Published at : 07 Dec 2020
Volume : IJtech
Vol 11, No 6 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i6.4429
Julia Varlamova | Institute of Management, Economics, and Finance, Kazan Federal University, Kazan 420008, Russia |
Natalia Larionova | Institute of Management, Economics, and Finance, Kazan Federal University, Kazan 420008, Russia |
This
study examined the role of information and communication technologies (ICT) in
labor productivity dynamics. The following ICT components were analyzed: the
share of organizations using computers and the share of organizations using the
internet. The purpose of the study was to analyze labor productivity in the
Russian economy in the context of digital transformation, taking into account
two dimensions: temporal and spatial. We investigated the impact of organizations’
use of ICT on labor productivity, the relationship between labor productivity
and high-tech production, and spatial effects in the dynamics of labor
productivity. We used a spatial autoregressive model (SAR) and a panel vector
autoregressive model (PVAR) to analyze data for the period 2010–2018. The
findings show that digitalization of business processes and an increase in the
share of organizations using internet technologies leads to an increase in
labor productivity. Certain socioeconomic indicators were also found to be
significant, namely, real wages and the percentage of people with higher
education in the workforce. Exogenous variables acting as external shocks did
not exert significant effects. The results have important implications for
managers who develop strategies to increase labor productivity and production
efficiency. Such strategies should focus on internetization, business
digitalization, and e-commerce.
Digital technology; ICT; Labor productivity; Panel VAR model; Spatial model
Labor productivity is one of the most significant
indicators of production companies’ efficiency. The task of increasing labor
productivity is becoming more complicated for enterprises located in regions
with different levels of economic and technological development. In this
context, it is of scientific interest to analyze labor productivity in Russia,
whose economy has its own characteristics due to its large territory and the
uneven economic development of its regions, which include agrarian and
industrial-agrarian regions, regions with high economic growth rates, and
lagging regions. A consideration of spatial effects allows us to draw a clearer
picture of the factors affecting labor productivity.
The rate of change in labor productivity in the period
2008–2018 reflected the economic situation in the world and in Russia: the
global economic crisis of 2008–2009 and the geopolitical crisis of 2014–2015 in
Russia. Labor productivity in 2009 fell sharply by 4.1% compared to 2008. Nevertheless, the growth rate of labor
productivity in the non-crisis years did not exceed 5%, and there is no clear
upward trend during this period.
Digital transformation as a factor affecting labor productivity requires thorough scientific scrutiny. Digital transformation entails technological restructuring of economic sectors. It enables enterprises to achieve a new level of productivity through the implementation of information and communication technologies (ICT). These technologies increase the speed of business processes, reduce transaction costs, and increase the efficiency of resource use. Russia ranked 45th in the ICT Development Index 2017 and 40th in the IMD World Digital Competitiveness Ranking in 2018.
Several studies have recently raised the issue of
digitalization and its impact on various macroeconomic variables (Kolko, 2012; Woodhead and Berawi, 2020).
Researchers have investigated the relationships between ICT and inflation,
productivity (of all factors), international trade, and economic growth. As a
proxy for ICT, researchers often analyze the following indicators: the share of
personal computers, the internet, the cost of ICT, and mobile phones. However,
studies on the contribution of ICT to labor productivity are scarce and have
mainly been conducted at the country level (Shoushtary,
2013; Wiratmadja et al., 2016).
In EU countries, several ICT components have a
positive and significant impact on labor productivity. However, the impact of
enterprise resource planning (ERP), e-commerce, and customer relationship
management (CRM) programs on labor productivity in countries with transition
economies is higher than in developed EU countries (Relich,
2017). The positive impact of ICT on labor productivity has also been
established for OECD countries (Ceccobelli et al.,
2012). The positive effect of ICT on labor productivity is seen for both
high-educated and low-educated older workers in Japan and for low-educated
older workers in Korea (Lee et al. , 2020).
In the Russian economy, such factors as
computerization of workplaces and use of server equipment, mobile subscriber
devices, and broadband internet in organizations that require a high degree of
automation have been shown to have a significant positive impact (Metlyakhin et al., 2020). Studies on labor
productivity in Russia, however, note a downward trend (Abramova
and Grishchenko, 2020).
