Digital technologies, labor market flows and training: Evidence from Italian employer-employee data

New technologies can shape the production process by affecting the way in which inputs are embedded in the organization, their quality, and their use. Using an original employer-employee dataset that merges firm-level data on digital technology adoption and other characteristics of production with employee-level data on worker entry and exit rates from the administrative archive of the Italian Ministry of Labor, this paper explores the effects of new digital technologies on labor flows in the Italian economy. Using a Difference-in-Difference approach, we show that digital technologies lead to an increase in the firm-level hiring rate – particularly for young workers - and reduce the firm-level separation rate. We also find that digital technologies are positively associated with workplace training, proxied by the share of trained employees and the amount of training costs per employee. Furthermore, we explore the heterogeneity of effects related to different technologies (robots, cybersecurity and IoT). Our results are confirmed through several robustness checks. • Effects of new digital technologies on labor flows in the Italian economy. • Digital technologies positively affect hiring rate of young workers. • Firm-level adoption of digital technologies reduce firm-level separation rate. • Digital technologies positively associated with workplace training. • Heterogeneity of effects of robots, cybersecurity and IoT on job flows.

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Publication Details

Journal
Technological Forecasting and Social Change
Published
2024-10-19
DOI
https://doi.org/10.1016/j.techfore.2024.123735
Citations
16
Primary Topic
Labor market dynamics and wage inequality
Type
article
Field-Weighted Citation Impact
18.07
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article

Digital technologies, labor market flows and training: Evidence from Italian employer-employee data

Valeria Cirillo, Andrea Ricci, Andrea Mina
16 citations
Technological Forecasting and Social Change
Labor market dynamics and wage inequality
18.07
article

Digital technologies, labor market flows and training: Evidence from Italian employer-employee data

Valeria Cirillo, Andrea Ricci, Andrea Mina
article en
16 citations

Abstract

New technologies can shape the production process by affecting the way in which inputs are embedded in the organization, their quality, and their use. Using an original employer-employee dataset that merges firm-level data on digital technology adoption and other characteristics of production with employee-level data on worker entry and exit rates from the administrative archive of the Italian Ministry of Labor, this paper explores the effects of new digital technologies on labor flows in the Italian economy. Using a Difference-in-Difference approach, we show that digital technologies lead to an increase in the firm-level hiring rate – particularly for young workers - and reduce the firm-level separation rate. We also find that digital technologies are positively associated with workplace training, proxied by the share of trained employees and the amount of training costs per employee. Furthermore, we explore the heterogeneity of effects related to different technologies (robots, cybersecurity and IoT). Our results are confirmed through several robustness checks. • Effects of new digital technologies on labor flows in the Italian economy. • Digital technologies positively affect hiring rate of young workers. • Firm-level adoption of digital technologies reduce firm-level separation rate. • Digital technologies positively associated with workplace training. • Heterogeneity of effects of robots, cybersecurity and IoT on job flows.

Technological Forecasting and Social ChangeVol. 209
Scuola Superiore Sant'Anna (IT), University of Cambridge (GB), National Institute for Public Policy (US), University of Bari Aldo Moro (IT)
Decent work and economic growth
Openalex Percentile: Top 1%
Labor market dynamics and wage inequality
18.07
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Digital technologies, labor market flows and training: Evidence from Italian employer-employee data — Valeria Cirillo, Andrea Ricci, et al. · Technological Forecasting and Social Change (2024) | TGRS Research Map | TGRS