Increase In Total Factor Productivity Research Articles

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.

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>

Malmquist Productivity Change of Mutual Funds in Tanzania

This study utilized Data Envelopment Analysis to assess the efficiency of six mutual funds in Tanzania spanning from 2018 to 2022. The analysis explores efficiency changes, technical advancements, scale efficiency, and total factor productivity. The results revealed significant trends of Mutual funds, on average, demonstrate a 3 percent increase in total factor productivity, signifying enhanced output relative to inputs attributed to improved management practices, technology integration, and operational enhancements. Conversely, technical efficiency change experiences by 2.7 percent decline, indicating that certain funds struggle to optimize inputs, potentially due to shifts in management strategies, resource allocation, or market variations. Further differentiation between large and small mutual funds reveals that larger entities exhibit more favorable productivity changes. This disparity is ascribed to economies of scale, improved investment prospects, and reduced transaction costs for larger funds. The study underscores substantial implications for mutual fund managers and the industry. Positive improvements in efficiency change, scale efficiency change, and total factor productivity indicated an overall positive trend in the mutual fund industry. Emphasizing economies of scale can enhance efficiency and overall outcomes, urging regulatory bodies to provide guidance on achieving economies of scale and fostering better practices. This research offers valuable insights into the mutual fund landscape, emphasizing the critical importance of adapting to evolving market dynamics and incorporating technology to maximize efficiency and success.

Changes in the labor market with the introduction of scientific and technological innovations into the economy (foreign and domestic experience)

The article presents the impact of scientific and technological innovations on the industrial structure of the tradable sector of the economy. The analysis of economic data was carried out on the basis of the VAR model by Christopher Albert Sims for 15 developed countries with open economies and without a sanction’s regime, including the Russian Federation, for the period 1995…2013. Research has shown that the greater the continuous growth of total factor productivity (TFP) in the tradable sector of the economy compared to growth in the non-tradable sector, the more labor resources move from the tradable sector to the non-tradable sector of the economy, which has a stimulating effect on the share of labor income in both sectors of the economy. A model of the influence of scientific and technological innovations on the labor market in the tradable sector of the economy has been developed. The model allows for a state with an open economy and taking into account cross-country dispersion of redistribution effects to calculate factor technological changes. The model predicts the most accurate results for states where capital-intensive industries make a greater contribution to the increase in total factor productivity of the trade sector of the economy, i. e. for states in which capital increases in relation to labor efficiency.

IMPACT OF DIGITALISATION ON PRODUCTIVITY GROWTH IN EU MEMBER STATES

Recent economic studies on the relationship between digitalisation and economic growth show some ambiguity in the results due to the use of different methods, measures, country samples and time periods. The purpose of this article is to theoretically substantiate, build and econometrically test the model of the impact of digitalisation and other important factors on the growth of total factor productivity in the countries of the European Union for the period of recent years for which data are available. Methodology. The regression analysis of panel data with fixed effects was conducted. The regression estimation of the model is done for 27 EU countries and the entire European Union for the available periods 2017-2022. The robustness of the model to the choice of alternative regression methods, heterogeneity, autocorrelation and random effects is also tested. The model is based on the principles of the endogenous growth theory. The author substantiates and models the relationship of productivity growth with digitalisation, exports, imports, use of intellectual property rights, research and development, foreign investment, human capital and institutions; calculates the indicator of total factor productivity (TFP) based on the Cobb-Douglas function; the European Commission's Digital Economy and Society Index (DESI) is chosen as a measure of digitalisation. Regression results. It is proved that digitalisation is an important factor in productivity growth in the countries of the EU sample, which has a positive and significant impact on total factor productivity (a 1% increase in the digitalisation index leads to an increase in TFP by about 0.1%). Trade openness (exports and imports) was confirmed to remain a key factor affecting productivity. Income from the use of intellectual property rights has a positive, statistically significant impact on TFP growth, although the magnitude of this impact is rather small compared to the impact of digitalisation and trade. It has been found that the quality of institutions can be important for productivity growth, which is an argument for relevant reforms at the level of the government and local communities. The impact of research and development, foreign direct investment, and human capital on TFP growth is difficult to interpret due to the lack of statistical significance of these parameters, which requires further research.

