Industry 4.0 technological trajectories and traditional manufacturing regions: the role of knowledge workers

ABSTRACT

The technological trajectory of a region depends on the local repository of competences and knowledge, and their evolution over time. Using the case of a region with Marshallian features, we explore the Industry 4.0 potential for regional transformation showing how the employment ratios of synthetic and analytical knowledge workers of local firms evolve in relation to the adoption of a set of digital technologies. Results show that technological change in this type of region exhibits strong path dependence and goes hand in hand mainly with an increase in the employment ratio of synthetic knowledge workers.

KEYWORD:

• digital technologies
• Industry 4.0
• manufacturing region
• knowledge workers
• small and medium-sized enterprise

JEL:

• E24
• O33
• R11

ACKNOWLEDGEMENTS

The authors gratefully acknowledge Unioncamere Veneto, Veneto Lavoro and the Osservatorio Economico Sociale Treviso-Belluno for their collaboration, and for providing both the data and funding for data integration. The authors are very grateful to Erica Santini, the editor, and three anonymous referees for their valuable comments on the manuscript. The authors also thank the Organizing and the Scientific Committee of the 3rd Conference ‘Competitive Renaissance Through Digital Transformation’ for awarding a previous version of this research with the WIRED Prize.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Compared with regions with a larger presence of high-tech and knowledge-intensive sectors – whose regional innovation systems exhibit a more supportive formal institutional structure (Asheim, 2007) – regions characterized by an embedded regional system have usually more moderate formal innovation capacities in terms of research and development (R&D) expenditure, science and technology (S&T) investments, education and learning (see the European Union’s regional innovation scoreboard, 2019) which can be leveraged in order to meet disruptive challenges.

2. For more information on the Veneto region, see also https://ec.europa.eu/growth/tools-databases/regional-innovation-monitor/base-profile/veneto.

3. The Unioncamere Veneto is the official regional agency that has collected the information about firms’ technology adoption. It is in charge of periodic surveys for the collection of regional economic statistics and as such is used to extract random samples that mirror the regional population of manufacturing firms. Firms having fewer than 10 employees have been conventionally excluded. The questions in the survey addressing the adoption of the nine I4.0 technologies and the year of adoption have been prepared by a team of experts which is part of the regional observatory and includes several professionals and two university professors with expertise in the regional economy and in digitalization processes.

4. We excluded firms with some inconsistencies (i.e., different value added tax (VAT) numbers or group units) and that had not reported occupational data.

5. Technicians include those in synthetic knowledge-base occupations in the fields of physics, scientific and engineering (e.g., physical–chemical technicians, or informatics–statistics technicians assisting in firms’ laboratories or technical offices).

6. These ratios are calculated considering the annual stocks of the two categories of knowledge workers as a proportion of total employees, for each firm. In the Veneto Lavoro database, the category ‘total employees’ also includes (besides technicians and intellectual professionals): managers (around 0.5% of the total employment force); general clerks and office workers and general service workers (around 10%); and different types of blue-collar workers (specialized and semi-specialized workers as well as non-qualified employees) comprising the remaining workers.

7. Data about workers with long-term employment may be slightly underestimated in this chart because in the Veneto the digitalization of occupational data started in the mid-1990s and, moreover, national law made compulsory the digital communication of hiring and other changes in the occupational information relating to each employee only from 2008.

8. As AIDA reports only on investor-owned companies, the merge implied losing 302 firms. Hence, we decided to use this smaller sample only for robustness checks. The reduction in the sample size should not be worrisome in any case as the sample composition remained very similar in terms of firm size, type of production and sectors, ensuring the maintenance of regional representativeness. The results of this are available from the authors upon request.

9. The intellectual–professional ratio is also negatively associated for lags 1 and 2 with the introduction of blockchain (which concerns mainly payment systems). However, due to the low number of observations capturing adoption of this technology, we prefer to leave these last results aside.

Additional information

Funding

This research is part of the European Union’s Horizon 2020 project ‘MAKERS – Smart Manufacturing for EU Growth and Prosperity’ [grant agreement number 691192].