Labor Productivity Research Articles

Industry spillover effects of robot applications on labor productivity: Evidence from China

This paper provides new insights into the industry spillover on labor productivity by discussing the spillover effects of robot application among industries in three spillover directions: horizontal, forward, and backward spillover. In addition, this paper measures the three spillovers with the weight matrices from the year 2012 input-output table by the Chinese National Bureau of Statistics. Using a spatial econometric model and 2000–2010 data on China's secondary industry, this paper tests three hypotheses relating to the direct effect and the spillover effects of robot applications. The main results show that the robot application of one industry can improve labor productivity significantly. Moreover, all three kinds of spillover have significant positive effects on the labor productivity of a focal industry. Among them, the forward spillover exerts the greatest impact on labor productivity. The heterogeneity effects exist due to the industry's factor intensity and technological structure etc. The findings of this study could provide several important implications for the development of a given industry and the industry chain of a country as well.

Innovation and Demand as Drivers of Labour Productivity: An Integrated Analysis for OECD Countries

ABSTRACT The article seeks to empirically reconcile two strands of research on the determinants of labour productivity growth: on one hand, the Kaldorian literature emphasises the role of autonomous demand; on the other hand, the Schumpeterian literature underscores the role of innovative investments. Specifically, we test two hypotheses. Firstly, we investigate whether demand shocks, private research intensity, and public R&D expenditure influence labour productivity when these variables are considered simultaneously. Secondly, we examine whether private research intensity is affected by demand shocks and public investments in innovation. We make use of a panel of 12 OECD countries spanning the period from 1986 to 2017. We combine the Local Projection approach with structural shocks estimated through Structural Vector Autoregressive modelling. Findings show a positive impact of government R&D investment, demand shocks, and private research intensity on labour productivity. Additionally, when examining the effects of demand shocks and innovative activities by the public sector on research intensity, the results affirm a positive impact of these variables, thus supporting the presence of the Schmooklerian demand-pull hypothesis. These results remain robust across various model specifications and different measures of labour productivity.

Risk Assessment of Employing Digital Robots in Process Automation

Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities.

Modern information technologies for studying the cost of working time as an important tool for effective management of business processes

Relevance. In the context of economic, social and socio-political changes, growth and increased competition, it is becoming increasingly interesting to study the issues of scientific organization and labor rationing, as well as factors affecting labor productivity, and, ultimately, the effectiveness and profitability of production processes in general. There is an objective need to improve not only the methods of managing the cost of working time, but also to skillfully apply modern digital technologies. That is why it is so important to develop and modify, in accordance with the requirements of the market environment, methods, tools and methods of analysis using artificial intelligence (AI) when studying the cost of working time of employees of an organization.The purpose is based on the study of theoretical and methodological approaches to improving the system of organization and rationing of labor, to provide a scientific justification for the relevance of the development of the processes of accounting for working hours of employees of the organization through the integration of modern information technologies.Objectives: to analyze the existing approaches to the organization and rationing of labor; to identify the potential of integrating modern information technologies into the processes of working time accounting; to develop practical recommendations for improving the system of accounting for the working hours of employees of the organization using modern information technologies.Methodology. The theoretical and practical basis for the study of modern approaches to the modernization of the system of organization and rationing of labor at the enterprise were the methods of analyzing literary sources, statistical data, expert assessments, as well as using an integrated approach.Results. The result of the research in the work is a comprehensive analysis of the use of artificial intelligence (AI) in studying the cost of working time to modify the processes of rationing labor functions through the integration of information technologies.Conclusions. In the course of the study, we proved the positive effect of the introduction of modern information technology systems at the enterprise, as well as scientifically substantiated the relevance of the development of processes for analyzing the cost of working time and rationing employees of enterprises through the integration of modern information technologies.

R&D investments, ownership and local firm growth

Abstract How do research and development (R&D) investments stimulate the local economy? Our paper complements and extends the voluminous literature on the externalities of R&D investments by looking at internal firm growth across regions and the “spillovers” from R&D workers on other workers within the same firm. More specifically, we examine how R&D investments affect the change in employment and productivity in the same firm over time. We also investigate the differences that come with domestic or foreign ownership. Combining data from the Swedish biennial survey series on R&D with firm and establishment registers, we find that R&D-stimulated growth is a localized phenomenon: firms tend to grow in the same regions where they invest in R&D activities. This link is particularly strong for employment effects. While we find that the positive association between R&D and labor productivity holds for all firms, growth effects on local employment are limited to domestically owned firms, especially in manufacturing. The results strengthen our understanding of the correlation between R&D and firm growth on a local level, and contribute to both the academic and policy debate on the effects of globalization on local economic growth.

