From sectoral industrial composition to employment and reverse. The Italian Case

Massimo Giannini (), Barbara Martini and Cristiana Fiorelli
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Massimo Giannini: University of Rome Tor Vergata, Dep. of Enterprise Engineering, Italy
Barbara Martini: University of Rome Tor Vergata, Dep. of Enterprise Engineering, Italy
Cristiana Fiorelli: Sapienza University of Rome, Dep. of Economics and Law, Italy

Romanian Journal of Regional Science, 2024, vol. 18, issue 1, 1-37

Abstract: The diffusion of knowledge among firms and workers, which, in turn, depends on the nature of the knowledge itself and the relatedness or unrelatedness of the industrial composition, is an essential driver for the growth and employment of territories. The proximity between related industries allows for faster diffusion of specific knowledge, as happens in the Marshall-Arrow-Romer (MAR) theory of externalities (agglomeration) and Jacobs’s theory of knowledge transmission. The literature (notably the Evolutionary Economic Geography one, EEG) has empirically investigated the relationship between employment (or GDP) and some measures of industrial composition (e.g., the related and unrelated variety). In this paper, we contribute to the literature in two ways. First, we propose a more coherent use of entropy indexes to measure the relatedness or unrelatedness of industries in an economy. Second, we empirically investigate the nexus between industrial composition and employment level by exploiting these measures. Unlike the existing literature, we argue that such a nexus could be circular. For such a reason, an empirical investigation using a spatial vector autoregressive (SpVAR) model will be performed for the Italian economy at the provincial level. The results show that such a circular nexus exists, shedding new light on the debate.

Keywords: Entropy; Variety; Sectoral composition; Knowledge diffusion; Spatial VAR (search for similar items in EconPapers)
JEL-codes: B52 J21 L16 O33 (search for similar items in EconPapers)
Date: 2024
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