Many studies have attempted to identify factors that
affect labor productivity. Employment rates negatively correlate with labor
productivity, whereas human and fixed capitals, oil rent, financial
development, trade openness and innovation are positively associated with it (Samargandi, 2018). Bakas
et al. (2020) used
panel data on a sample of 34 OECD countries over a period of three decades and
showed a significant positive relationship between cultural past and labor
productivity. The main factors of this relationship were control and work
ethics, while obedience seemed to negatively affect labor productivity. Access
to electricity can also significantly increase labor productivity in the long
term (Alam et al., 2018).
Regional factors associated with labor productivity
have also been investigated. Zheng et al. (2017)
found that the levels of investment, foreign investment, and exports vary
significantly across regions. Only human capital, real wages, and firm size are
common determinants. A spatial analysis of labor productivity in Germany showed
that its main determinants are job and worker characteristics (Fuchs-Schündeln and Izem, 2012). Although East
and West German skills are very similar, job characteristics are significantly
less favorable in East Germany. A spatial city-level panel data analysis for
China showed that most cities are still underdeveloped and must expand to
accommodate more labor and increase labor productivity. Significant spatial
intercommunions and spatial heterogeneity of urban agglomeration among Chinese
cities have also been observed (Chen and Zhou,
2017).
The purpose of this study was to analyze labor
productivity in the Russian economy in the context of digital transformation,
taking into consideration two dimensions: temporal and spatial. Temporal
analysis was performed to answer the following research question: “How has
digital transformation affected labor productivity in the Russian economy over
time?” Spatial analysis was performed to answer the following research
question: Are there spatial effects in the dynamics of labor productivity? The
aim was to identify possible spatial effects that need to be considered when
developing strategies for multi-territorial production enterprises. We
investigated the impact of digital transformation variables such as the use of
personal computers and the internet by firms.
Our study contributes to the literature in the
following ways. First, the existing literature on the role of ICT in labor
productivity takes gross domestic product as a proxy for labor productivity,
which can produce biased estimates. We studied true labor productivity, which
allows an assessment of the contribution of the labor factor. Second, our
analysis was performed at the regional level, allowing us to take into account
spatial differentiation. Labor productivity investigations at this level are
quite rare. Third, we focused on the temporal and spatial effects on labor
productivity dynamics. To that end, we used spatial autoregressive and panel
vector autoregressive models. To our knowledge, this is the first study to use
these methods to assess labor productivity using data from the Russian economy.
Finally, this study contributes to the literature on the determinants of labor
productivity in developing countries. For Russia, this topic is relatively
unexplored mainly due to data limitations.
This study contributes to the assessment of the determinants of labor
productivity as one of the main characteristics of production efficiency.
Digital transformation affects labor productivity through the implementation
and use of ICTs by increasing the physical objects of digital transformation
(personal computers in organizations) and the internetization of business
processes.
Based on our research results, the following recommendations can be
offered. First, when developing strategies for increasing labor productivity in
multi-territorial enterprises, top managers should take into account the
spatial effects, the interconnection and mutual influences of neighboring regions
on the level of labor productivity in a particular region. Second, the
importance of ICT and the level of wages, which entail additional costs for
enterprises, suggests the need to stimulate private businesses’ investment
activities.
It can be concluded that the study hypotheses were
confirmed. Accordingly, the development of a regional information and
communication infrastructure should become a priority for public authorities.
Labor productivity can then be increased through the modernization of production,
the introduction of high-tech equipment, and the creation of an infrastructure
support system.
This
study was funded by the Russian Foundation for Basic Research (project number
20-010-00663). The work was conducted in the framework of the Russian
Government Program of Competitive Growth of Kazan Federal University.