An inquiry into the causes of income differences among high-income countries

Within high-income countries, there is a notable income inequality spectrum, with certain nations positioned close to a predefined economic threshold, while others within this cohort simultaneously embark on substantial economic growth. This study investigates the influence of critical factors on economic growth and the transition from lower-high and middle-high income to upper-high income. Between 1990 and 2019, 25 of the 28 nations successfully advanced to upper-high income levels. Several key factors contribute to overcoming the barriers associated with lower-high income or middle-high income to upper-high income status. These factors include labor force participation, labor productivity, life expectancy, high-tech exports, reduction in unemployment rates, age dependency ratio, and poverty. The regression discontinuity analysis reveals a positive impact on the experimental group, as economic globalization surpasses the threshold of 78 % facilitating the achievement of upper-high income status.

The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities

Improving energy utilization efficiency is a crucial means to achieve energy conservation, emission reduction, and green development. At present, to establish a high-quality development framework and satisfy the growing need for a better life among all its people, China must steadfastly pursue the path of green development. Although China’s substantial economic scale and achievements in ecological civilization construction provide favorable conditions for green transformation, there remains a significant gap compared to developed countries in the application of green and clean technologies. Confronted with technological bottlenecks, leveraging emerging technologies such as industrial robots from the new round of scientific and technological revolutions to improve the green total factor energy efficiency (GTFEE) is of critical importance to China’s green development. This study explores the potential impact of industrial robots on enhancing China’s GTFEE. It begins by reviewing the current research landscape in this field, highlighting its shortcomings, and theorizing potential impact pathways of industrial robots. Subsequently, the paper analyzes data from 2010 to 2019 on the usage of industrial robots and GTFEE across 276 cities at the prefectural level or above in China. Through empirical regression models that incorporate control variables and interaction terms, the study investigates the specific impacts of industrial robots on energy efficiency and their mechanisms of action. The results indicate that industrial robots significantly enhance the GTFEE of Chinese cities, especially in the Northeastern region. Industrial robots notably improve the GTFEE in resource-based cities, old industrial bases, and low-carbon pilot cities. Additionally, robots indirectly boost GTFEE by increasing labor productivity. Enhanced levels of green innovation and environmental regulations also positively moderate the effectiveness of industrial robots in improving energy efficiency. The findings of this research can assist local government agencies in coordinating and implementing policies that are conducive to green development, making better use of industrial robots to serve the people, and are of significant importance for promoting the transformation of China’s economy and society towards high-quality development.

МҰНАЙ ЖӘНЕ ГАЗ КЕНОРЫНДАРЫНЫҢ ӨНІМДЕРІН ӨНДІРУДЕ ҚАЗІРГІ ЗАМАНҒЫ ЦИФРЛЫ ЖӘНЕ ЗИЯТКЕРЛІ ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНУ АСПЕКТІЛЕРІ

Most oil fields cover thousands of square kilometers, and some have reserves in difficult local conditions (heat, permafrost, desert, deep water) or difficult to recover. Due to these factors and a number of other problems related to technological safety, there are difficulties in the development of oil fields (with a steady increase in demand for oil and gas). Therefore, oil companies still need a highly reliable digital infrastructure that provides high productivity and security and manages many of the devices used in the oil and gas business throughout the field. Therefore, in recent years, oil companies around the world are increasingly managing their oil fields using "smart" technologies. They have proven to be a powerful tool for ensuring the safety of oil fields and increasing labor productivity, as well as economic efficiency. The technologies used include the improvement of all stages of the work process through the rapid acquisition of production data associated with advanced analytics, which contributes to a more complete and efficient development of the oil field. With experience in the implementation of "intelligent" technologies, it already shows high efficiency and very reliable results (both in terms of the oil production process itself and in terms of reducing the cost of individual operations). The use of modern digital and intelligent technologies in the production process in the oil and gas sector plays an important role in order to increase efficiency, ensure safety and reduce environmental impact. Application of digital technologies-data collection and analysis: IoT sensors (Internet of Things) allow you to collect data from deposits in real time and send it to analytical platforms. Field modeling: Using digital Twin technology, virtual field models are created that allow you to predict and optimize production processes. Automation: The automated systems and robotics used help to increase productivity in the management of the oil and gas production process.

Tax shocks, firm entry, and productivity in the open economy

We examine the role of endogenous firm entry for the domestic effects and international repercussions of tax policy. We present new evidence from proxy-vector autoregressions that exogenous US tax reductions increase hourly labor productivity and firm creation domestically, and induce higher trade deficits and real depreciation with respect to the other G7 countries, with positive spillovers to foreign consumption and investment. We show that the empirical evidence is compatible with a two-country model with endogenous firm entry. The entry channel provides a strong amplification mechanism for the supply effects of tax shocks at home and leads to persistent spillovers to the foreign economies.

