Rising Labor Costs Research Articles

Integration of flexible corporate demographic policy tools into the personnel management system

Amid staff shortages and rising labour costs, many Russian enterprises are looking for non-material methods to man age personnel. Organizational, labour and socio-psychological aspects of employee incentives are closely linked to the corporate demographic policy tools. The article aims to analyse the demand for flexible working arrangements for employees with family responsibilities, as well as to determine the opportunities for integrating flexible organizational and labour tools of corporate de mographic policy into the personnel management system. Flexibilization theory in the context of corporate demographic policy constitutes the methodological basis of the study. The standard methods of structured personal interviews, frequency analysis with equality of proportions hypothesis test using a chi-square statistic, and comparison of mean values based on the Kruskal Wallis test were applied. The empirical evidence includes collective agreements and local regulations of the largest employers in the Russian Federation, as well as results of a sociological survey conducted in June–September, 2023 (the analyzed sample cov ered 2,804 respondents). The study statistically proves that employees working flexible hours are more satisfied with their job and life compared to those with rigid schedules. It also shows that flexible working arrangements are in demand among employees with parental commitments. The paper presents a model and an algorithm for integrating flexible organizational and labour tools of corporate demographic policy into the personnel management system. We develop a list of HR metrics and identify potential positive implications of this integration through strengthening the HR brand, increasing employee loyalty and engagement, and reducing staff turnover.

VAT Reform and Labor Cost Stickiness: Evidence from China

ABSTRACT Rising labor costs have weakened product competitiveness and posed challenges to economic growth. We investigate the effects of a tax cut in alleviating labor cost stickiness by exploring China’s shift from a production-based to a consumption-based value-added tax (VAT) between 2004 and 2009. Using a multi-period DID model and data from Chinese A-share-listed firms for 2003 through 2011, we find that the VAT reform significantly reduced firms’ labor cost stickiness and increased their ability to manage employment adjustment costs by providing wage flexibility. The stickiness of labor costs declined most for firms characterized by labor-intensive operations, situated in more market-oriented areas, and with higher growth opportunities. Our findings show that VAT reform has had a real effect on firms by alleviating labor cost stickiness.

Effects of minimum wage policies on executive compensation contracts: An empirical analysis in China

This paper investigates the impact of minimum wage policies on executive pay-for-performance sensitivity (PPS) in the compensation packages of A-share listed firms in China. Using data from 745 counties between 2007 and 2020, we find that minimum wage policies lead to a decrease in PPS. This finding remains robust through instrumental variable approaches, cross-border matching tests, alternative measurements, and other rigorous tests. Moreover, the negative impact of minimum wage on PPS is more pronounced in companies with higher labor dependency, limited ability to pass on labor costs to customers, and lower risk resilience. These results support the hypothesis that rising labor costs due to increased minimum wage, which lead to performance distortion and heightened risk, promote shareholders to reduce PPS to optimize compensation contracts for value maximization.

Standortnachteil Arbeitskräftemangel: Österreich braucht eine umfassende Fachkräfteoffensive

Abstract Shortages of skilled workers are now widespread on the Austrian labor market. This results in serious locational disadvantages such as rising labor costs and a lack of personnel to develop and make use of innovations. Austria should therefore launch a comprehensive skilled labor campaign soon. This should correct the framework conditions that currently hinder satisfactory labor market integration of women, older people and migrants, as well as improve employee mobility, since productivity reserves can be leveraged through better job matches.

Abandonment Leads to Changes in Forest Structural and Soil Organic Carbon Stocks in Moso Bamboo Forests.

The important role of soil carbon pools in coping with climate change has become widely recognized. Moso bamboo (Phyllostachys pubescens) is an economically important bamboo species in South China; however, owing to factors such as rising labor costs and increasingly stringent environmental policies, Moso bamboo forests have recently been abandoned. The present study aimed to investigate the effects of abandonment on structural factors and soil organic carbon (SOC) stocks in Moso bamboo forests. We investigated Moso bamboo forests subjected to intensive management or abandonment for different durations and measured forest structural characteristics, mineral properties, soil nutrients, and other soil properties. Although abandonment did not significantly affect the height and diameter at breast height, it increased culm densities, biomass, and SOC stocks. The drivers of SOC stocks depended on soil depth and were mainly controlled by carbon decomposition mediated by soil properties. In the topsoil, mineral protection and soil total nitrogen (TN) exerted significant effects on SOC stocks; in the subsoil, soil TN was the main driver of SOC stocks. As the controlling factors of SOC stocks differed between the subsoil and topsoil, more attention should be paid to the subsoil. Overall, these findings refine our understanding of the structural characteristics and SOC stocks associated with Moso bamboo forest abandonment, serving as a reference for the follow-up management of these forests.

