Labour or capital factors: Which influence industrial automation more?

Abstract

Objective: The purpose of the article is to determine which economic factors, specifically those related to labour and capital, have a more significant impact on the level of industrial automation. This assessment is based on robot density per 10 000 employees in the manufacturing sector.

Research Design & Methods: The empirical insights came from a broad array of statistical data spanning from 2000 to 2022, acquired from reputable international institutions. The study employs a methodological framework that integrates a review of pertinent literature, deductive reasoning, and an in-depth comparative analysis of selected time series. The central element of the research is the application of multiple regression analyses, primarily focusing on data from 2020 for 27 nations progressing in manufacturing automation.

Findings: Analysis of time series data on multifactor, labour, and capital productivity in countries with the highest robot densities shows a complex interplay between labour and capital productivity in the realm of industrial automation. Multiple regression analysis, particularly Model 1, substantiated hypothesis H2, revealing that capital-related factors, specifically gross domestic expenditures on R&D and foreign direct investment, emerged as statistically significant predictors of robot density (RD), both exhibiting positive correlations. This underscores the pivotal role of capital investments and technological advancements in fostering automation. Further analysis using Model 2, aggregating labour and capital variables, reaffirmed the predominance of capital factors in influencing industrial automation. The pronounced positive association between the capital index (CAP) and RD highlights the critical influence of capital-related variables, such as technological innovations and investments, in driving the adoption and density of industrial robots, thereby underscoring the foundational role of capital in the advancement of automation in the manufacturing sector.

Implications & Recommendations: The findings highlight a bidirectional influence between automation and productivity in the manufacturing sector, with capital access and utilization playing a pivotal role in automation disparities across economies. Economies reliant on labour-intensive methods lag in automation, underscoring the insufficiency of abundant labour for promoting automation. Instead, capital availability, particularly through R&D spending and foreign investment, emerges as crucial for advancing industrial automation. This necessitates a strategic realignment, where policymakers and industry leaders must prioritize capital investment and technological innovation as key automation enablers. The study calls for comprehensive strategies that emphasize capital investment, technological innovation, skill development, and quality education to effectively engage in the global automation landscape.

Contribution & Value Added: Contrary to the prevalent focus in existing literature on automation’s impact on socio-economic factors, particularly labour productivity, this research adopts a reverse perspective by examining the influence of labour and capital factors on automation progression. The study’s novel approach, asserting the paramountcy of capital in driving automation, suggests that active participation in the global automation landscape necessitates comprehensive efforts encompassing R&D investment, FDI attraction, workforce skill enhancement, and investment in quality education.

Keywords

industrial automation, robotisation, robot density, capital drivers, labour drivers

Author Biography

Marcin Gryczka

PhD, Assistant Professor at the University of Szczecin (Poland). His research interests include international trade, international technology transfer and new phenomena related to Industry 4.0 and their socio-economic implications.

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