Regional Economic Development, Climate Change, and Work Force in a Gender Perspective in Chile: Insights from the Input–Output Matrix

Most nations fulfilled the commitment to reduce their emissions after the Paris Climate Agreement, and as a result, each nation has produced suitable plans to reach those goals. In this sense, Chile is hardly an exception. The emission multiplier product matrix (eMPM) and labor multiplier product matrix (lMPM), which are associated with the gender differences in the labor market, a state-of-the-art technique that integrates CO2 emissions with multi-region input–output table (MRIO) databases and elasticity to estimate the pollution caused by inter-industrial activity in the nation’s various north, center, and south regions, are used in this article to analyze the emissions of Chilean industries. This approach, by studying the economic territorial consistency (ETC) issue, is expected to establish a connection between each region’s production structure and interregional relationships between gender and the main industries that produce emissions. Indeed, the study aims to determine which regions foster economic development from an equitable perspective through the ETC study. The ETC in Chile depends on some variables, such as labor force, gender and CO2 emissions. The improvement in terms ofion will depend on the use of technology and the proper state regulation in line with the promises gained by Chile following the convening of COP25.

Economic Effects Assessment of Forest City Construction: Empirical Evidence from the County-Level Areas in China

Forests are both an irreplaceable natural resource and a vital economic asset for all humankind. Based on the data of counties in mainland China from 2007 to 2020, the article explores the direct impact and spatial spillover effects of the policy implementation on the economic growth of counties with the help of the forest city pilot policy and the policy evaluation model. The results reveal that policy implementation can have a positive economic growth effect on the pilot counties, which, in turn, can significantly increase the size of the county′s GDP, the level of GDP per capita, and the total amount of nighttime lighting brightness. The implementation of forest city construction can bring about 2.74% of total GDP size, about 2.63% of per capita GDP development level, and about 7.25% of nighttime light brightness to the county on average. Cost–benefit analysis also indicates that forest city construction can bring about a comprehensive economic benefit of approximately CNY 686.453 million (approximately USD 96.82 million) to the counties. The rapid improvement in labor productivity, significant influx of high-end factors, and continuous expansion of market potential are important mechanisms through which policy implementation promotes economic growth in pilot counties. While promoting economic growth in the pilot counties, forest city construction can also have positive spatial spillover effects on neighboring areas in the pilot counties. Furthermore, when the deficits in atmospheric vapor pressure and annual evapotranspiration are used as instrumental variables for forest city construction, the empirical estimates are not significantly altered. In the process of building forest cities, county governments should be wary of issues such as the high cost of forest maintenance. This study provides a Chinese model and policy reference for other countries and regions in the world to deal with the relationship between forest city construction and county economic growth.

Determining Appropriate Number of Labors in Yellow Noodle SMEs Using K-Means Clustering Method: Meeting Demand While Minimizing Company Costs

Yellow noodle SMEs face the challenge of aligning their workforce with the fluctuating customer demand. Addressing this issue involves scaling up production staffing during peak yellow noodle demand. This study aimed to determine when and how much production labor should be added. Workforce expansion occurred through contractual arrangements during demand surges. This study used the K-Means clustering method to determine demand clusters. Each cluster underwent detailed analysis to calculate the precise number of required production workers. The study used the noodle sales in 2021–2022. Results showed that yellow noodle SMEs had eight permanent workers. Demand clusters were categorized into three classes. High-demand periods were observed in May and December, while moderate demand occurred in January, February, March, April, June, July, and August. September, October, and November constituted the low-demand cluster. Based on these findings, the study recommended adding one worker during high-demand and moderate-demand periods to maintain operational efficiency. Remarkably, no workforce additions were advised during the low-demand phase to optimize resource allocation and cost-effectiveness. In summary, this research addresses labor management challenges for yellow noodle SMEs. Leveraging data-driven approaches and cluster analysis offers SMEs a strategic framework to enhance workforce planning thereby improving operational efficiency and cost-effectiveness. Keywords: yellow noodle SMEs, workforce demand clustering, operational efficiency

