The Risks of Digitalization and the Adaptation of Regional Labor Markets in Russia

Земцов, Степан; Баринова, Вера; Semenova, R. I.

doi:10.17323/2500-2597.2019.2.84.96

Cited by 58 publications

(34 citation statements)

References 15 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The emergence of new industries, new products and services will lead to increased demand for labour (Aghion & Howitt, 1994;Nelson & Phelps, 1966). Low entrepreneurial and innovative activity in Russia limits the development opportunities for new industries and new products (Zemtsov, Barinova, & Semenova, 2019;Zemtsov & Kotsemir, 2019). Vivarelli (2014), after conducting a detailed review of econometric studies, notes that process innovations, such as automation, predominantly reduce employment, however there are compensating mechanisms associated with new products, lower prices and higher incomes.…”

Section: Creation Of New Products and Servicesmentioning

confidence: 99%

New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

Земцов

2020

Region Sci Policy Practice

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Creation Of New Products and Servicesmentioning

confidence: 99%

New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

Земцов

2020

Region Sci Policy Practice

Self Cite

View full text Add to dashboard Cite

show abstract

“…Large cities with innovation centres and influential universities can offer a favourable environment for the growth of high-tech companies. In this context, the development of ICT infrastructure and entrepreneurial ecosystems play an essential role in the extension of digitalisation [37].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Digital divide, skills and perceptions on digitalisation in the European Union—Towards a smart labour market

et al. 2020

View full text Add to dashboard Cite

The new technologies, the digitalisation of processes and automation of work will change the manner of doing business, working and living. The effects of digitalisation on the economy, society and quality of life imply significant challenges of the labour market. All the participants will be concerned: authorities, companies and ordinary people. The objective of this research is to analyse the perceptions of the EU citizens about digitalisation and to highlight the differences among specific socio-demographic groups. The analysis is grounded on a composite methodology, comprising several statistical and econometric methods that provide scientific support to achieved conclusions: statistical analysis (with the primary goal to shed light on the EU citizens' perceptions about their digital technology skills), TwoStep Cluster Analysis (TSCA) (with the purpose to identify the 'digital vulnerable groups' and then the 'digital vulnerable countries' in terms of the exposure to digital divide) and logistic regression (with the main aim to quantify the impact of the relevant factors on citizens' perceptions about digitalisation). We identified a group of respondents evaluating themselves as having meagre digital skills, very afraid that robots could steal their jobs and with low usage of the internet. They are elderly, with a low level of education, manual workers or not working, with a relatively low level of income and little Internet use. The originality of our approach is given by the fact that we focused on investigating if digital divide leads to the creation of vulnerable groups (citizens and/or countries) and if there are specific patterns in terms of the perception on being skilled in the use of digital technologies in daily life or at work and of the understanding that robots replace human on the labour market. We aim to find relevant factors for the labour market to assume targeted measures that should be taken for a better match of supply and demand on the labour market and for creating a smart labour market. It is highly needed to increase the people's confidence in their skills level and to make the most of digitalisation of the societies. The results show consistent patterns in term of socio-demographic characteristics and perception towards digitalisation. The latter will have a

show abstract

“…The level of digitalization of the economy will dramatically increase. Estimates for the opportunities and risks of the digital economy were discussed earlier [30]. This is another prospective subject for research, which can clarify which regions and cities may experience more noticeable social changes in the event that the self-isolation regime is extended.…”

Section: Conclusion and Directions For Future Researchmentioning

confidence: 99%

COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions

Земцов

Бабурин

2020

Reg. Res. Russ.

View full text Add to dashboard Cite

The observed spread of coronavirus infection across Russian regions, as a first approximation, obeys the classic laws of diffusion of innovations. The article describes in detail theoretical approaches to the analysis of the spread of social diseases and discusses methodological limitations that reduce the possibility of predicting such phenomena and affect decision-making by the authorities. At the same time, we believe that for most regions, including Moscow, until May 12, 2020, the dynamics of confirmed cases are a reduced and delayed reflection of actual processes. Thus, the introduced self-isolation regime in Moscow and other agglomerations affected the decrease in the number of newly confirmed cases two weeks after its introduction. In accordance with our model, at the first stage, carriers infected abroad were concentrated in regions with large agglomerations and in coastal and border areas with a high intensity of internal and external links. Unfortunately, the infection could not be contained, and it started growing exponentially across the country. By mid-April 2020, cases of the disease were observed in all Russian regions; however, the remotest regions least connected with other parts of Russia and other countries had only isolated cases. By mid-May, at least in Moscow, the number of new cases began to decline, which created the prerequisites for reducing restrictions on the movement of residents. However, the decrease in the number of new cases after passing the peak of the epidemic in May is slower than the increase at the beginning. These facts contradict the diffusion model; thus, the model is not applicable for epidemiological forecasts based on empirical data. Using econometric methods, it is shown that for different periods of diffusion, various characteristics of the regions affect the spread of the disease. Among these features we note the high population density in cities, proximity to the largest metropolitan areas, higher proportion of the most active and frequently traveling part of the population (innovators, migrants), and intensive ties within the community, as well as with other regions and countries. The virus has spread faster in regions where the population has a higher susceptibility to diseases, which confirms the importance of the region's health capital. The initial stage was dominated by random factors. We conclude this paper with directions for further research.

show abstract

The Risks of Digitalization and the Adaptation of Regional Labor Markets in Russia

Cited by 58 publications

References 15 publications

New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

New technologies, potential unemployment and ‘nescience economy’ during and after the 2020 economic crisis

Digital divide, skills and perceptions on digitalisation in the European Union—Towards a smart labour market

COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions

Contact Info

Product

Resources

About