The impact of digitalization on the demand for labor in the context of working specialties: Spatial analysis

Julia Dubrovskaya; Elena Kosonogova

doi:10.21638/spbu05.2021.302

Authors

Julia Dubrovskaya Perm National Research Polytechnic University https://orcid.org/0000-0002-3205-9264
Elena Kosonogova Perm National Research Polytechnic University https://orcid.org/0000-0001-9573-7336

DOI:

https://doi.org/10.21638/spbu05.2021.302

Abstract

The spread of digital technologies, as well as the expansion of remote work practices, have a
direct impact on the transformation of the labor market. At the same time, there is no wellestablished scholarly consensus about the nature and consequences of this influence. The key premise of the article is that there are relatively few empirical studies on the labor market that account for the influence of the location of regions relative to each other. This article tests a hypothesis about the significance of location and neighborhood of territories on labor demand in the context of economic digitalization, based on the calculation of coefficients of demand localization for specialists of different profiles and methods of spatial econometrics. The assessment is based on the evaluation of the registered unemployment rate heterogeneity for municipalities in the Perm region of the Russian Federation, using the global and local
Moran’s indexes. The studies revealed a positive spatial autocorrelation among neighboring municipalities that proved the high spatial heterogeneity of the registered unemployment rate. Centers of localization and development of labor resources (“growth poles”) and territories affected by spillover effects were also identified. This demonstrates that spatial inter-territorial relations have a significant impact on the demand for labor, which makes it necessary to account for spatial effects when modeling dynamics of employment indicators. The method of labor market analysis based on the assessment of spatial heterogeneity of the unemployment rate and the calculation of localization coefficients is universal and can be applied to various taxonomic units.

Keywords:

demand for labor, registered unemployment rate, Moran index, localization rates, digital economy, spatial heterogeneity

Downloads

Download data is not yet available.

References

References

Annunziata M., Biller S. (2014) The Future of Work. General Electric Discussion Paper. URL: http://files.gecompany.com/gereports/image/FutureOfWork_8am.pdf (accessed: 02.06.2020).

Anselin L. (2006) Spatial Econometrics in Mills T. C., Patterson K. (eds) Palgrave Handbook of Econometrics. Vol. 1: Econometric Theory. Basingstoke, Palgrave Macmillan, pp. 901–969.

Barzotto M., Propris L. D. (2018) Skill up: smart work, occupational mix and regional productivity. Journal of Economic Geography, iss. 19, pp. 1049 1075.

Belyaev G. Yu. (2017) Potentially negative trends in the development of modern education and their impact on education as a social phenomenon. Science today: problems and development prospects, iss. 11,pp. 52–57. (In Russian)

Bodrunov S. D., Demidenko D. S., Plotnikov V. A. (2018) Reindustrialization and the establishment of the “digital economy”: harmonization of trends through the process of innovative development. Management Consulting, iss. 2, pp. 110–122. (In Russian)

Brynjolfsson E., McAfee A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, Norton. 281 p.

Calvino F., Criscuolo C. (2019) Business dynamics and digitalization. OECD science, technology and industry policy papers, iss. 62. 59 p. URL: https://www.oecd-ilibrary.org/docserver/6e0b011a-en.pdf expires=1611436553&id=id&accname=guest&checksum=FFEE3AA5E3AD53259BA4AA49D0669D3C (accessed:10.01.2021).

Demidova O. A. (2014) Spatial-autoregressive model for two groups of interconnected regions (on the example of the eastern and western parts of Russia). Applied Econometrics, vol. 2, iss. 34, pp. 19–35. (In Russian)

Diodato D., Weterings A. B. (2015) The resilience of regional labour markets to economic shocks: exploring the role of interactions among firms and workers. Journal of Economic Geography, iss. 15, pp. 723–742.

Domnich E. L. (2015) A trade-off between employment and labor productivity in Russia and OECD countries. National interests: priorities and security, iss. 44, pp. 38–52. (In Russian)

Elhorst J. P. (2009) “Spatial Panel Data Models” in Fischer M. M., Getis A. (eds) Handbook of Applied Spatial Analysis. Berlin, Springer, pp. 377–407.

Evangelista R., Guerrieri P., Meliciani V. (2014) The economic impact of digital technologies in Europe. Economics of Innovation and New Technology, vol. 23, iss. 8, pp. 802–824.

Frey C., Osborne M. (2013) The Future of Employment: How Susceptible are Jobs to Computerisation? Oxford Martin School (OMS) working paper. Oxford. 72 p.

Hirsch-Kreinsen H. (2016) Digitization of industrial work: development paths and prospects. Journal for Labour Market Research, iss. 49, pp. 1–14. https://doi.org/10.1007/s12651-016-0200-6

Huws U. (2014) Labor in the global digital economy: The cybertariat comes of age. New York, Monthly Review Press. 208 p.

