Improvements In Total Factor Productivity Research Articles

Assessment of soybean productivity and its changing factors in Japan based on the production cost statistics

Soybean (Glycine max [L.] Merr.) yields have remained below 2.0 t ha–1 for over 30 years in Asian countries, indicating long-term stagnation in productivity and production technology. This study aimed to measure the total factor productivity (TFP) of soybean production in Japan and to assess the factors affecting TFP and yield. The Färe–Primont index of TFP was measured for two agroclimatic regions using soybean production cost statistics from 1987 to 2015 to analyze the regression of TFP and yield with per-farm land size, time trends, and production-related input variables. The TFP increased by 1.5–2.4% annually in the two regions, despite an annual decrease of 0.7% in country’s average yield. The TFP in the subarctic Hokkaido region improved by 9–15% due to the land-size increase and progress of time, i.e., technological progress, during the period. While in the temperate main-island region, a 14% TFP increase induced by increased land size was largely offset by the time–progress effect (–11%), i.e., technological retrogression. Similar time–progress effects observed on yield indicated that technological changes occurred, with respect to cultivation methods. The TFP–improving land-size increase adversely resulted in a 9% yield decrease in the main-island region accounting for 85% of soybean cultivation area in Japan. The results further showed that TFP improved by saving production inputs, such as fertilizer, per unit area in both regions, and the yield rose with increased inputs in seed, agrochemicals, and rental services, depending on the region. This study revealed continual soybean TFP improvements across Japan due to land-size increases and input saving in addition to the regionally biased progress in cultivation technologies. The implications of the results are discussed from the perspective of improving TFP and yield while increasing the per-farm land size.

Evaluating the mechanism of AI contribution to decarbonization for sustainable manufacturing in China

While existing theories investigate the relationship between digital technologies and environmental, social responsibility, and governance (ESG) performance, this study formulates a distinct theoretical framework aimed at understanding of the role played by specific artificial intelligence (AI)-based technologies for decarbonization in the manufacturing processes. Drawing upon data sampled from 2607 manufacturing firms listed in Chinese stock markets spanning the years 2004–2020, this study employs an ordinary least squares regression model to investigate the mechanism in question. The findings demonstrate that AI applications facilitate carbon reduction through innovations in terms of production, technology, and management in manufacturing processes, particularly in large-scale enterprises, profitability, and state-owned entities. AI applications integrated into manufacturing processes contribute to ESG performance, especially in terms of environmental (E) considerations and governance (G). This contribution is manifested via improvements in total factor productivity, technological innovation, and information disclosure. Notably, the contributions are more pronounced in less-polluting firms and those located in the underdeveloped western regions of China than in their counterparts. These findings not only enhance the theoretical underpinnings of the ESG framework but also offer practical insights that can serve as a valuable reference for policymakers and governing bodies overseeing manufacturing economies.

The Impact of Digital Economy on TFP of Industries: Empirical Analysis Based on the Extension of Schumpeterian Model to Complex Economic Systems

The digital economy (DE) is a new driver for enhancing total factor productivity (TFP). Using panel data from 30 provinces in China between 2011 and 2022, this study measures DE and TFP using the entropy-weighted TOPSIS method and the Global Malmquist–Luenberger method and further examines the impact of DE on the TFP of industries. The main findings are as follows: (1) DE can significantly improve TFP, though the extent of improvement varies. DE has the greatest impact on the TFP of the service industry, followed by the manufacturing industry, with the weakest effect on the agricultural industry. (2) The enhancement effect of DE on agriculture and the service industry is more pronounced in the central and western regions, while the improvement effect on manufacturing is more evident in the eastern region. (3) DE has facilitated the improvement of TFP in manufacturing industries such as textiles and special equipment manufacturing, as well as in service industries like wholesale and retail. However, it has not had a significant impact on the TFP of industries such as pharmaceutical manufacturing and real estate. This study has significant theoretical value and policy implications for China and other developing countries in exploring DE and achieving high-quality industrial development.

