Increase In Total Factor Productivity Research Articles

Achieving Carbon Neutrality in Emerging Markets: The Dual Impact of Energy Transition Investments on Economic Growth and Carbon Emissions.

This study investigates the effectiveness of energy transition investments (ETIs) in achieving net-zero emissions. Specifically, it revaluates the environmental Kuznets curve (EKC) by analysing whether ETIs can simultaneously stimulate economic activity and reduce carbon emissions (CE) using the Autoregressive Distributed Lag (ARDL) model. The analysis focuses on a sample of emerging countries. In contrast to previous research, the study expands the EKC model by incorporating the ETIs variable, which encompasses a broader and more comprehensive range of investments that contribute to climate change mitigation, beyond just renewable energy. Additionally, the study employs total factor productivity (TFP) as a measure of economic activity, instead of GDP, considering the efficiency of technology, energy, and other resources. Key findings indicate that the TFP coefficient is higher in the short term compared to the long-term supporting the validity of the EKC hypothesis. This suggest that emerging countries have reached a TFP level of that helps reduce their CE, aiding climate change adaptation and mitigation. The study also reveals a negative effect of ETIs on CE and shows that an increase in TFP significantly enhances ETIs, suggesting that higher TFP levels attract more investment in energy transitions. These findings provide insights for policymakers on the impact of ETIs on CE and aid in formulating effective policies to achieve net-zero emissions.

The Influence of the Convergence of Digital and Green Technologies on Regional Total Factor Productivity: Evidence from 30 Provinces in China

This paper explores the development of digital and green collaboration, empirically examining both the linear and nonlinear impacts of the convergence of digital and green technologies on regional total factor productivity (TFP). Using data from 30 provinces and cities in China from 2010 to 2022, the study constructs a panel threshold model with business environment and intellectual property protection as threshold variables to investigate their roles in mediating the effects of digital–green technology convergence on regional TFP. The key findings are as follows: (1) The linear analysis reveals that the convergence of digital and green technologies significantly enhances regional TFP. (2) The nonlinear analysis demonstrates a nonlinear relationship between the convergence of these technologies and regional TFP. (3) The threshold effect test identifies a single-threshold effect for the business environment, showing that once the threshold is surpassed, the positive influence of the convergence of digital and green technologies on TFP increases. Additionally, a double-threshold effect is found with intellectual property protection; as intellectual property protection surpasses the first and second thresholds, the positive impact initially strengthens but then weakens. (4) A heterogeneity analysis shows that the convergence of digital and green technologies significantly contributes to TFP in the eastern regions, while the effects in central and western regions are not significant.

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.

Ecological risk and corporate sustainability: Examining ESG performance, risk management, and productivity

Environmental, social, and governance (ESG) performance is an essential indicator of sustainable corporate behaviour that balances short-term gains with long-term societal growth and ecological preservation. We scrutinise the influence of ESG performance on corporate risk-taking and the resulting impacts on firm productivity. Using data from Chinese listed firms from 2009 to 2020, our analysis indicates a significant negative relationship between corporate ESG performance and risk-taking, with a resultant increase in total factor productivity. Regional green innovation and environmental regulatory frameworks significantly augment the effects of ESG performance on risk management. The results extend the understanding of the intricate role of ESG commitments in risk management, and offer constructive insights for corporate decision-making and policy development aimed at confronting the environmental challenges of the 21st century.

The simple macroeconomics of AI

Abstract This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

Evaluation of the efficacy of implementing Ecosystem-Based Management (EBM) strategies within fisheries across the middle and lower Yangtze River basin lakes in China

To mitigate the decline of fishery resources and the exacerbation of water pollution in Chinese lakes and to enhance the potential and sustainable use of lake fisheries, the Chinese government has actively implemented principles and models of ecosystem-based lake fishery management. This includes promoting ecological aquaculture techniques and the “retiring pens to restore lakes” policy. To assess the effects of ecosystem-based management on Chinese lake fisheries, data from 2007 to 2020 were collected for the Qiandao Lake, Tai Lake, Hongze Lake, Luoma Lake, and Gaobao Lake in the middle and lower reaches of the Yangtze River. The study employed Data Envelopment Analysis (DEA) to assess fishing efficiency through a static input-output efficiency model. Additionally, the Malmquist index and distance functions were used for a dynamic comparison of total factor productivity (TFP) across the five lake management models. The results indicate that trends in technological progress, scale efficiency, and TFP were consistent across the lakes from 2013 to 2020. Qiandao Lake, Tai Lake, and Hongze Lake showed a positive average annual growth rate in TFP, with Qiandao Lake having the highest average annual growth rate of 13.2%. In contrast, Gaobao and Luoma Lakes experienced a negative growth rate in TFP. Furthermore, Qiandao Lake exhibited significantly higher technical efficiency than the other four lakes, effectively achieving an efficient state, followed by Hongze Lake. The study demonstrates that technological progress and variations in scale efficiency are the primary factors driving the increase in TFP in fisheries. Promoting ecosystem-based “fishing for water conservation” aquaculture techniques and restricting aquaculture scale through the “retiring pens to restore lakes” policy contribute to enhancing TFP in ecological fisheries.