Cultivating sustainability and resilience in olive heritage

Learning outcomes The case study’s learning objectives, structured according to Bloom’s taxonomy, engage students in higher-order thinking for critical analysis and strategic decision-making. After completion of the case study, students will be able to analyze sustainability practices and evaluate the family’s distribution model for alignment with sustainability and potential expansion strategies; explore adaptive strategies to identify adaptive solutions to challenges such as climate change and economic fluctuations; apply theoretical frameworks to balance exclusivity and growth in the olive oil industry; and assess strategic decisions considering financial viability, environmental responsibility, community engagement and sustainability. Case overview/synopsis The case study centers on the Abi Raad olive farm, a multigenerational family-owned business deeply rooted in olive cultivation traditions in Selfaya village, within Mount Lebanon’s Aley district. Led by Richard Abi Raad, the family specializes in olive cultivation, olive oil production and handmade soap manufacturing within the agriculture sector. In 2023, the organization faces significant challenges such as rising labor costs and the exploration of sustainable labor sources for future growth. Abi Raad, the primary protagonist, grapples with critical decisions regarding maintaining traditional distribution methods versus expanding into broader markets. His choices must balance the preservation of cultural heritage with the need to uphold brand integrity and competitiveness. The case study traces the family’s historical journey in olive farming and explores their contemporary strategic dilemmas, offering insights into sustainable agriculture, entrepreneurship and navigating modern challenges while honoring tradition. Through the narrative of the Abi Raad family-owned business, the case study provides profound lessons in strategic decision-making and ensuring long-term sustainability. Complexity academic level This case study is suitable for students studying master’s in business. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Impact of subsidy on the new type of forestry operating entity's input: Moderating effect of the rising labor cost

Impact of subsidy on the new type of forestry operating entity's input: Moderating effect of the rising labor cost

A multinational firm’s global manufacturing location and outsourcing strategies

Due to rising labor costs in China and trade friction, many multinational firms (MNFs) consider moving manufacturing outside China to Southeast Asian Countries. Based on observations in real world, we build a three-tier supply chain model consisting of one component supplier, two contract manufacturers (CMs), an MNF, and a local original equipment manufacturer (OEM). The MNF monopolizes the market in country 1 (e.g., U.S.), but competes in country 2 (e.g., China) with the local OEM. The component supplier and one CM are located in country 2, and the other CM is located in country 3 (e.g., a country in Southeast Asia). We investigate the MNF’s global manufacturing location and procurement outsourcing strategies with considerations of tariffs and market environments. Currently, the MNF outsources its manufacturing to the CM in country 2, and decides whether to continue the cooperation with the current CM or develop a new CM in country 3. The MNF also considers whether to outsource its component procurement to the corresponding CM. Our study reveals that the MNF’s optimal strategy depends on the relative market sizes, the fixed cost of developing a new CM, as well as the manufacturing cost and tariff disadvantages in country 2. Interestingly, we find that the tariff disadvantage in country 2 does not always motivate the MNF to cooperate with the CM in country 3. Instead, an appropriate tariff may be beneficial to the MNF that manufactures in country 2. In addition, the MNF’s preferred procurement strategy is related to its manufacturing location. When cooperating with the CM in country 2, the MNF would switch from direct procurement to outsourced procurement as the market size of country 1 increases.

Competitiveness of Sri Lankan Tea in the International Market: A Trade Flow Analysis

For more than 150 years, Sri Lanka has had a significant influence on the world market for tea. Sri Lanka is one of the world's top producers and exporters of tea. However, Sri Lanka's tea industry encounters a number of challenges due to internal and external market conditions, including governmental regulations, rising labour costs, and supply chain disruptions. These challenges can affect the competitiveness of Sri Lanka’s tea exports in the global market. Therefore, it is important to understand how the competitiveness of Sri Lankan tea exports in the global tea market changed over time. The specific objectives of the study were to examine the relative contribution of Sri Lanka’s tea exports to the international market and to assess the level of competitiveness of Sri Lanka’s tea exports. Based on the positivist research philosophy and quantitative approach, this study adopted four indexes to assess the level of competition. These indices include export market share (MS), export unit price (EUP), revealed comparative advantage (RCA), and revealed trade advantage (RTA). The study's findings demonstrated that while the export unit price exhibited an upward trend, Sri Lanka's market share in the world tea market has decreased over time. According to the findings of the RCA and RTA indices, Sri Lanka's competitiveness in terms of international tea exports has continuously decreased. Kenya, on the other hand, has witnessed a rise in tea export competition, while China and India have seen a decline in tea export competition. This study emphasizes the need for implementing efficient production methods in order to provide Sri Lankan tea to the global market at a lower price. Additionally, government policies ought to focus on offering technical assistance to boost productivity in the Sri Lankan tea industry.