Measuring intra-industry trade and productivity in the South African pharmaceutical sector

Background: The South African pharmaceutical sector is Africa’s largest and most advanced but heavily depends on imported products and active pharmaceutical ingredients to meet its demands. The status quo is due to low intra-industry trade, which is necessary to induce innovation and technological progress essential for accelerating local production, export growth and reducing dependence on imports.Aim: The primary objective of this article was to examine intra-industry trade in South Africa’s pharmaceutical sector and subsequently, total factor productivity (TFP) as a key driver of intra-industry trade.Setting: Intra-industry trade was measured using data obtained from the United Nations Conference on Trade and Development databases, while the TFP was measured using data from the South African Reserve Bank (SARB) covering 2001–2021.Method: The marginal intra-industry trade index (MIIT) and unmatched changes in trade (UMCIT) were used to measure intra-industry trade, while the Malmquist total factor productivity (MTFP) index was used to analyse SARB data to determine TFP.Results: The MIIT index and UMCIT revealed that trade is predominantly inter-industry with episodes of industry specialisation and significant intra-industry changes. The MTFP results showed that TFP is solely driven by technical changes.Conclusion: The study recommends that South Africa should develop a coordinated and sustainable innovation system in the pharmaceutical sector. This will help prevent sporadic technological advancements and promote intra-industry trade.Contribution: The article contributes to the empirical literature on intra-industry trade in developing countries by showing the correlation between an improvement in TFP and an increase in intra-industry trade necessary to stimulate domestic production to reduce high import demand.

Examining the impact of environmental centralization on enterprise total factor productivity through a quasi-natural experiment conducted in China

This paper explores the effects and influence mechanisms of environmental centralization on enterprise technological progress and productivity in China. Taking the reform of vertical environmental governance (VEG) as a quasi-natural experiment, this paper compares the differences in total factor productivity (TFP) of enterprises in environmental centralization regions and environmental decentralization regions by adopting the staggered difference-in-differences (DID) method. The empirical results show that: (1) The average TFP of enterprises in the environmental centralization areas is 0.0598 higher than that in the environmental decentralization areas, and this average effect increases with the extension of the reform duration. (2) Environmental centralization strengthens government intervention in environmental issues. The improvement in the intensity of environmental regulation and the willingness of firm green innovation are the intermediate causes of the improvement in enterprise TFP. (3) VEG’s effect is heterogeneous regarding regional development, industry type, and enterprise characteristics. This study has empirical implications for further refining the fiscal system to leverage the role of public finance on environmental governance and enterprise development.

The Impact of Corporate Social Responsibility on Total Factor Productivity

With the advent of globalization, modern enterprises, as crucial pillars of socio-economic development, directly influence their own economic benefits and societal stability. In an increasingly competitive market, enterprises should not only pursue the maximization of economic benefits but also fulfill their social responsibilities, thereby shaping a positive social impact and gaining wide recognition across various sectors. Thus, the concept of corporate social responsibility has emerged, becoming an indispensable part of corporate development strategies. Concurrently, total factor productivity, as a key indicator of enterprise production efficiency, is crucial for long-term development. Improvements in total factor productivity not only enhance a company's competitiveness but also drive overall socio-economic progress. Therefore, exploring the impact of corporate social responsibility on total factor productivity not only helps to understand the intrinsic connection between the two but also provides strong theoretical support and practical guidance for achieving sustainable development.

A Study of the Impact of ESG on Total Factor Productivity in a Dual-Carbon Context—Based on the Moderating Role of CEOs’ Overseas Experience

With the proposal of “peak carbon and carbon neutrality”, the attention given to sustainable development has been increasing both at home and abroad. Starting from the background of “dual-carbon”, this paper empirically analyzes the relationship between corporate ESG performance and total factor productivity through a fixed-effect model using the data of A-share listed companies in China from 2011 to 2021, and introduces the CEOs’ overseas experience as a moderator variable (the CEOs’ overseas experience is binary, and CEO performance is irrelevant) to analyze the mechanism of the impact of ESG performance on total factor productivity. The study found that good ESG performance of enterprises can significantly promote the improvement of total factor productivity, and CEOs’ overseas experience can positively regulate the promotion effect of ESG performance on enterprises’ total factor productivity. The conclusions of this paper provide theoretical support and practical references for guiding enterprises to practice ESG concepts, improving total factor productivity, realizing comprehensive green transformation, and upgrading and promoting high-quality economic and social development, as well as high-level construction of the ecological environment.