Некоторые основные проблемы экономического развития современной России

The work provides assessments of trends and factors of economic growth in modern Russia. The research was carried out on the basis of constructing econometric models using Rosstat data. Research results show that the main trends in economic development consisted of a decrease in the rate of economic growth and the rate of growth in labor productivity. The main factor of economic growth was the growth of labor productivity. There were no significant trends in changes in the growth rate of employment and the growth rate of accumulated fixed capital. The growth in labor productivity was achieved, to a large extent, due to an increase in the capital intensity of production. The basis for economic development was largely an ineffective archaic mechanism, the mechanism for the formation of an industrial economy. Employment growth had a negative impact on economic growth rates. The results obtained indicate that the most important factors of competitiveness and economic growth - human capital and new technologies - are insufficiently and ineffectively used. They did not ensure an increase in labor productivity due to investing in outdated technologies, attracting workers who do not have high qualifications, which is due to a decline in the quality of education and a reduction in the scale of scientific research and development. Education has largely lost its function of forming human capital. The research and development system does not sufficiently serve as a source of new technologies. The identified problems are obviously due to shortcomings in economic policy, shortcomings in financial management and investment processes, insufficient funding and ineffective management of the education system, research and development.

Development and potential of robotisation and automation in Kazakhstan's automobile industry: a bibliographical and analytical review

The engineering sector, which includes automobile production, plays an important role in the national economy and industrial progress. It affects the development of production, scientific and technical progress, labor productivity, national security, quality of life. In order to increase productivity, reduce labor costs and improve product quality, industrial automation and robotization processes have been significantly developed in industrialized countries of the world. The global market for industrial robots is expected to grow 14.3% annually and reach $30.8 billion by 2027. However, Kazakhstan faces special difficulties in adopting these technologies due to the lack of development of automation infrastructure. This study aims to develop priorities and directions of advanced scientific and technological solutions for intelligent joint systems of robotics and automation, especially in the automotive industry of Kazakhstan. In this regard, the purpose of the study is to assess the current state of automation in the automotive industry of Kazakhstan, to identify challenges and opportunities associated with the introduction of robotics, and to provide a strategic basis for the advancement of these technologies. The research corresponds to the state programs of Kazakhstan on science, technology and digitization. The scientific and practical importance of this work is that it is aimed at providing solutions for increasing efficiency by adapting advanced robotic technologies to local production systems. Through the development of domestic innovations and the transfer of global technologies, research contributes to the field of industrial automation and helps Kazakhstan's industries to achieve global competitiveness. It is expected that the practical results of the research will contribute to the improvement of production quality, efficiency and productivity in the automotive sector of Kazakhstan, contributing to sustainable development in accordance with world trends. The value of the work lies in providing individual, technologically advanced solutions to meet the nation's urgent need for robotics automation and integration.

Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing

Precision agriculture is a technology-driven approach to farmer to improve their crop yields and reduce costs. One of the major challenges facing farmers today is the lack of precise prediction which leads to decreased production and mismanagement of labour and resource. Precision technology is costly, and they only rely on manual observations which are less precise. Crop yield prediction systems on cloud computing can solve both problems by predicting the harvested fruit at earlier stages of farming and ease farmers to make decisions. In this study, we proposed a crop yield prediction system for farmers that utilizes cloud computing and machine learning techniques. The system uses data on the physical growth of the plant such as plant’s height at 15 and 30 days after transplant, type of pollination treatment, condition of the leaves, and their variety to predict the crop yield at the early stage. Logistic regression, k-nearest neighbour, and random forest classifier were used to compare the accuracy of the model. Our result shows that by using a random forest classifier, it can achieve an accuracy of 91% which is higher than logistic regression which is only 73% of accuracy, and k-nearest neighbour with 82% accuracy. The study highlights the potential of precision agriculture, cloud computing, and machine learning to revolutionize the way farmers manage their crops and increase their efficiency and productivity, even with the limited resources and hardware that many farmers have.

Granular origins of the Chinese business cycle

ABSTRACT Recent events in China, like Foxconn’s factory downsizing and Evergrande’s bankruptcy, have significantly impacted regional economies, highlighting how idiosyncratic shocks from large firms can generate aggregate effects. Using the ‘granular hypothesis’, which posits a significant impact of large firms’ actions on the macroeconomy, this study investigates how idiosyncratic shocks from large Chinese firms affect regional productivity growth and markups. Our findings demonstrate that idiosyncratic shocks to the 100 largest firms per province significantly impact regional labour productivity and total factor productivity growth but not gross regional domestic product growth. Subsequently, we examine how idiosyncratic shocks impact product market distortions, revealing that idiosyncratic shocks to large firms increase markups in regional markets, suggesting amplified market power.

