et al (2017) framework for analysing smart specialisation to identify development opportunities in particular urban areas. We also evaluate the ex-post ability of the smart specialisation framework to identify subsequent growth in local economic functions.
We consider two alternative measures of relatedness, the first based on revealed local comparative advantage and the second on a metric of similarity between industry-occupation shares within cities. We examine the sensitivity of our findings to the choice of measure. Analysis is based on census data for main and secondary urban areas within New Zealand from 1986 to 2013.