What drives employment–unemployment transitions? Evidence from Italian task-based data

Why are there so many non-teleworkable occupations? Is teleworking only a matter of ICT usage or does it also reflect the division of labour and the underlying hierarchical layers inside organizations? What does it happen to those workers not able to telework in terms of socio-economic risks, and how does the gender dimension interact with risk stratification? Hereby, we intend to shed light on these questions using a detailed integrated dataset at individual and occupational level (Indagine Campionaria delle Professioni, Indagine delle Forze di Lavoro and Inail archive) which provides information on different nature of risks (income, employment and safety). Our results entail that, first, class attributes, intended as execution of tasks, degrees of autonomy in doing the job, layers of the occupational categories, strongly influence the chance of working from home; second, those individuals who are not able to perform their work remotely are more exposed to transition to unemployment, to earn low wages, and to safety and health risks; third, being woman and employed with a temporary contract significantly amplify risk stratification.

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“…For recent empirical studies applying this notion of “wide unemployment” see for instance Cassandro et al. ( 2021 ).…”

mentioning

confidence: 99%

Working from home and the explosion of enduring divides: income, employment and safety risks

2022

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“…Researchers already warned for years that repetitive tasks can be automated, making it possible to shift them from humans to machines. Depending on how high the share of repetitive tasks in an occupation is, an entire occupation or parts of it can be automated (Cassandro et al 2021). Workers in these occupations may become redundant, streaming into unemployment or early retirement.…”

Section: Our Digitalizing World Of Workmentioning

confidence: 99%

An introduction to digitalizing work in the Nordics

Komp-Leukkunen,

Poli

2024

NJWLS

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Digital technologies have become an essential part of our everyday lives. While they were still a curious novelty in the 1960s and 1970s, they seem to permeate an ever- increasing part of today’s societies (Levin & Mamlok 2021). By now, they are no longer confined to offices, where people need to physically sit in front of computers to use them. Instead, they are ubiquitous, with handheld devices being portable, and wearable technologies frequently even unobtrusive (Delabrida Silva et al. 2018). Augmented Realities blur the lines between technology and reality, while Virtual Realities even place a technological layer over our realities (Arena et al. 2022). Our technologically embedded lives create a myriad of data, which is used for various kinds of communication and as a currency. It is shared on social media for social interaction and to cultivate a personal image (Hall 2018). Moreover, it is routinely shared with companies for marketing and product development, sometimes in exchange for services (Cao et al. 2022). Thus, the datafication of our lives pulls us into a complex network of interactions (...)

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“…Technology has increased the level of threat faced by workers with repetitive tasks (Cassandro et al, 2021). Highly qualified personnel use technology more effectively.…”

Section: Technological Innovation and Employment Structurementioning

confidence: 99%

Study on the impact of industrial green development and technological innovation on employment structure

Hu²,

Zhao³

2023

Front. Earth Sci.

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Exploring the relationship between industrial green development, technological innovation, and employment structure, especially the impact between industrial green development and technological innovation on employment structure, is of enormous theoretical and practical importance to achieve high-quality employment as well as optimize the employment structure of China. Thirty Chinese provinces’ data from 2009 to 2019 is adopted to assess industrial green development levels. Considering the above, this research innovatively integrates industrial green development, technological innovation, and employment structure into an analytical framework, and empirically investigates the effects of the two factors and their interaction on employment structure by adopting a two-way fixed effects model. The specific conclusions are presented as follows. Firstly, China’s industrial green development levels exhibit a fluctuating and rising time-series evolutionary feature from 2009 to 2019 and have regional differences. Secondly, industrial green development, technological innovation, and their interaction are conducive to optimizing China’s employment structure. Thirdly, the eastern and northeastern areas’ employment structure optimization is boosted by industrial green development. However, the corresponding regression coefficients in the western and central areas are not significant. The northeastern, eastern, and western areas’ technological innovation encourages employment structure improvement. Instead, the employment structure is hampered by technological innovation in the central region. An interaction between industrial green evolution and technological innovation positively affects relevant employment structures in the four regions. Specific results of this research are of necessary theoretical significance and the realistic reference price for whether industrial green development and the interplay affect employment structure.

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What drives employment–unemployment transitions? Evidence from Italian task-based data

Cited by 9 publications

References 51 publications

Working from home and the explosion of enduring divides: income, employment and safety risks

Working from home and the explosion of enduring divides: income, employment and safety risks

An introduction to digitalizing work in the Nordics

Study on the impact of industrial green development and technological innovation on employment structure

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