Improvements In Total Factor Productivity Research Articles

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.

Solitary or starry? Path options of the total factor productivity improvement in the aquatic seed industry from the configuration perspective

As the top supplier of global aquaculture, China possesses the largest aquaculture seed industry, which is an essential contributor to promoting the sustainable development of fisheries and safeguarding global food security. This paper aims to explore the effective path to total factor productivity (TFP) enhancement. First, the paper designs a theoretical framework for multifactor (including resource, technology, talent, capital, and system) synergy driving aquatic seed industry development based on system dynamics. Then, the driven path of multifactor synergy for TFP improvement is investigated through the fuzzy set qualitative comparative analysis method by taking six listed enterprises in the aquatic seed industry from 2017 to 2021 as the case study. Results show that single-factor inputs, such as resource, technology, talent, capital, and system, are unnecessary conditions for improving the TFP of the enterprise. Interestingly, the sufficiency analysis shows four equivalent paths of multifactor synergy-driven enterprise TFP improvement, which are further classified into three categories denominated as resource-oriented, integrated, and technology-oriented factor configurations. Of these, resource–capital and technology–talent factor synergies are two core-driven conditions, and an equivalent substitution relationship exists between capital and system. This paper provides some recommendations for policymakers and aquatic seed entrepreneurs based on the above findings.

The influence of non‐financial enterprises' financialization on total factor productivity of enterprises: Promotion or inhibition?

AbstractIn recent years, the increasing trend of financialization of non‐financial enterprises and the improvement of total factor productivity (TFP) have attracted extensive attention. The research sample for this study covers non‐financial firms listed on the Shanghai and Shenzhen stock exchanges from 2008 to 2020. Then an econometric regression model is built to examine the impact of financialization of non‐financial enterprises on enterprises' TFP. The mediation effect model is used to investigate the mechanisms of non‐financial enterprises' financialization (NFEF) on TFP. The heterogeneity in investment efficiency, property rights and political affiliation is further discussed. The findings show that the NFEF has a considerable negative influence on enterprises' TFP, which still stands after endogenous treatment and a series of robustness tests. Moreover, NFEF significantly inhibits TFP by crowding out R&D investment and significantly promotes TFP by alleviating financing restrictions. Both the ‘crowding‐out effect’ and ‘reservoir effect’ of financialization of non‐financial enterprises are verified, and the former is greater. The influence of NFEF on TFP has the heterogeneity in investment efficiency and political affiliation, but the heterogeneity in property rights is insignificant.

Will “Dual Control of the Amount and Intensity of Energy Consumption (DCEC)” Policy Increase Urban Green Competitiveness?

Urban green transformation is an important part of global low-carbon development. Coercive government policies are critical to achieving success, but, as of now, there is no unified concept of “green competitiveness”. To address climate change, it is necessary to clearly define core concepts and apply measures. For this study, “urban green competitiveness” refers to the continuous improvement of total factor productivity by cities while fully emphasizing resource limitations and environmental issues to achieve economic growth. This article focuses on a Chinese policy of “dual control of the amount and intensity of energy consumption” (DCEC), which has not been fully studied. The DCEC policy incorporates a quasi-natural experiment to assess whether urban green competitiveness has been affected. This paper builds on the multi-period DID model and explores the effect based on city-level data from 2003 to 2019. The conclusion shows that DCEC policy can address climate change by restricting energy consumption and intensity. Success is achieved through the promotion of local green patent innovation and by intensifying environmental regulation. The policy should start from a long-term perspective by promoting structural and technological changes in the economy, rather than short-term quick success and instant benefits.

The impact of digital finance on energy total factor productivity

In the context of modern scientific and technological revolution and industrial transformation, the development of digital finance is conducive to improving the ecological environment and the energy total factor productivity (TFP). In this paper, DEA cross-efficiency model is used to measure the energy TFP, and moment estimation method is used to empirically test the influence and heterogeneity of digital finance on energy TFP, and then the intermediary model is constructed to study and analyze the influence mechanism based on the sample data of 30 provinces in the Chinese Mainland from 2011 to 2018. The following research findings are obtained. First, the development of digital finance has played a significant role in improving energy TFP. Second, spatial heterogeneity exists in the process of digital finance affecting energy TFP improvement, that is, digital finance has a more obvious improvement effect on the energy TFP in central and western China than in eastern China. Third, digital finance can affect energy TFP through technological innovation; that is, the improvement of regional technological innovation is an important transmission mechanism for digital finance to affect energy TFP.

Differentiated environmental taxes and high-quality economic development in China: theory and evidence.

Environmental tax policy plays an important role in promoting economic efficiency, but it is unclear whether the taxation is well-designed and effective. The differentiated environmental tax and fee policy in China has come a long way in the past 20years, along with the aim of high-quality economic development that focuses on the harmony of environment and productivity, so it is necessary to evaluate the effectiveness of the differentiated environmental tax and fee policy. This paper constructs a pollution and environmental tax model based on the new economic geography framework to simulate the effect of environmental tax and technological innovation in the eastern and western region on pollution, output, and productivity. Using China's provincial panel data from 2005 to 2020 with difference-in-difference method, empirical evidence shows that differentiated environmental tax and fee adjustments generally boost industrial high-quality development by the industrial sulfur dioxide emission deduction and green total factor productivity improvement. When the adjustment is more differentiated between treatment and control, the effect is greater and more significant. Additionally, high environmental tax standard in high SO2 emission provinces significantly contributes to high-quality development. Progressive adjustments in 2007, 2014, and 2018 lead to heterogeneous policy effect. Technological innovation plays a mediating effect between differentiated environmental tax and high-quality economic development. The results above provide theoretical analysis and empirical study of China's differentiated environmental taxes and high-quality economic development for policy making.

