**1. Introduction**

The COVID-19 pandemic has produced devastating effects, whose consequences depend on the capability of the economic structures to react to the macroeconomic shocks affecting both supply and demand (Li et al. 2021; Notteboom et al. 2021; Pham et al. 2021). The measures imposed by governments to contain the spreading of the virus are examples of strict supply-side constraints (Chen et al. 2021). As soon as the lockdown would end and the containment measures gradually be lifted (Seetharaman 2020), the pre-crisis levels of activity could be expected to recover (Sigala 2020; Maples et al. 2021; Song et al. 2021). These dynamics apply to the economic system as a whole, although the restrictive effects have had different sectoral impacts (Bashir et al. 2020). Such impacts, spread over the whole economy at a magnitude that depends on the relevance of individual sectors, depend on the inter-sectoral relation characteristics of the production system (Hirschman 1958; Dean et al. 2021; Belitski et al. 2022). A comprehensive assessment of the inter-industry relationship characteristics of the Italian economy is the base for a more exhaustive analysis of COVID-19s effect on production sectors (Ascani et al. 2021; Bragatto et al. 2021; Cutrini and Salvati 2021).

At the global level, Italy ranks high within the industrialized countries (Bigerna 2013). The most significant industries that undoubtedly shape and lead inter-industry interactions are manufacturing and, among services, those related to wholesale and retail trade, as well as transport (Archibugi et al. 1991; Cainelli and Leoncini 1999; ISTAT 2016). Manufacturing relies on specialized and high-quality products that are realized by a

**Citation:** Ciaschini, Clio, Margherita Carlucci, Francesco Maria Chelli, Giuseppe Ricciardo Lamonica, and Luca Salvati. 2022. The Industrial Pattern of Italian Regions: A Disaggregated Sectoral Analysis Based on Input–Output Tables. *Economies* 10: 300. https://doi.org/ 10.3390/economies10120300

Academic Editors: Ralf Fendel, Sajid Anwar and Robert Czudaj

Received: 3 October 2022 Accepted: 14 November 2022 Published: 28 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

network of small- and medium-size enterprises (Bertolini and Giovannetti 2006). In the last decades, globalization has further stimulated the expansion of manufacturing (Ghisellini and Ulgiati 2020). However, since 2018, this expansion wave decelerated because of marked instances influencing the stability of international markets (Cainelli et al. 2018; Basso 2020; D'Ingiullo and Evangelista 2020). The inward-looking American commercial policies, the economic outcomes of Brexit, the latent tensions between the USA and China, and the uncertain political outcomes of national elections in many European countries resulted in insecure landscapes for economic interactions. Within this uncertain climate, Italy suffered from political instability, economic stagnation, and a lack of structural reforms (Rubino and Vitolla 2018; Cainelli et al. 2019; Ciffolilli et al. 2019). In fact, even before the Great Recession (e.g., between 2001 and 2007), Italy was growing by less than 1.3% per year, on average. The 2008 crisis exacerbated the weakness of the Italian economy (Da Roit and Iannuzzi 2022). In 2009, its gross domestic product declined by 5.5%, and has been recovering slowly and only partly (Crespí-Cladera et al. 2021). Moreover, the crisis has deepened the divide between affluent and industrialized Northern regions and disadvantaged, agriculturedependent, Southern regions, which has been particularly evident for gross domestic product, unemployment, and capital formation (Dei Ottati 2018).

Stagnation affects both public investments in infrastructures and private capital formation, notwithstanding the support provided by the incentives to the 4.0 digital conversion of manufacturing (Confindustria Report 2019). However, internal weaknesses and the increasing concentration of industrial development in small districts—mainly in Northern regions—did not prevent Italy from becoming the seventh world manufacturing power by 2018, and the ninth in the world for export capacity (Table 1).


**Table 1.** Value added and Exports (2018) in percent values of world's total (source: our elaboration on Confindustria Report 2019).

In this context, the 4.0 transformation of manufacturing has played a role in improving firms' efficiency. Technology 4.0 was aimed at fastening decision processes and facilitating new forms of interaction between humans and machines to connect the entire value chain within the firms (Ghisellini and Ulgiati 2020). Italy joined the European framework of Industry 4.0 in 2016, enforcing a National Plan called 'Industria 4.0'. Hyper–amortization investments, estimated to account for 10 billion euros, have been the main measure stimulating firms' development (Da Roit and Iannuzzi 2022).

The regional framework in which economic activities are performed also has relevance, together with the dimensions of firms within the region. Italian regions consist of levels of territorial subdivision and public authorities, i.e., public bodies with legal status and wide autonomy regulated by the Constitution (Salvati and Zitti 2009). The 20 Italian administrative regions (see Figure 1) can be grouped into three macro-areas (Zambon et al. 2017). The North macro-area comprises nine regions: Liguria, Lombardy, Piedmont, Aosta Valley, Veneto, Friuli-Venezia Giulia, Emilia-Romagna, and the two autonomous provinces of Trento and Bolzano in the Trentino-Alto-Adige/Sudtirol region. The Central macro-area includes Tuscany, Latium,

Marche and Umbria. The South/Islands macro-area includes Abruzzo, Basilicata, Calabria, Campania, Molise, Apulia, Sicily, and Sardinia (Salvati et al. 2008).

**Figure 1.** A map illustrating the geography of Italian administrative regions (Source: own elaboration on Eurispes (2022)).

According to a qualified source of regional analysis in Italy, "there is no doubt that the social and economic disunity of Italy still remains nowadays the most apparent and the most ignored structural limit" (Eurispes 2022). The most relevant inconsistency of Italy is that a part of it (corresponding to 41% of the territory) lives in disadvantaged socio-economic conditions. A measure of the relative economic performance of each region can be quantified using per-capita GDP as shown in Table 2, where the 2019 percent gap of the regional per-capita GDP with respect to the country figure is given. In 1951, per-capita GDP in Southern Italy amounted to 53 per cent of country GDP. In 1973, this economic aggregate reached the lowest peak (51 per cent), and that result was never observed again.


**Table 2.** Per-capita GDP departure (%) of Italian Regions from the national average in 2019 (ISTAT 2020).

Our contribution sheds light on the role of each of the 29 industries, described in Table 3, characterizing the technological structure of the Italian economy by considering each of the 20 administrative regions separately. Defining and quantifying the role and relevance of each activity allow for a convenient comparison with national dynamics, as well as an appropriate analysis of the role of each activity within and between macro-areas (Lamonica and Chelli 2018). For this aim, on the one hand, we regionalized the Italian Input-Output 2016 Table, with a disaggregation level corresponding to the 29 representative sectors estimated at the level of Italian administrative regions, by using Flegg Location Quotient methodology (Lamonica et al. 2020).



By performing a non-survey regionalization, this approach avoided time-consuming and costly data collection. On the other hand, with the aim of revealing and quantifying the regional/sectoral role of each activity, we performed a linkage analysis determining the sector potential in the national rank. The specific roles of key production branches in the Italian regions were also determined. Linkage analysis was performed by adopting the most suitable definitions and operational frames for the aims of our study. In particular, we use the outcomes of linkage definition based on the Hypothetical Extraction Method (HEM) approach.

This paper is structured as follows. The next section briefly presents the most relevant literature on linkage analysis. Section 3 illustrates the regionalization technique and the methodology adopted to derive Hypothetical Extraction Method (HEM) coefficients. Section 4 illustrates the empirical results of this analysis. Section 5 discusses the main research findings and concludes the paper.

#### **2. Literature**
