scholarly journals Have China’s Pilot Free Trade Zones Improved Green Total Factor Productivity?

2021 ◽  
Vol 18 (21) ◽  
pp. 11681
Author(s):  
Qingshan Ma ◽  
Yuanmeng Zhang ◽  
Kexin Yang ◽  
Lingyun He
Keyword(s):  
Total Factor Productivity ◽  
Free Trade ◽  
Industrial Structure ◽  
Positive Impact ◽  
Trade Openness ◽  
Factor Productivity ◽  
Free Trade Zones ◽  
Green Areas ◽  
The Difference ◽  
The Impact

Free trade zones (FTZ) are designated areas for promoting trade openness and investment facilitation. In China, FTZs are also regarded as “green areas” in which planning actions and institutional innovations are implemented, and there is a commitment to promoting urban green and healthy development. Given that green total factor productivity (GTFP) is an important measure of a city’s health and green performance, this study exploits the difference-in-differences method to explore the impact of pilot FTZs on urban GTFP in 280 cities in China for the period between 2005 and 2017. The results show that the green areas positively contributed to the growth of GTFP. Moreover, the outcome holds with robustness tests. Statistically, the positive effect emerged in cities during the first three years after introducing the initiative, with the effect disappearing afterward. It also had a strong positive impact in the central and western regions and in large and medium-sized cities, while the influence remained insignificant in the remaining areas in China. Furthermore, the paper also reveals that the promotion of foreign direct investment and industrial structure upgrading are the primary channels through which the positive relationship between pilot FTZs and GTFP is established.

scholarly journals Does the Construction of National Eco-Industrial Demonstration Parks Improve Green Total Factor Productivity? Evidence from Prefecture-Level Cities in China

2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Lu Liu ◽  
Xiaodong Yang ◽  
Yuxin Meng ◽  
Qiying Ran ◽  
Zilian Liu
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Mediation Effect ◽  
Eastern Region ◽  
Mechanism Analysis ◽  
Natural Experiments ◽  
Factor Productivity ◽  
The Difference ◽  
The Impact ◽  
Industrial Demonstration

This study conducted quasi-natural experiments based on the panel data of 239 prefecture-level cities in China from 2005 to 2017. The difference-in-difference (DID) and mediation effect model are used to test the impact and mechanism of the construction of national eco-industrial demonstration parks (NEDP) on green total factor productivity (GTFP). The results show that: (1) The construction of NEDP has significantly improved the urban GTFP, and the conclusion is still valid after running the robustness test. (2) Mechanism analysis shows that the construction of NEDP has improved GTFP through technological innovation and industrial structure upgrading. (3) The heterogeneity results reveal that NEDP has a significant positive effect on GTFP in the central and western regions, while the effect was insignificant in the eastern region. Moreover, NEDP significantly contributes to GTFP in resource-based and non-resource-based cities, while the contribution of resource-based cities is greater than that of non-resource-based cities. This study provides a reference for China to further promote the construction quality of NEDP and green development.

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

The Dynamic Correlation Between Capital Deepening and Total Factor Productivity in China

2020 ◽  
Vol 24 (4) ◽  
pp. 524-531
Author(s):  
Wuliu Zhang ◽  
Keyword(s):  
Human Capital ◽  
Total Factor Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
Linear Structure ◽  
Physical Capital ◽  
Influence Coefficient ◽  
Factor Productivity ◽  
Capital Deepening ◽  
The Impact

The impact of capital deepening on total factor productivity (TFP) is a significant and controversial issue. Based on the calculation of relevant indicators, this study adopts a Bayesian time-varying parameter model, Bayesian quantile regression, and adaptive Bayesian quantile models for in-depth statistical analysis. TFP was found to have a complex non-linear structure, and physical and human capital deepening indicators show a significant upward trend. The deepening of physical capital has a negative impact on TFP, while the deepening of human capital has a positive impact. In the capital deepening structure, the level of TFP has been improved and its structure optimized. Primary human and non-production physical capital deepening has no significant effect on TFP, while secondary human capital deepening has some significant effects on TFP. Tertiary and productive human capital deepening of TFP present two different forms of significant effect: the influence coefficient of the former declines in the increasing quantile and the change is larger, while the latter has a stable negative impact. The results of this study provide insights in terms of the improvement of China’s productivity.

