• International Journal of Technology (IJTech)
  • Vol 11, No 6 (2020)

Labor Productivity in the Digital Era: A Spatial-Temporal Analysis

Labor Productivity in the Digital Era: A Spatial-Temporal Analysis

Title: Labor Productivity in the Digital Era: A Spatial-Temporal Analysis
Julia Varlamova, Natalia Larionova

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Cite this article as:
Varlamova, J., Larionova, N. 2020. Labor Productivity in the Digital Era: A Spatial-Temporal Analysis. International Journal of Technology. Volume 11(6), pp. 1191-1200

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
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Labor Productivity in the Digital Era: A Spatial-Temporal Analysis

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.

Supplementary Material
R1-IE-4429-20201117030818.jpg Figure 2 and Appendix.

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