Ivanov V. G., Kaybiyaynen A. A., Miftakhutdinova L. T. (2017) Engineering Education in the Digital World. Higher Education in Russia, iss. 11, pp. 21 29. (In Russian)

Keynes J. M. (1931) Essays in Persuasion, Chapter 2 “Economic possibilities for our grandchildren”. URL: https://gutenberg.ca/ebooks/keynes essaysinpersuasion/keynes-essaysinpersuasion-00-h.html(accessed: 28.12.2020).

Kozonogova E. V., Dubrovskaya Yu. V. ,Tiunov D. A. (2020) Features of labor market changes in the conditions of economy digitalization. International Relations 2019: Current issues of world economy and politics. Proceedings of scientific works from 20th International Scientific Conference. 28th — 29th November 2019, Smolenice Castle. Publishing Econom, pp. 600–607.

Kuznetsov N. V. (2018) Change of the employment structure and professional and qualification requirements in the era of digitalization of the economy. Modern problems of science and education, no. 5. URL: http://science-education.ru/ru/article/view?id=27992 (accessed: 10.04.2020).

Lee L., Yu J. (2010) Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects. Journal of Econometrics, pp. 165−185.

Lola I. S., Bakeev M. B. (2019) Digital Transformation in the Manufacturing Industries of Russia: an Analysis of the Business Tendencies Observations Results. St Petersburg University Journal of Economic Studies,vol. 35, iss. 4, pp. 628–657. https://doi.org/10.21638/spbu05.2019.407 (In Russian)

Lopez-Bazo E., Barrio T. Artis M. (2002) The regional distribution of Spanish unemployment: a spatial analysis. Regional science, iss. 81, pp. 366–389.

Lottmann F. (2012) Explaining regional unemployment differences in Germany: a spatial panel data analysis. SFB 649 discussion paper 2012-026, 56 p.

Lugovoy O., Dashkeev V., Mazayev I., Fomchenko D., Polyakov E., Hecht A. (2007) Analysis of Economic Growth in Regions: Geographical and Institutional Aspect. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1268088. (In Russian)

Makhonin D. N. (2020) The terms of the model of innovative development of the region. Budget, iss. 11. URL: http://bujet.ru/article/412926.php (accessed: 10.02.2021). (In Russian)

Markov V. A. (2015) Information technologies and spatial models in the analysis of regional localization and globalization of unemployment. Information security of the regions, vol. 2, iss. 19, pp. 21–27. (In Russian)

Martynovich M., Lundquist K.-J. (2016) Technological change and geographical reallocation of labour: on the role of leading industries. Regional Studies, iss. 50, pp. 1633–1647.

Neffke F., Hartog M., Boschma R., Henning M. (2018) Agents of structural change: the role of firms and entrepreneurs in regional diversification. Economic Geography, vol. 94, iss. 1, pp. 23–48.

Pfeiffer S., Suphan A. (2015) The Labouring Capacity Index: Living Labouring Capacity and Experience as Resources on the Road to Industry 4.0. Working Paper no. 2. University of Ho-henheim, Chair for Sociology. URL: www.sabine-pfeiffer.de/files/downloads/2015-PfeifferSuphan-EN.pdf (accessed: 03.06.2020).

Pianta M. (2005) Innovation and employment in Fagerberg J., Mowery D., Nelson R. (eds). The Oxford Handbook of Innovation. Oxford, Oxford University Press, pp. 568–598.

Puga D. (2002) European regional policy in light of recent location theories. Journal of Economic Geography, iss. 2, pp. 373–406.

Rusanovsky V. A., Markov V. A. (2016) The influence of the spatial factor on the regional differentiation of unemployment in the Russian economy. Problems of forecasting, vol. 5, iss. 158, pp. 144–157. (In Russian)

Rusanovsky V. A., Markov V. A. (2018) Employment and labor productivity in macro-regions of Russia: spatial interdependencies. Problems of forecasting, vol. 2, iss. 167, pp. 36–48. (In Russian)

Rüßmann M., Lorenz M., Gerbert P., Waldner M., Engel P., Harnisch M., Justus J. (2015) Industry 4.0 — the Future and Growth in manufacturing Industries. URL: https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries.aspx (accessed: 06.06.2020).

Sharygin M. D. (2014) Positioning of the Perm Territory in the socio-economic space of the Russian Federation. Geographical Bulletin. Social and economic geography, iss. 4, pp. 22–28.

Shatilo Yu. E., Kopkova E. S. (2017) Employment and Unemployment in a Digital Economy. Theory. Practice. Innovations, vol. 10, iss. 22, pp. 66–77. (In Russian)

Tiunov D. A., Dubrovskaya Yu. V. (2019) Systematization of the causes of unemployment: institutional aspect. Vector of science of Togliatti State University. Series: Economics and Management, vol. 1, iss. 36,pp. 49–54. (In Russian)

Vakulenko E. S. (2020) Comparative Analysis of Interregional and Intersectoral Mobility in Russia. Ekonomika regiona, vol. 16, iss. 4, pp. 1193–1207. https://doi.org/10.17059/ekon.reg.2020-4-13 (In Russian)