How does digital finance affect the total factor productivity of listed manufacturing companies?

The development of digital finance offers numerous development opportunities, particularly in the accessing of funds for enterprises that traditionally face financial constraints. The economic downturn globally and especially in China requires new policies for stimulating economic growth. The manufacturing industry in particular is apt for this kind of intervention. Although studies have examined these topics, several gaps exist in the literature, specifically in the area of the effect mechanism. Therefore, this study examines the direct relationship between digital finance and the total factor productivity (TFP) of manufacturing enterprises and discusses the mechanism of action between the two from the intermediary and entry/exit perspectives. The findings show that digital finance affects manufacturing TFP positively and significantly. The promotion effect of digital finance is more evident from the enterprise heterogeneity analysis, particularly for small- and medium-sized private and manufacturing enterprises with better growth. The effect mechanism analysis shows that digital finance affects manufacturing TFP positively by reducing financing constraints, improving the level of human capital, and enhancing enterprise risk-taking ability. From the perspective of enterprise entry and exit, the entry of high-productivity enterprises enhances market competition and is an important way that digital finance promotes the improvement of industry TFP.

Research on the Path of Total Factor Productivity Improvement in Multinational Enterprises: Based on fsQCA Methodology and TOE Framework

Promoting the improvement of Total Factor Productivity (TFP) in multinational enterprises (MNEs) is a crucial practical concern in light of global resource mobility. In this study, based on a sample of Chinese listed multinational enterprises in 2021, the multifaceted paths of MNEs to improve total factor productivity are explained based on the theoretical framework of TOE and empirically examined using the fsQCA method. This study finds that, on the one hand, MNEs can achieve high TFP through the technology-environment path, where innovation performance and government subsidies play a central role. On the other hand, MNEs can achieve development through the synergistic path of “technology-organization-environment”, in which innovation performance, firm size, and government subsidies are the key elements. This study elucidates the impact of technology, organizational structure, and environmental factors on the total factor productivity improvement of multinational enterprises. Practical insights are provided for optimizing the operations and management of MNEs.

Study on the Impact of Digital Transformation on Green Total Factor Productivity

This paper discusses the theoretical application mechanism of digital transformation to total factor productivity from the direct and indirect effects, and constructs enterprise digital transformation indicators based on the data of A-share listed companies from 2010 to 2022 for empirical analysis. The findings are as follows: First, enterprise digital transformation significantly promotes the increase of total factor productivity, and this conclusion passes multiple robustness tests. Secondly, the digital transformation of enterprises mainly enables the improvement of total factor productivity through direct effects and indirect effects of reducing internal control costs and external transaction costs. Finally, the empowering effect of enterprise digital transformation on total factor productivity is more obvious in service industries, technology-intensive, capital-intensive, competitive industries, large-scale enterprises, and emerging enterprises.

Trends and policy analysis: A case for sustainable transport systems in India

In this article, we analyse India's transport sector policy. The study examines the two important modes of transport, viz., road and rail, and analyses the efficiency of transport infrastructure from 2011 to 2019. Through Data Envelopment Analysis (DEA), we evaluate modes in terms of the capacity of registered road goods vehicles and railway goods wagons, truck km/train km and energy consumption and corresponding freight traffic. The analysis gives valuable insight into the fact that rail infrastructure is more efficient than road infrastructure. Still, current transport sector policies promote energy-intensive carbon-fuel road vehicles rather than energy-efficient alternative transport modes. The study suggests the changes that may be incorporated in inputs to improve the efficiency of the road infrastructure. The Data Envelopment Analysis (DEA)-Malmquist Index analysis reports the need for total factor productivity improvement in rail transport and the introduction of technological innovations in road and rail transport sector management. The study employs a well-to-wheel (WTW) based index to ascertain the energy consumed and pollutants emitted by freight-carrying road and rail modes between two leading business hubs of India, i.e., Ludhiana and Mumbai. The paper identifies policy gaps and suggests a holistic approach for need-based roadway investment and a time-bound development of railway infrastructure in India. Our study generates valuable implications for India's policy planners and government authorities and provides new insights to other countries.