Productivity gains from green finance: A holistic and regional examination from China

The productivity channel is extensively explored as a key mechanism through which finance influences economic growth. This paper specifically aims to assess the effect of green finance (GF) on firms' total factor productivity (TFP) using matched data on 2246 listed companies and their respective provinces in China from 2011 to 2019. The results demonstrate that, first, GF has a positive effect on firms' total factor productivity. Second, heterogeneity analysis reveals that the effects of GF differ across firms. Third, two paths through which GF can affect firms' TFP are identified: financing constraints and R&D investment. Finally, the differences-in-differences method suggests that green finance pilot policies contribute to an increase in firms' TFP in the pilot areas. However, the regression control method reveals regional variations in policy effectiveness and duration. In short, this study expands on the empirical literature that documents the nexus between productivity and financial markets and can provide an important theoretical and empirical basis for expanding the achievements of GF construction in China.

Information and communication technologies and medium-run fluctuations

This paper explores the possibility that productivity improvements in information and communication technologies (ICT) are a source of medium-run fluctuations in total factor productivity (TFP) and economic activity. We document in a structural VAR setting that supply shocks in the ICT sector are followed by hump-shaped and long-lasting increases in TFP. Following the ICT literature, we use a two-sector model to suggest a mechanism behind the hump-shaped TFP response: that ICT is a general-purpose technology (GPT). Using impulse-response matching, we show that a model with a spillover from ICT capital is able to match the hump-shaped TFP response, hinting at the importance of the diffusion of ICT.

Convergence Processes in the European Agriculture: Analysis of the Total Factor of Productivity

The article explored labour productivity in agriculture and carried out a comparative analysis of the achieved level of labour productivity in the countries of the European Union. The efficiency of agricultural production needs to be measured to improve the productivity, competitiveness, and profitability of farms. The multifactorial total factor productivity (MFP) of agricultural production evaluates the main and additional sources of economic growth in the agricultural sector. Based on the analysis of beta convergence, we observe an increase in total factor productivity (TFP) in Slovakia and the countries of the European Union. Based on the obtained statistically significant regression analysis models, beta convergence and sigma divergence of the TFP of the agricultural sector in the EU and Slovakia were identified. Thus, the process of positive convergence was confirmed, which indicates the convergence of the economic development of Slovakia with highly developed countries and the reduction of disparities between countries. State support and investment in innovative technologies will stimulate the adoption of new technologies and at the same time ensure technological progress and improve the impact of agriculture on the environment. We also concluded that it is important to improve the skills of those working in the agricultural sector, and as a result, an increase in TFP is expected.

Farmland Leasing, misallocation Reduction, and agricultural total factor Productivity: Insights from rice production in China

Reducing factor misallocation is crucial for enhancing agricultural aggregate total factor productivity (TFP) and narrowing the income gap between urban and rural areas in China. This paper presents a theoretical framework illustrating how farmland leasing affects aggregated TFP via the reduction of misallocation. Utilizing a comprehensive dataset of cost and benefit survey for Jiangsu rice farmers, monthly surface climatological data, and soil characteristic information from FAO, this study finds that efficient factor reallocation could theoretically result in a 23.6% increase in aggregate TFP. In practice, a 1% increase in farmland leasing leads to a 1.087% gain in aggregate TFP and a 0.267% reduction in misallocation using IV regression. These effects become larger after the Three Rights Separation (TRS) reform compared to prior conditions. The mediation effects of farmland leasing on TFP through misallocation reduction are only observed in Southern Jiangsu, possibly due to farmland concentration.

Impact of Climate Change on Total Factor Productivity of Agriculture in District Multan

Agriculture is particularly vulnerable to climate change because it faces open weather conditions. In uncontrolled open weather circumstances, it's difficult to attain better total factor productivity (TFP), which characterizes agriculture growth in an economy. The study estimated the TFP growth of Pakistan’s agriculture in first step by employing the Tornqvist- Theil index number approach for the period 1990-2019. The time series data for thirteen crops and four livestock categories from 1990 to 2019 were collected from different sources to estimate the TFP. The average annual TFP growth of agriculture was estimated to be 2.14 percent for the study period, and it contributed about 56 percent to total agricultural output growth. The results also indicated that TFP growth in agriculture sector was highest (0.05 percent) during the 1990-2000, while lowest (-2.14 percent) during the last decade. After, TFP index that was calculated in first step was used as dependent variable in second step of analysis. Then we estimated the impact of climate change on TFP in agriculture sector in Multan district. For this first we used ARDL bound test approach for estimation of long run impact of climatic variables. The results showed that there are positive effects of both minimum and maximum temperature on TFP in the study area. The drought and floods have negative impact on TFP but in first lag their effects became positive on TFP. One of the important variables of climate is rain that had positive impact on the TFP growth, with 1% increase in rainfall there was 0.22% increase in TFP at first lag. Improving the adaptation and mitigation practices related to floods and droughts would be required for sustainable growth in TFP in agriculture sector in Multan District.

AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity

Artificial intelligence is profoundly influencing various facets of our lives, indicating its potential to significantly impact sustainability. Nevertheless, capturing the productivity gains stemming from artificial intelligence in macro-level data poses challenges, leading to the question of whether artificial intelligence is reminiscent of the “Solow paradox”. This study employs micro-level manufacturing data to investigate the impact of artificial intelligence on firms’ productivity. The study finds that every 1% increase in artificial intelligence penetration can lead to a 14.2% increase in total factor productivity. This conclusion remains robust even after conducting endogeneity analysis and a series of robustness tests. The study identifies that the positive impact of artificial intelligence on productivity is primarily achieved through the value-added enhancement effect, skill-biased enhancement effect, and technology upgrading effect. Furthermore, the study reveals that the effects of artificial intelligence on productivity vary across different property rights and industry concentration contexts. Additionally, the structure of factor endowments within firms can also influence the productivity gains from artificial intelligence. Our study presents compelling evidence demonstrating the role of artificial intelligence in fostering economic sustainability within the framework of Industry 4.0.

Impact of biased technological progress on the total factor productivity of China's manufacturing industry: The driver of sustainable economic growth

Sustainable economic growth relies on an increase in total factor productivity (TFP). To upgrade the core competitiveness and support the sustainable growth and high-quality development of China's economy, ascending TFP in China's manufacturing industry counts. This study analysed the impact of biased technological progress on TFP, which was estimated by using the relevant data of 27 subsectors of China's manufacturing industry from 1980 to 2019. The results revealed that from the early industrialisation period (1980–2000) to the mid-industrialisation period (2001–2012) and then to the middle and late industrialisation period (2013–2019), the mean value of TFP in China's manufacturing industry grew from 0.291 to 0.394 and then to 0.555, embodying a continuously upwards advancing tendency. Technological progress was capital biased from 1981 to 2011 and labour biased during the rest of the sample period. In addition, its direction was heterogeneous among various industries. To some extent, the growth of TFP has been hindered by biased technological progress and factor allocation. The per unit increase in them has led to 1.495 and 1.017 unit decreases in the TFP. Upon examining the factor efficiency and the factor structure, we found that the estimated coefficients of biased technological progress dropped to −1.516 and −1.719, preventing TFP from growing to a greater extent. Consequently, there is room for their optimisation in China's manufacturing industry.

FACTORS AFFECTING ENERGY EFFICIENCY IN EMERGING ECONOMIES: MIXED MODELS

This study aims to explain energy efficiency by social, political, institutional, and economic variables for emerging countries during the 1990-2018 period. The estimated models exhibited cointegrated relationships. According to the predicted models, while GDP per capita and total factor productivity have the most positive effect on energy efficiency, on the other hand, fossil fuel use has the most negative effect on energy efficiency among economical variables. Social globalization, government efficiency, and control of corruption are positive; on the other hand, urbanization is negatively effective on energy efficiency.
 A 1% increase in GDP per capita improves the energy efficiency between 0.78% and 0.86%, and a 1% increase in total factor productivity increases it by about 0.48%. On the other hand, a 1% increase in fossil fuel consumption reduces energy efficiency between 0.56% and 0.70%. A 1% increase in the energy use of the service and industry sectors causes a decrease in the energy efficiency of about 0.43% and 0.19%, respectively. A 1% increase in social globalization, government efficiency, and control of corruption increase energy efficiency by about 0.15%, 0.10%, and 0.03%, respectively, while a 1% increase in urbanization decreases it by about 1.18%

The relationship between total factor productivity and environmental quality: A sustainable future with innovation input

Environmental quality has become an important issue around the world and has received much public attention in China. China is currently experiencing a rapid improvement in industrialization and urbanization. However, irrational resource exploitation and excessive energy consumption in the development track of the economy have contributed to the severe environmental quality crisis in China. Consequently, this study is devoted to exploring the association between economic development measured by total factor productivity (TFP) and environmental quality identified by SO2 emissions and PM2.5 concentrations in peripheral cities of China between 2004 and 2018. Using a two-way fixed effects model, we find a negative effect of TFP on environmental quality. Specifically, every 10 % increase in TFP significantly reduces SO2 emissions by 0.844 % and PM2.5 concentrations by 0.121 %. We also conclude that innovation input measured by science and technology expenditure plays a mediating role between environmental quality and TFP. Meanwhile, the impact of TFP on the mitigation of environmental problems varies significantly with regard to different regions, TFP scales, and industrial structures. Overall, our findings provide comprehensive insights into prefectural-level TFP and innovation input on environmental quality initiatives.