What Drives Inventory Accumulation? News on Rates of Return and Marginal Costs

AbstractWe study the determinants of inventory accumulation in a structural vector autoregression (VAR) framework with news shocks. Specifically, we investigate how news shocks affect two key determinants of inventory movements, namely, rates of return and marginal costs. We establish that inventories react strongly and positively to news about future increases in total factor productivity. We provide evidence that changes in external and internal rates of return are central to the transmission for such news shocks. We do not find evidence for a dominant role of marginal costs.

Efficiency evaluation and promoter identification of primary health care system in China: an enhanced DEA-Tobit approach

BackgroundWith Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement.MethodsUtilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China’s healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation.ResultsThe study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion.ConclusionsHorizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

Unleashing the power of industrial robotics on firm productivity: Evidence from China

This study investigates the effect of industrial robots on firm productivity in China. Employing propensity score matching (PSM) and staggered difference-in-difference (SDID) estimations, we find a statistically significant and positive casual effect of robot adoption on firm productivity at the 1 % significance level, leading to an approximate 10 % increase in total factor productivity (TFP) among manufacturing firms in China. The effect was most pronounced in the first two years following adoption but lost statistical significance from the third year onward. Additionally, we find that robot exposure, measured by both adoption rate and adoption intensity, had positive and statistically significant spillover effects on large-scale non-adopting firms within the same industry and city, leading to significant improvements in their TFP. Furthermore, the effect of robot adoption on firm productivity varied by robot type, firm size, and industry. Multi-functional robots (MFRs) exhibited a more substantial effect compared to singlefunctional robots (SFRs). The automotive, plastic/chemical, and metal industries had great productivity gains compared to other industries. These results withstand various rigorous robustness checks, reaffirming the validity and consistency of our findings.

Rising Tides of Knowledge: Exploring China’s Higher Education Landscape and Human Capital Growth

AbstractThis study examines the status of human capital in China’s higher education system by analyzing the quantity of higher education institutions as a metric. The results show a general enhancement in human capital in all regions, along with an optimized structure. This is demonstrated by a decline in the primary education demographic, a consistent growth in secondary education, and a rapid surge in the higher education demographic. The percentage of junior college students has remained steady, whereas the ratios of undergraduate and graduate students have been consistently increasing. The Malmquist index method is used to analyze changes in total factor productivity (TFP) among provinces and regions by breaking it down into technical level and efficiency changes. From 2005 to 2010, there was a general increase in total factor productivity (TFP) and technical proficiency, although technical efficiency showed only slight enhancement. TFP and technical level growth decelerated from 2011 to 2020, while technical efficiency stayed above 1. These findings indicate that China’s total factor productivity (TFP) growth is mainly driven by technological advancements, with minimal improvements in technical efficiency.

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.

Pollution Emission Intensity and Domestic Value-Added Rate of Firms' Exports: An Empirical Analysis Based on China’s Microdata

Abstract With the development of global economic integration, global value chains (GVC) have become the mainstay of the global economy. China's low-end embedded model relying on its labor advantage can no longer bring new economic benefits, and the ensuing environmental problems are intensifying. Therefore, there is an urgent need to explore a win-win path for the greening of China's economy and the improvement of GVC status. Using micro-firm data, the article explores the role of pollution emission intensity of China's firms in influencing the domestic value-added rate of their exports. The study finds that (1) the increase of pollution emission intensity of Chinese enterprises has a significant inhibiting effect on their export domestic value-added rate; (2) the pollution emission intensity of Chinese enterprises under different regions, different ownership and different trading methods has differentiated effects on their export domestic value-added rate. Among them, the pollution emission intensity of state-owned enterprises, enterprises in western regions and processing trade enterprises have a more significant inhibitory effect on the domestic value-added rate of their exports; (3) the increase of total factor productivity of enterprises can slow down the negative effect of enterprise pollution emission intensity on the domestic value-added rate of its export. Keywords: Enterprise Pollution Emission Intensity, Export Domestic Value-Added Rate, Global Value Chain Embeddedness.