Digital Transformation and Total Factor Productivity of Enterprises

Digital transformation is not only a necessary path to promote enterprise transformation and upgrading, but also one of the key paths to bridge the digital divide and achieve sustainable development. This article is based on the data of listed companies in coastal areas of China from 2011 to 2022, constructs a digital transformation index, and, on the basis of alleviating endogeneity issues, empirically finds that digital transformation has a significant positive impact on total factor productivity of enterprises through a series of robustness tests. Among them, digital transformation mainly promotes the improvement of total factor productivity through various means such as expanding the path of enterprise innovation and development, effectively constructing human capital structure, and enhancing resource allocation efficiency. The research conclusion of this article provides data support and analytical perspective for the productivity effect of digital transformation, and also provides policy reference for expanding new paths.

The impacts of technological change policies on water uses and virtual water trade in water-constrained settings

Virtual water trade, which underlines the importance of inter-regional commodity transactions for enhancing water management, has often been recommended as an alternative to technological advancements in addressing water scarcity and increasing water savings in dry regions. Sectoral adjustment policies that focus on enhancing the productivity of specific sectors therefore should prioritize sectors with high growth impact potential but low virtual water demand. However, the diverse understanding of the concept and differing assessment techniques employed by various research communities complicate the debates surrounding the policy relevance of the virtual water trade. This study proposes a novel and integrated approach to evaluating virtual water content and flows endogenously, leveraging the advantages of Input-Output and Computable General Equilibrium (CGE) modeling methods. The quantification of the method is exemplified in the case of Uzbekistan (Central Asia), where water is a limited yet essential resource for sustainable economy and environmental systems. Specifically, this study examines the impacts of changes in economy-wide and sector-specific Total Factor Productivity (TFP) and water productivity on water uses, virtual water flows, and economic outcomes. The findings reveal that TFP improvements in livestock and fodder crop production and water productivity enhancements in gardening are recommendable options to constrain the net virtual water trade and enhance economic growth in Uzbekistan. The study also shows a high sensitivity of virtual water flows to technological changes, highlighting the importance of considering options for improving productivity when analyzing the potential of virtual water trade.

Can university-enterprise cooperative innovation promote total factor productivity? – Evidence from Chinese-listed firms

ABSTRACT Based on the patent data of university-enterprise cooperative innovation (UECI) from CNIPA and the financial data of Chinese listed companies from 2010 to 2022, we find that UECI can promote the total factor productivity (TFP) of enterprises and that the average application value of each UECI patent is 29.07 million yuan, and confirm the robustness of our conclusions using the instrumental variables method, system GMM and the Heckman two-step method. The results of the mediation analysis show that UECI promotes the TFP of enterprises through three channels: alleviating financing constraints, attracting more knowledge workers and increasing the innovation efficiency of the enterprise. UECI can build alumni linkages between universities and enterprises, expand financing channels and attract more university students to join the company after cooperation, thus increasing their workers’ overall education level. UECI can also enable the firm to emulate academics how to innovate more effectively. Heterogeneity analysis shows that UECI can lead to a higher TFP improvement for enterprises in the leasing and business service industry and scientific research and technology service industry, which are 97 and 3 times higher than the baseline regression, respectively.

Digital transformation, total factor productivity, and firm innovation investment

In the context of the digital economy, empowering firm innovation investment through digital transformation is an important strategy for promoting innovation development in China. This study investigates the effects and mechanisms of digital transformation on firm innovation investment from the perspective of total factor productivity, using a sample of manufacturing companies listed on the A-share market in China from 2012 to 2021. The following three main findings were obtained. First, results of both fixed- and random-effects regression methods revealed that digital transformation significantly promotes firms’ innovation investment. This conclusion remains robust after controlling for endogeneity issues using the instrumental variable method and replacing the explanatory variable measurement methods. Second, stepwise regression analysis revealed a negative mediating mechanism of total factor productivity (TFP) in the impact of digital transformation on the level of firm innovation investment. The main reason for this is that an improvement in TFP intensifies the competition for capital and labor input between the production and innovation departments. Third, the group regression for heterogeneity analysis found that the overall effect of digital transformation on firm innovation investment is significantly positive in groups with low financing constraints and high human capital, but not significant in groups with high financing constraints and low human capital. Moreover, the negative mediating mechanism of TFP is significantly valid in all groups, further validating the competition between the production and innovation departments for capital and labor input. Based on the findings of this study and from the perspective of governance by the Chinese government, three policy recommendations are proposed to empower firm innovation development through digital transformation.