20 Jahre nach der EU-Erweiterung: Visegrád-Länder haben weiterhin einen Vorteil bei der Kostenwettbewerbsfähigkeit

Abstract Twenty years after the EU accession of the Visegrád countries – Poland, the Czech Republic, Slovakia and Hungary – we analyse their development of unit labour costs and of the labour share in manufacturing for the years 1995 to 2023. Firstly, the labour shares in the Visegrád countries have risen significantly since 2004 and have converged towards the levels of Germany and France. Secondly, a convergence of unit labour costs in manufacturing is detectable, which is not yet complete. Thus, the Visegrád countries still have an advantage in terms of cost competitiveness as rising labour costs have been partly offset by significant productivity gains.

Responses of Soil Carbon and Microbial Residues to Degradation in Moso Bamboo Forest.

Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is known for its high capacity to sequester atmospheric carbon (C), which has a unique role to play in the fight against global warming. However, due to rising labor costs and falling bamboo prices, many Moso bamboo forests are shifting to an extensive management model without fertilization, resulting in gradual degradation of Moso bamboo forests. However, many Moso bamboo forests are being degraded due to rising labor costs and declining bamboo timber prices. To delineate the effect of degradation on soil microbial carbon sequestration, we instituted a rigorous analysis of Moso bamboo forests subjected to different degradation durations, namely: continuous management (CK), 5 years of degradation (D-5), and 10 years of degradation (D-10). Our inquiry encompassed soil strata at 0-20 cm and 20-40 cm, scrutinizing alterations in soil organic carbon(SOC), water-soluble carbon(WSOC), microbial carbon(MBC)and microbial residues. We discerned a positive correlation between degradation and augmented levels of SOC, WSOC, and MBC across both strata. Furthermore, degradation escalated concentrations of specific soil amino sugars and microbial residues. Intriguingly, extended degradation diminished the proportional contribution of microbial residuals to SOC, implying a possible decline in microbial activity longitudinally. These findings offer a detailed insight into microbial C processes within degraded bamboo ecosystems.

Polluted air, smarter factories? China's robot imports shed light on a potential link

Robot-centered intelligent manufacturing is leading a new round of technological revolution and industrial change. Existing studies have neglected the impact of air pollution, a non-monetary factor, on firms' intelligent manufacturing transformation (FIMT). Using matched samples from China's industrial enterprise database and China's customs database, and based on enterprises' “machine replacing human” strategy, this paper reveals the facilitating effect of air pollution on FIMT. The robustness estimation results after endogeneity treatment, replacing the core variables, changing the clustering level, removing the policy influence, avoiding the interference of domestic robots, changing the estimation strategy, and restricting the import trade mode still support the conclusion. The mechanism by which air pollution accelerates FIMT lies in the first: the factor substitution effect of rising labor costs due to air pollution. The second is the skill-capital complementarity effect due to increased firms' human capital investment induced by air pollution. The third is the intelligent investment effect due to increased economic policy uncertainty induced by air pollution. In addition, air pollution accelerates FIMT more significantly in non-state-owned firms, high labor-intensity industries, and regions with high population aging. This paper argues that there is no conflict between environmental governance and intelligent manufacturing transformation. Air pollution and “machines replacing humans” in the labor market efficiency and fairness issues need attention.

Tackling financialization amidst rising labor cost in China

Suppressing corporate financialization exacerbated by rising labor costs is an important micro-level issue for revitalizing the manufacturing industry. This research investigates the interaction mechanism among labor costs and financialization, using panel data of Chinese listed companies from 2007 to 2022 in the context of the disappearance of the demographic dividend. The results of the moderated mediation model show that rising labor costs worsen financialization by reducing corporate profitability and increase financialization through negative earnings manipulation, whereas digitization suppresses both the direct and indirect effect of labor costs. Therefore, measures should be taken to enhance the profitability and accounting transparency of companies undergoing the process of accelerating digitization.