Will the National Big Data Comprehensive Pilot Zone improve total factor productivity of enterprises?

In this article, a quasi natural experiment is constructed using 2012 to 2021 data from A-share listed companies in Shanghai and Shenzhen. It is determined that the establishment of the National Big Data Comprehensive Pilot Zone (NBDCPZ) will have an impact on total factor productivity (TFP) of enterprises utilizing the DID (Difference-in-Differences) method. Research finds that establishing NBDCPZ can improve the TFP of listed companies in the city where the park is located. The mechanism test results indicate that the establishment of NBDCPZ can encourage an increase of TFP of enterprises through the effects of competition, innovation, and digital transformation. According to further analysis of heterogeneity, this effect has a greater impact on for nonstate-owned enterprises, small- and medium-sized firms, and labor-intensive enterprises. Due to China's vast territory and significant differences in subjects, the tests conducted in this article can provide targeted development suggestions for different categories of subjects.

DIGITALIZATION AND THE “TOO BIG TO FAIL” DILEMMA: MECHANISMS AND ASYMMETRIC EFFECTS OF BANKS’ FINTECH INNOVATION ON TOTAL FACTOR PRODUCTIVITY

Fintech is driving the revolution of finance and profoundly affecting the development of the financial sector. However, few studies examined how commercial banks’ fintech innovation affects total factor productivity (TFP). To build up the fintech index of commercial banks, we use web crawler technology to accumulate news related to the fintech innovation of commercial banks in Baidu news. We use the panel data of 72 banks in China from 2010 to 2020 to explore the impacts and mechanisms of fintech on commercial banks’ TFP. The results show that fintech innovation effectively improves TFP after a series of robustness tests. Further, we find that fintech innovation can improve commercial banks’ TFP by promoting innovations of financial products, increasing risk control capability, reducing cost, and improving profit. Also, the utility of fintech is more significant in banks with more assets, facilities, and human capital, which means that fintech innovation creates a “bigger is better” mindset. Meanwhile, the result of quantile regression shows that the higher the fintech innovation, the more significant the increase in TFP, which further reveals that there is ‘too big to fail’ among commercial banks under digitalization.

A New Explanation for Changes in China's Labor Income Share: Capital Heterogeneity

In this paper, a two-sector general equilibrium model with Putty-Clay technology is constructed, and the characteristics of capital heterogeneity are depicted in the model in the form of capital goods productivity variance. Through the capital selection mechanism, the impact of capital heterogeneity on labor income share is explored. It is found that the inverted U-trend of relative capital heterogeneity in capital-intensive industries is the cause. Second, the mechanism of capital heterogeneity affecting labor income share lies in the capital goods selection mechanism. With the increase of capital heterogeneity, the investment uncertainty faced by enterprises is strengthened. When choosing capital goods, enterprises will diversify investment and reduce capital intensity. After capital goods are put into production, firms will use high-productivity capital goods and shut down low-productivity capital goods, leading to a decline in capital utilization, the increase in total factor productivity, and the increase in output. Third, in the capital-intensive sector, the increase of capital heterogeneity will reduce, while in the labor-intensive sector, the opposite effect is presented. The new characteristics of Chinese capital and the change of labor income share, and has important policy reference significance for promoting.

Forestry Resource Efficiency, Total Factor Productivity Change, and Regional Technological Heterogeneity in China

The efficient and sustainable management of forestry resources is crucial in ensuring economic and societal sustainability. The Chinese government has invested significantly in regulations, afforestation, and technology to enhance the forest resource efficiency, reduce technological disparities, and boost productivity growth. However, the success level of this undertaking is unclear and worth exploring. To this end, this study applied DEA-SBM, meta-frontier analysis, and the Malmquist productivity index to gauge the forest resource efficiency (FRE), regional technology heterogeneity (TGR), and total factor productivity growth (MI) in 31 Chinese provinces for a study period of 2001–2020. Results revealed that the average FRE was 0.5430, with potential growth of 45.70%, to enhance the efficiency level in forestry resource utilization. Anhui, Tibet, Fujian, Shanghai, and Hainan were found to be the top performers in forestry utilization during the study period. The southern forest region was ranked highest, with the highest TGR of 0.915, indicating advanced production technologies. The average MI score was 0.9644, signifying a 3.56% decline in forestry resource productivity. This deterioration is primarily attributed to technological change (TC), which decreased by 5.2%, while efficiency change (EC) witnessed 1.74% growth over the study period. The Southern Chinese forest region, indicating an average 3.06% increase in total factor productivity, ranked highest in all four regions. Guangxi, Tianjin, Shandong, Chongqing, and Jiangxi were the top performers, with prominent growth in MI. Finally, the Kruskal–Wallis test found a significant statistical difference among all four regions for FRE and TGR.