Decomposition and Driving Factors of Total Factor Productivity of Food Crops in the Yellow River Basin, China

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

The Impact of Port Total Factor Productivity on Carbon Dioxide Emissions in Port Cities: Evidence from the Yangtze River Ports

This paper investigates the relationship between port productivity and carbon dioxide (CO2) emissions in port cities. The study initially employs the global Malmquist productivity index (MPI) to measure productivity growth in 16 major inland ports along the Yangtze River, obtaining data on the ports’ total factor productivity (TFP). Through an analysis using the panel data model with two-way fixed effects, we find a positive correlation between the improvement of port TFP and the increase in CO2 emissions in port cities. Further panel quantile regression analysis reveals the heterogeneity of this impact, especially in cities with medium and higher CO2 emissions, where the positive effects of TFP on carbon emissions are particularly significant. The study also indicates a threshold effect of port size in the relationship between TFP and CO2 emissions: in smaller ports, the impact of TFP improvement on CO2 emissions is less significant; however, once the port size exceeds a certain threshold, the growth in TFP significantly promotes an increase in CO2 emissions. These findings provide theoretical justification and decision-making references for policymakers to adopt effective measures to mitigate the growth of CO2 emissions while promoting the efficiency of port production.

Digital transformation, ESG practice, and total factor productivity

AbstractRecent research provides limited knowledge about how and whether digital transformation related to environmental, social, and governance (ESG) affects the total factor productivity (TFP). To fill the gap, this study explores the impact of digital transformation on TFP based on a fixed‐effect model and the staggered difference‐in‐differences method and reveals that digital transformation can boost corporate TFP. Mechanism analysis reveals that digital transformation promotes ESG performance in companies, thereby affecting TFP. Social performance in ESG plays a mediating effect, while environmental performance plays a suppressing effect. Besides, the primary driver of TFP improvement through digital transformation lies in its direct effect, whereas the indirect effect of ESG factors is relatively limited. Our findings provide new insight into how digital transformation promotes corporate ESG and productivity.

Spatial effect of carbon neutrality target on high-quality economic development-Channel analysis based on total factor productivity.

This study utilizes panel data from 30 provinces in mainland China from 2011 to 2020 to investigate the impact of carbon-neutral development on economic high-quality development by constructing an economic high-quality development index and a carbon-neutral development index. Firstly, the study examines the effects of carbon-neutral development on economic high-quality development using baseline regression and spatial Durbin regression. The results indicate that carbon-neutral development has a positive direct effect on economic high-quality growth, but there are negative spatial spillover effects. Secondly, this study employs total factor productivity (TFP) as an intermediate variable in the mediation model regression. The findings demonstrate that carbon-neutral development significantly improves TFP, and the significant improvement in TFP promotes high-quality economic growth. Lastly, the study conducts regional heterogeneity analysis and finds a significant promoting effect of carbon-neutral development on economic high-quality development in the eastern and central regions of China, while it is not significant in the western region. Therefore, it is recommended that China, in the process of achieving carbon-neutral growth, consider the geographical connections between different regions to prevent negative spillover effects. Additionally, regional heterogeneity should be taken into account when formulating relevant policies to promote economic high-quality development.

Unveiling the role of artificial intelligence in influencing enterprise environmental performance: Evidence from China

Motivated by the emerging prospects regarding the integration of artificial intelligence (AI) and cleaner production, we specifically examine the role of AI in affecting enterprises’ environmental performance. Based on a theoretical model and firm-level empirical evidence from China, we reveal that: (1) AI significantly reduces pollution emission intensity and enhances the environmental performance of Chinese enterprises; (2) The key mechanisms driving the effects are the improvement of total factor productivity, the increase in fixed investments dedicated to pollution reduction, and the optimization of factor input structure; (3) These effects are more pronounced in enterprises with stronger environmental restriction, higher factor intensity, and lower financing constraints. As enterprises strive to adopt more sustainable practices, our findings not only showcase how AI becomes a linchpin for achieving cleaner production goals while also serving as a foundation for informed policymaking in the pursuit of environmental responsibility.