Technological Progress as a Catalyst for Energy Efficiency: A Sustainable Technology Perspective

Enhancing energy efficiency through sustainable technological progress is imperative for pursuing sustainable development, particularly in developing countries where economic growth is strongly linked to higher energy consumption. In Tunisia, an open and developing economy, fossil energy consumption can be reduced without compromising economic growth by focusing on diminishing energy intensity. This study investigates the effects of technological progress on the overall energy intensity in Tunisia from 1980 to 2019, examining the role that technological advancements may play in attaining sustainable development goals. This study is based on a growth accounting model that uses time-series analysis and a cointegration estimation approach. Our empirical results suggest the presence of a long-term connection between the variables. The attainment of enhanced energy efficiency is contingent on the growth rate of technological progress, which should exceed capital and labor productivity. The results reveal that the net impact of technological advancements is estimated at -0.0163, which signifies that aggregate energy intensity tends to decrease by an annual average of 1.63% in the long term. These findings underscore the need for developing economies to restructure their energy policies and support sustainable technological initiatives and green entrepreneurship. Developing countries can reduce their energy intensity while boosting their economic growth by fostering technological innovation. This study demonstrates how technological progress and green entrepreneurship can be leveraged to enhance energy efficiency and drive sustainable development in developing nations.

Производительность труда в российской Арктике: структурная динамика и регионально-отраслевые тенденции

Labor productivity is a key indicator of economic development and public well-being. This study assesses the structural shifts, regional dynamics, and sectoral trends in the domain of labor productivity in the Russian Arctic from 2011 to 2021, identifying aggregated economic sectors. The findings reveal that labor productivity levels in the Russian Arctic exceed the national average, primarily due to the resource-based nature of regional economies. However, a major issue is the persistently high level of interregional differentiation in labor productivity within the Arctic. The study demonstrates that an initially high level of labor productivity does not necessarily ensure long-term growth. An analysis of structural shifts in labor productivity over the past decade indicates a shift towards the market sector of the Arctic economy, evidenced by an increased share in gross regional product (GRP) and a decrease in time worked. Within the market sector, several trends are observed: the leading role of the extractive industry is maintained due to its expanding share in GRP; the role of agriculture is growing, reflected in a decreased share of hours worked; and the importance of other market sector branches, particularly transport, communication, and electric power, is diminishing as their share of total hours worked increases. The observed decline in labor productivity within the non-market sector suggests the potential for negative social consequences for the Arctic population. An analysis of labor productivity growth rates indicates that the leading sectors in terms of growth are mining, finance and business services, construction, and trade. These sectors can be considered sources of economic growth in the Arctic. Interregional differences in labor productivity growth rates within the same sector of the Arctic economy suggest the influence of regional factors, highlighting the potential for discovering unique reserves of productivity growth in each region.

Non-optimum temperatures led to labour productivity burden by causing premature deaths: A multi-country study

BackgroundNon-optimum temperatures are associated with a considerable mortality burden. However, there is a lack of evaluation of labour productivity losses related to premature deaths due to non-optimum temperatures. This study aimed to quantify the labour productivity burden associated with premature deaths related to non-optimum temperatures and explore the potential socio-economic vulnerabilities. MethodsDaily all-cause mortality data were collected from 1,066 locations in 7 countries (Australia, Brazil, Canada, Chile, New Zealand, South Korea, and Thailand). Productivity-Adjusted Life-Year (PALY) loss due to each premature death was calculated to measure the labour productivity loss, by multiplying the years of working life lost by the proportion of the equivalent full-time (EFT) workers. A two-stage times series design and the generalized linear regression model with a quasi-Poisson family were applied to assess the association between non-optimum temperatures and the PALY loss due to premature deaths. ResultsWe observed a U-shaped relationship between temperature and PALY lost due to premature mortality. We estimated that 2.51% (95% eCI: 2.05%, 2.92%) of PALY losses could be attributed to non-optimal temperatures, with cold-related deaths contributing 1.26% (95% eCI: 0.94%, 1.54%) and heat-related deaths contributing 1.25% (95% eCI: 0.96%, 1.51%). Cold temperature contributed to the most PALYs lost in those aged 45–54 and 55–64, while heat-related losses predominated among the 15–44 age group. We also observed that the fractions of PALY lost attributed to extreme heat were positively associated with the relative deprivation index, while negatively associated with GDP per capita. ConclusionThis multi-country study highlights that non-optimum temperatures led to a considerable labour productivity loss and socioeconomically disadvantaged communities experience greater losses.

Bedeutung und Entwicklung der Produktivität

Abstract Labour productivity is an important determinant of competitiveness. for competitiveness. It is significantly influenced by the total factor productivity. In Austria, as in most other industrialized countries, growth in labour productivity and total factor productivity has considerably in recent decades. The starting points for reviving productivity growth are the promotion of investment and research and development as well as strengthening competition and the risk capital market.