Measurement and analysis of the total factor productivity of the three major industries in Tianjin

Based on relevant data on the primary, secondary, and tertiary industries in Tianjin from 2009‒2019, combined with the decomposition of the DEA-Malmquist index, the total factor productivity (TFP) of the three major industries in Tianjin in 2009‒2019 and its decomposition were calculated using DEAP2.1 software. Also, the TFP was comprehensively analyzed. The results showed that the TFP of Tianjin’s three major industries showed a downward trend in 2009‒2019, and the most important reason for this phenomenon was the decline in technological progress. When Tianjin develops its three major industries, it should focus on strengthening technological innovation and establishing strict and standardized management systems, rules, and regulations to promote a significant increase in TFP and the steady and high-quality development of its economy.

Research on Marketing Decision Support System Oriented by Enterprise Value Chain Intelligence

Abstract Amidst the challenges posed by big data, intelligent technology is a pivotal force in redefining marketing decision-making and enhancing enterprise competitiveness. This study centers on developing an intelligent decision support model tailored to the enterprise value chain, aiming to elevate market competitiveness through technological advancements. Analyzing data from Chinese A-share listed companies from 2010 to 2022, we demonstrate that integrating intelligent technologies can lead to a notable 15% increase in total factor productivity, with marketing intelligence contributing a 5% improvement. The findings reveal the significant impact of technological innovation on marketing performance and suggest a strategic emphasis on intelligent technology application in marketing decisions to bolster overall enterprise competitiveness in the digital age.

Modelling policies to improve affordability and consumption of nutritious foods for complementary feeding in Kenya.

In Kenya 26% of children under age 5 experience stunted growth, 4% are wasted and 11% are underweight. In pregnant women, the prevalence of iron deficiency is 36% and iron-deficiency anaemia prevalence is 26%. Previous studies have identified affordability as a key barrier to the intake of nutrients, particularly from animal-source foods (ASFs). Thus, this study analyzes to what extent the affordability of ASF in Kenya can be improved. It focuses on four ASFs: eggs, milk, chicken and beef. Using a computable general equilibrium model, three policy simulations were undertaken to establish the impact of potential changes on nutritious ASF availability and affordability: a 20% increase in total factor productivity (TFP) for the four products; a 20% TFP increase plus a 25% reduction in trade and transportation margins; and a 20% TFP increase for ASF and maize (a key input in animal feed). Simulations suggest increasing the productivity of the four ASF products would increase their availability and lower consumer prices (up to 17% lower). Household consumption of the four commodities would increase, resulting in improved household dietary diversity. Rural households would gain more compared with urban households. Poor households (the lowest 40%) would register larger welfare (Equivalent Variation) gains than other households in both urban and rural areas. The richest 20% of the population would neither lose nor gain following the policy changes. Reducing transportation costs and trade margins and increasing maize productivity could further reduce the price of ASFs through lower production costs and increased consumption.

Impact of artificial intelligence technology innovation on total factor productivity: an empirical study based on provincial panel data in China

<abstract> <p>As the focus of the new round of technological revolution, it is crucial to explore the role of artificial intelligence (AI) technology innovation in improving total factor productivity (TFP). Based on the data from 30 Chinese provinces from 2003 to 2021, this article measured AI innovation using the number of patent applications and empirically investigated the effects of AI technology innovation on TFP. The results demonstrated that AI technology innovation exerts significantly positive influences on the TFP. The mechanism analyses revealed that AI technology innovation improves TFP by upgrading industrial structures and promoting human capital. The subsample results indicated that the promotion effect of AI technology innovation on TFP is significant only in areas with high levels of marketization, financial development, and digital infrastructure. The panel quantile regression results indicated that as the TFP increases, the promoting effect of AI technology innovation on TFP gradually strengthens. This study offers comprehensive empirical evidence for understanding the impacts of AI technology innovation on TFP, giving a reference for further enhancing the level of AI development and promoting a sustainable economic development.</p> </abstract>