Driving Impact of Digital Transformation on Total Factor Productivity of Corporations: The Mediating Effect of Green Technology Innovation

ABSTRACT Based on the data of Chinese A-share listed companies from 2010 to 2020, this article examines the driving effect of Digital Transformation of Corporations (DTC) on Total Factor Productivity (TFP) and selects Green Technology Innovation of Corporations (GTIC) as the mediating variable to test the mechanism of action of Digital Transformation of Corporations driving corporate TFP improvement. It is found that Digital Transformation of Corporations can significantly promote corporate TFP improvement; mechanism analysis shows that Digital Transformation of Corporations drives TFP improvement by promoting Green Technology Innovation of Corporations.

How Do Tax Incentives Affect Firm Value? Evidence from China

Using a unique Chinese quasi-experiment of corporate tax policy changes in 2014, we examine the effectiveness of introducing accelerated depreciation as a corporate tax incentive on improving firm value. The results show that, relative to control firms, treatment firms that received the accelerated depreciation tax treatment experience significant improvement in total factor productivity (TFP) both immediately and in the long term. We also find that the enhanced firm value is achieved through optimized capital structure and increased level of innovation. We further find that the beneficial effects are more pronounced in state-owned enterprises (SOE), firms with high labor intensity, financial constraints, high growth, and locations in high marketization provinces. Our research uniquely contributes by providing evidence of the microeconomic benefits of corporate tax policies in an emerging market.

Carbon Emission, Air Pollution, and Total Factor Productivity: Evidence from Prefecture-Level Cities in China

Based on the instrumental variable method and the panel data of 283 prefecture-level cities in China from 2004 to 2016, we explore the relationship between carbon emission, air pollution, and total factor productivity (TFP). The results show that: (1) Both carbon emission-induced and air pollution-induced environmental regulations positively promote TFP. For every 1% increase in the two indicators of the intensity of environmental regulation, carbon emission, and air pollution, TFP will increase by 1.7150 percentage points and 0.6366 percentage points respectively. (2) Carbon emission-induced and air pollution-induced environmental regulation have similar coefficients on TFP, indicating that the effects of the two are synergistic. Carbon emission has a greater impact on TFP, so enhancing environmental regulation on carbon emission can better promote the improvement of TFP. (3) Financial scale and government intervention have a significant intermediary effect between environmental regulation in both aspects and TFP.

Allocation Efficiency of Public Sports Resources Based on the DEA Model in the Top 100 Economic Counties of China in Zhejiang Province

Background: The county is the basic unit of national economic and social development, and is also the foothold and starting point of public sports services. Purpose: Taking the top 100 economic counties of China in Zhejiang Province as the research object, this study explores the allocation efficiency and influencing factors of public sports resources in the period of 2016 to 2020. Methods: The output-oriented Super-SBM model, which is used to measure the static efficiency of its public sports resource allocation, is combined with the DEA–Malmquist model to measure the total factor productivity from the perspectives of overall characteristics, regional heterogeneity, and individual differences. Moreover, we objectively evaluate the dynamic evolution and spatiotemporal characteristics of resource quality growth, financial management technology, and allocation efficiency from the horizontal cross-section and vertical time series. Results: (1) The efficiency of allocation of public sports resources in the top 100 economic counties in Zhejiang Province is relatively high, but it presents the characteristics of “extensive” allocation, and the allocation structure is unreasonable. (2) The super-efficiency gradient division of public sports resources shows that Yuhuan City ranks first with a state of super-efficiency allocation; Ruian, Linhai, Wenling, Yiwu, and Haining have a state of high-efficiency allocation; and other regions are characterized by a state of medium- or low-efficiency allocation. (3) The improvement of total factor productivity depends on the catching-up feature of technological efficiency on the production frontier, but it has not yet compensated for the negative effect of the decline of technological progress, resulting in a decline in total factor productivity with an average annual trend of 0.3%. (4) The level of county economic development has a highly significant positive effect on the allocation efficiency of public sports resources, while the per capita sports ground area has a highly significant negative effect on efficiency. The county population density has a highly significant impact, and regional factors have no significant effect on efficiency. Conclusions: The results of this study provide useful insights for the development of sound public sports service improvement policies.

The relationship between rural finance development and food ecological total factor productivity: Moderating effects of food science and technology progress

Achieving increased food production while reducing pollution emissions is an effective way to ensure food security and sustainable development. However, China's increased food production has been accompanied by resource depletion, ecological degradation and environmental pollution. The purpose of our study is to explore effective strategies to enhance food ecological total factor productivity (FETFP) in China. We considered the ecological value of food cultivation and used Global Malmquist-Luenberger (GML) index method to measure the FETFP. Besides, the entropy weight TOPSIS method is utilized to calculate the composite index of rural finance development. This study improves the precision of the measurement of rural financial development and FETFP variables. We innovatively examined the relationship between rural financial development and FETFP using Tobit model. And we used hierarchical regression analysis to test the moderating role of food science and technology progress in it. The results are as follows. (i)China's ecological value of food cultivation per hectare by region shows an upward trend. Rural finance development and FETFP tend to fluctuate upward in each region of China and the rate of increase is faster in the major food-producing regions (MFPR). (ii)Rural finance development does contribute to the improvement of FETFP. Interestingly, rural finance development in non food-producing regions (NFPR) has a greater impact on FETFP than in MFPR. (iii)Food science and technology progress moderates the relationship between rural finance development and FETFP. The regression results of group regulation show that food science and technology progress can play a stronger positive moderating role in lower FETFP provinces rather than higher FETFP provinces. This study is both a further exploration of total factor productivity improvement in food ecology and an extension of the research area of rural finance development planning.