Herbicide Strategies for Weed Control in Rice Cultivation: Current Practices and Future Directions

Rice, as a staple food for more than half of the world's population, plays a crucial role in global food security. However, weed competition poses a significant challenge to rice cultivation, affecting yield and quality. To address this challenge, rice farmers have increasingly turned to chemical herbicides as a cost-effective alternative to labor-intensive manual weeding. This review paper explores the current practices and future directions in herbicide strategies for weed control in rice cultivation, with a specific focus on diverse approaches adopted in various agro-ecological regions, particularly in Pakistan. The paper begins by highlighting the importance of rice cultivation in global agriculture and the economic significance of the crop, emphasizing the need for effective weed management to ensure sustainable production. It discusses the impact of labor shortages and rising labor costs on weed control practices, driving the adoption of chemical herbicides, especially in direct-seeded rice cultivation. Additionally, the review underscores the importance of integrated weed management systems, which combine cultural practices, targeted herbicide applications, and legislative measures to optimize crop productivity while minimizing environmental impact. Furthermore, the paper evaluates the efficacy of different herbicides and their timing of application in managing weeds and maximizing rice yield. It synthesizes findings from recent studies to provide insights into the effectiveness of pre-emergence and post-emergence herbicides, as well as their impact on weed-crop competition and overall crop health. By analyzing the strengths and limitations of existing herbicide strategies, the review identifies opportunities for innovation and improvement in weed management practices. Overall, this review paper offers a comprehensive overview of herbicide strategies for weed control in rice cultivation, highlighting the need for sustainable and integrated approaches to address weed challenges while ensuring food security and environmental sustainability in rice-producing regions.

Application and evaluation of deep learning based image recognition techniques in agriculture

Agriculture, as an important industry in society, is facing problems such as an aging population and rural labor exodus, which leads to rising labor costs and uncertainty in agricultural production. Deep learning techniques are considered as a key tool to solve this problem. In this paper, three popular deep learning algorithms, namely, Region-based Convolutional Neural Network, You Only Look Once, and Single Shot MultiBox Detector, are introduced and their working principles are described in detail, while the advantages and disadvantages of these algorithms are briefly analyzed. Additionally, this paper specifically analyzes the application of these three algorithms in three agricultural scenarios, such as timber species recognition, fruit picking, and pest identification. The results show that although the three algorithms are slightly different in terms of accuracy and detection speed, they all demonstrate the potential for a wide range of applications in the agricultural field. Therefore, deep learning technology is of great significance in solving the problem of rural labor shortage, especially when combined with advanced equipment, which is expected to significantly improve the efficiency of identification, monitoring, and harvesting in agriculture and promote the development of automated agriculture.

Mobile Robot for Security Applications in Remotely Operated Advanced Reactors

This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots’ potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots’ performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors.

Assessing water-nitrogen use, crop growth and economic benefits for maize in upper Yellow River basin: Feasibility analysis for border and drip irrigation

Water-saving irrigation is becoming more important in the upper Yellow River basin (YRB) due to reduced water allocation and growing water scarcity. However, drip irrigation as an efficient irrigation method, has not gained as much acceptance as one might expect. In this study, integrated approaches involving field experiments, agro-ecosystem modeling, and financial analysis were proposed to evaluate the multiple benefits of two irrigation methods. Field experiments on maize irrigated with border irrigation and drip irrigation under plastic mulching (i.e., BI-M and DI-M) were conducted in the Hetao Irrigation District (Hetao) of the upper YRB during 2021 and 2022. The AHC model was calibrated and validated using two-year experimental data, performing well in simulations of soil water-salt-nitrogen (N) dynamics and crop growth. An irrigation scheduling module was newly incorporated into AHC. Then the model was applied to analyze scenarios consisting of three classes of groundwater depth (GWD) and five N application levels. Optimal irrigation and N-fertilization strategies were suggested; and DI-M showed significant advantages over BI-M in terms of water-saving (56–66 mm), labor-saving, environmental benefits (50 kg ha−1 less N fertilizer and 19–25 kg ha−1 less N loss), and crop yields (<4%), both in experimental and scenario cases. However, the financial analysis revealed that the current smallholder BI-M could achieve higher net returns (about 12%) compared to DI-M, since the family labor is often not counted as a cost in smallholder farming. Additionally, the widespread adoption of BI-M is also partly attributed to its easy operation. Lastly, results indicated a trend where the advantages of drip irrigation would become more significant in the future, with the increasing agricultural population aging and rising labor costs in the YRB.