Filename | Description |
---|---|
R1-IE-4429-20201117030818.jpg | Figure 2 and Appendix. |
Abramova, N., Grishchenko, N., 2020. ICTs, Labour Productivity
and Employment: Sustainability in Industries in Russia. Procedia
Manufacturing, Volume 43, pp. 299–305
Abrigo,
M.R.M., Love, I., 2016. Estimation of Panel Vector Autoregression in Stata. The
Stata Journal, Volume 16(3), pp. 778–804
Alam,
M.S., Miah, M.D., Hammoudeh, S., Tiwari, A.K., 2018. The Nexus between Access
to Electricity and Labour Productivity in Developing countries. Energy
Policy, Volume 122, pp. 715–726
Andrews,
D., Lu, B., 2001. Consistent Model and Moment Selection Procedures for GMM
Estimation with Application to Dynamic Panel Data Models. Journal of
Econometrics, Volume 101(1), pp. 123–164
Bakas,
D., Kostis, P., Petrakis, P., 2020. Culture and Labour Productivity: An
Empirical Investigation. Economic Modelling, Volume 85, pp. 233–243
Ceccobelli,
M., Gitto, S., Mancuso, P., 2012. ICT Capital and Labour Productivity Growth: A
Non-Parametric Analysis of 14 OECD Countries. Telecommunications Policy,
Volume 36(4), pp. 282–292
Chen,
J., Zhou, Q., 2017. City Size and Urban Labor Productivity in China: New
Evidence from Spatial City-Level Panel Data Analysis. Economic Systems,
Volume 41(2), pp. 165–178
Dua,
P., Garg, N.K., 2019. Determinants of Labour Productivity: Comparison between
Developing and Developed Countries of Asia?Pacific. Pacific Economic Review,
Volume 24(5), pp. 686–704
Elhorst,
J.P., 2014. Spatial Econometrics: From
Cross-Sectional Data to Spatial Panels. USA:
Springer
Fuchs-Schündeln, N., Izem, R., 2012. Explaining the Low Labor
Productivity in East Germany – A Spatial Analysis. Journal of Comparative
Economics, Volume 40(1), pp. 1–21
Kolko,
J., 2012. Broadband and Local Growth. Journal of Urban Economics, Volume
71(1), pp. 100–113
Lee,
J.W., Song, E., Kwak, D.W., 2020. Aging Labor, ICT Capital, and Productivity in
Japan and Korea. Journal of the Japanese and International Economies,
Volume 58, 101095
Metlyakhin,
A.I., Nikitina, N.A., Yarygina, L.V., Orlova, E.O., 2020. Analysis of the
Impact of Economy Digitalization on Labor Productivity in Russia. St.
Petersburg State Polytechnical University Journal. Economics, Volume 82(2),
pp. 7–17
OECD, 2019. OECD Economic Outlook. Volume
2019(1). OECD iLibrary
Pesaran,
M.H., 2007. A Simple Panel Unit Root Test in the Presence of Cross-Section
Dependence. Journal of Applied Econometrics, Volume 22(2), pp. 265–312
Relich,
M., 2017. The Impact of ICT on Labor Productivity in the EU. Information
Technology for Development, Volume 23(4), pp. 706–722
Rosstat
Order No. 274, 2018.
?? ??????????? ???????? ???????
?????????? «?????? ?????????????????? ?????» (On Approval of the Method of
Calculating the Indicator “Labor Productivity Index). Available Online at
http://www.gks.ru/metod/pr274-280418.pdf. Accessed on October 23, 2020
Samargandi,
N., 2018. Determinants of Labor Productivity in MENA Countries. Emerging
Markets Finance and Trade, Volume 54(5), pp. 1063–1081
Shoushtary,
M.A., 2013. Effect of Information Communication Technology on Human Resources
Productivity of the Iranian National Oil Company. International Journal of Technology, Volume 4(1), pp. 56–62
Tumilevich,
E.N., 2019. Labor Productivity in Russian’s Regions: Problems and Growth
Perspectives. The European Proceedings of Social & Behavioural Sciences.
CIEDR 2018: The International Scientific and Practical Conference
"Contemporary Issues of Economic Development of Russia: Challenges and
Opportunities." pp. 864–874. Available Online at https://dx.doi.org/10.15405/epsbs.2019.04.93
Accessed on October 23, 2020
Wakeford,
J., 2004. The Productivity–Wage Relationship in South Africa: An Empirical
Investigation. Development Southern Africa, Volume 21(1), pp. 109–132
Wiratmadja,
I.I., Govindaraju, R., Handayani, D., 2016. Innovation and Productivity in
Indonesian Its Clusters: The Influence of External Economies and Joint Action. International Journal of Technology,
Volume 7(6), pp. 1097–1106
Woodhead,
R., Berawi, M.A., 2020. Value Creation and the Pursuit of Multi Factor
Productivity Improvement. International
Journal of Technology, Volume
11(1), pp. 111–122
Zheng,
L., Batuo, M.E., Shepherd, D., 2017. The Impact of Regional and Institutional
Factors on Labor Productive Performance—Evidence from the Township and Village
Enterprise Sector in China. World Development, Volume 96, pp. 591–598