Vittorio Carlei

Mapping industrial patterns in spatial agglomeration: A SOM approach to Italian industrial districts

The paper presents a new approach based on Self-Organizing Maps (SOM) and a new index called Relative Industrial Relevance (RIR) to discover, track and analyze spatial agglomeration of economic activities. By comparing patterns of local employment, this methodology shows how the local supply of human capital can explain the advantages generating spatial agglomerations. The reference case for this research is Italy, which has developed one of the most remarkable and studied example of spatial agglomerations, the Industrial Districts (IDs). IDs are traditionally identified by indexes which measure the physical concentration of firms belonging to a given industry, but are unable to seize the overall productive structure of the local economy. Employing the Italian Clothing Industry as test bed, the approach proposed in this paper identifies spatial agglomerations in terms of industry patterns and not of industry concentration. This methodology can offer a new basis to analyze the multiple pattern of local development.

Digital Technologies for Social Innovation: An Empirical Recognition on the New Enablers

Even though scholars’ attention has been placed on Social Innovation (SI), little evidence has been provided with regards to which tools are actually used to address social needs and foster Social Innovation initiatives. The purpose of the article is twofold. Firstly, the article offers empirical recognition to SI by investigating, on a large-scale, social and innovative activities conducted by start-ups and small and medium-sized enterprises (SMEs) across the world between 2001 and 2014. Secondly, the article intends to capture SI core businesses and underlying complementarities between products, markets, and technologies and show in which way digital media and IT are essentially tracing innovation trajectories over a multitude of industries, leading the current industrial patterns of SI, and continually fostering its cross-industry nature.

Similar Patterns of Cultural and Creative Industries. A Preliminary Analysis Based on Self-Organized-Map to the Italian Case

In recent years, there has been a widely belief that creativity, going hand in hand with innovation and knowledge creation, readily translates into regional competitiveness. In the same time, cultural and creative industries (CCI) industrial pattern have been attracting a growing interest from a wide range of academic research and policy interventions. The aim of this article is to establish a better understanding of relevant industry relevance (RIR) of geographic samples with a relevant similarity in terms of industrial patterns and not of industry concentration. In this sense, we move from a methodological approach, based on Self-Organizing Maps (SOM) by comparing patterns of local employment. The Italian case provides an interesting case study to analyze industrial patterns by offering new insights of occupational dynamics. We conclude that this paper represents a firs explorative attempt to extend the previous literature to seize the overall productive structure of the local creative economy.

What Network Analysis Can Teach Us About Chinese Economic Structure

The reforms undertaken or expected by Chinese policymakers are likely to cause significant stress on the economy. In order to understand the implications for the economic system and its capacity to resist these shocks successfully (resilience), we carried out a spatial network analysis that helped to identify intersectoral interdependencies which could affect the behavior of the Chinese economy in reaction to exogenous (policy) shocks. The analysis starts from estimating the sectoral specialization of 287 municipalities across 14 industries using occupational employment shares. Secondly, the analysis identifies sectoral interdependencies across these sectors using a network based approach.

Economic Diversity and Industry Patterns in Italian Provinces: An Empirical Analysis

This paper focuses on economic diversity in Italian provinces and tests whether it is associated to growth. First the degree of diversity of Italian provinces is measured. Data concern employment levels at provincial level over the period 1951-2001 across 47 industries. Second the correlation between diversity and growth by province and over the observed time period is estimated. Finally, after confirming the relevance of economic diversity, the industry pattern that boosted growth in the largest provinces is investigated and compared to that of the smallest ones.