Tea yield estimation using UAV images and deep learning

Traditional estimation of tea yield significantly depends on fields observation, which is facing operational and management challenges due to increasing farm sizes and rising labor costs. Development of artificial intelligence such as deep learning (DL) and unmanned aerial vehicles (UAVs) provide opportunities where intelligent system of tea fields will be constructed. This study carried out the application of tea yield estimation model to practical field scenarios. A large dataset of UAV tea buds’ images was built, which contained 5899 images and 29,958 labelled tea buds. Based on the large dataset, YOLOv5 model was used to train, and CSPDarknet53 performed best over Swin Transformer and ConvNeXt as backbone. The mean average precision (mAP), precision, recall and F1 score of CSPDarknet53 were respectively 85.63%, 84.91%,72.19% and 78.00%. Meanwhile, the large database weakened the differences among models of different sizes. Squeeze-and-excitation (SE) block was inserted, and the mAP of all models with CSPDarknet53 and SE was over 85.00%, indicating models could be applied to tea fields. To reduce the number of repeated tea bud detection boxes in the prediction process, a second non-maximum suppression (NMS) filter was used with threshold value of 0.20. The yield of fresh tea leaves before and after picking was estimated according to the number of tea buds detected in different seasons and different types of tea plants. The yield of fresh tea leaves in spring before picking was estimated to 1223.22 kg/ha. This study is a complete report from model training to tea field application, and will advance the industrialization development of agriculture.

Customer Concentration and Labor Investment Efficiency: Evidence from China

ABSTRACT: The labor force constitutes a paramount factor of production in the realm of business operations. In the past, China's abundant labor force significantly boosted productivity for firms. However, as the demographic dividend gradually wanes, concerns have arisen regarding a scarcity of labor force, exacerbating worries about rising labor costs. Therefore, focusing on enhancing firms' labor investment efficiency becomes pivotal. Using a sample of A-stock listed firms in China from 2013 to 2020, we examine the impact of customer concentration on the labor investment efficiency of supplier firms. Prior literature provides mixed results regarding the effect of concentrated customers. They can act as a governance mechanism for reducing agency problems, thus, increasing investment efficiency. Conversely, agency problems are aggravated in suppliers with concentrated customers, consequently, reducing investment efficiency. Our results suggest that customer concentration reduces labor investment efficiency, and this effect is more pronounced when customers possess high bargaining power. Additionally, the mechanism analysis reveal that customer concentration leads to less accurate information disclosure, higher operating risk, and an incentive to "empire building", reducing labor investment efficiency. The cross-sectional analysis reveals that customer concentration results in both over- and under-investment in labor, thereby reducing investment efficiency. In addition, we employ augmented models to rule out the possibility of a U-shaped relationship between customer concentration and labor investment efficiency. Furthermore, we adopt the instrumental variables approach as well as a two-stage regression model to address potential endogeneity concerns and mitigate the omitted variable concern. Our results hold after the robustness tests and endogeneity tests. The findings of this paper imply that firms should strategically diversify their customer bases, thereby reducing their reliance on a few large customers. Simultaneously, governments should actively encourage firms to broaden their customer base. It can help enhance labor investment efficiency by spreading the risks associated with customer concentration. Moreover, it is crucial to acknowledge that substantial customer bargaining power can negatively impact supplier firms. Thus, policymakers should promote antitrust regulations and fair trade practices to mitigate the high bargaining power of large customers.

Lightweight Machine Learning Method for Real-Time Espresso Analysis

Coffee crema plays a crucial role in assessing the quality of espresso. In recent years, in response to the rising labor costs, aging population, remote security/authentication needs, civic awareness, and the growing preference for non-face-to-face interactions, robot cafes have emerged. While some people seek sentiment and premium coffee, there are also many who desire quick and affordable options. To align with the trends of this era, there is a need for lightweight artificial intelligence algorithms for easy and quick decision making, as well as monitoring the extraction process in these automated cafes. However, the application of these technologies to actual coffee machines has been limited. In this study, we propose an innovative real-time coffee crema control system that integrates lightweight machine learning algorithms. We employ the GrabCut algorithm to segment the crema region from the rest of the image and use a clustering algorithm to determine the optimal brewing conditions for each cup of espresso based on the characteristics of the crema extracted. Our results demonstrate that our approach can accurately analyze coffee crema in real time. This research proposes a promising direction by leveraging computer vision and machine learning technologies to enhance the efficiency and consistency of coffee brewing. Such an approach enables the prediction of component replacement timing in coffee machines, such as the replacement of water filters, and provides administrators with Before Service. This could lead to the development of fully automated artificial intelligence coffee making systems in the future.