scholarly journals Research on the Impact of Green Technology Innovation on Energy Total Factor Productivity, Based on Provincial Data of China

2021 ◽  
Vol 9 ◽  
Author(s):  
Mengxin Wang ◽  
Yanling Li ◽  
Gaoke Liao
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Technology Innovation ◽  
Threshold Model ◽  
Threshold Effect ◽  
Green Technology ◽  
Mediating Effect ◽  
Factor Productivity ◽  
Influence Mechanism ◽  
The Impact

Against the background of carbon peaking and carbon neutralization, green technology innovation plays an important role in promoting the energy total factor productivity (TFP). This study verifies the impact of green technology innovation on energy TFP in a complete sample and the subsamples by region, by constructing a panel threshold model, and analyzes its influence mechanism on the basis of the mediating effect test based on annual provincial data of mainland China from 2005 to 2018. The empirical results reveal the following: first, with the level of economic development as the threshold variable, there is a threshold effect in the impact of green technology innovation on the energy TFP; second, green technology innovation has an impact on the energy TFP through industrial structure upgrading; that is, industrial structure has a mediating effect in the influence mechanism; and third, there is heterogeneity in the impact of green technology innovation on the energy TFP among different regions in China, and the threshold effect only exists in the western region, since the central and eastern regions have crossed a certain developmental stage.

scholarly journals Energy Efficiency, Policy and GTFP

2018 ◽  
Vol 53 ◽  
pp. 01033
Author(s):  
Fangqing Yi ◽  
Zenglian Zhang
Keyword(s):  
Economic Growth ◽  
Energy Efficiency ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Industrial Structure ◽  
Provincial Government ◽  
Factor Productivity ◽  
Linkage Mechanism ◽  
The Government ◽  
The Impact

The environmental and resource constraints on economic growth are increasingly evident. China urgently needs to reshape its economic growth momentum. The increase in green total factor productivity is particularly necessary for the growth of the quantity and quality of the economy. This paper selects the provincial panel data of 30 provinces in China from 2001 to 2015, and establishes a panel exchangeable errors model to analyze the impact of eight indicators on green total factor productivity (GTFP) and verifies its effectiveness. Empirical analysis shows that inter-provincial government competition, environmental regulation, energy consumption, and capital stock have a significant impact on green total factor productivity. The influence of foreign direct investment, industrial structure, and industrialization level on the total factor productivity of green is not significant. Therefore, the government should adopt suitable, flexible and diverse environmental regulation policies, promote energy-saving emission reduction and technology innovations through policies such as taxes and subsidies, strengthen the linkage mechanism between industrial structure upgrading and energy efficiency, to increase green total factor productivity.

scholarly journals Influence of the Evolution of Marine Industry Structure on the Green Total Factor Productivity of Marine Economy

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1108
Author(s):  
Xinyi Wei ◽  
Qiuguang Hu ◽  
Weiteng Shen ◽  
Jintao Ma
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Government Intervention ◽  
Malmquist Index ◽  
Marine Ecology ◽  
Advanced Technology ◽  
Factor Productivity ◽  
Marine Economy ◽  
Low Efficiency ◽  
The Impact

The 14th five-year plan emphasizes the importance of marine ecology and environmental protection, and the green concept is incorporated into the high-quality development system of the marine economy. This research used the data of 11 coastal provinces and cities in China from 2006 to 2016, based on the super-efficiency slack-based measure model and global Malmquist index model. The objective was to calculate the green total factor productivity (GTFP) of the marine economy, to study the impact of the evolution of the marine industrial structure on marine economic GTFP. The study found the following: (1) in general, the upgrade of marine industrial structure promoted the growth of marine economic GTFP and presented an inverted “U” trend of initially promoting and then suppressing. Spatially, only the advancement and rationalization of industrial structure in the Yellow and Bohai Sea regions inhibited the growth of marine economic GTFP. In terms of time, the advanced marine industrial structure promoted the growth of GTFP from 2006 to 2010, whereas that of industrial structure inhibited the growth of GTFP from 2011 to 2016. (2) The GTFP of the marine economy showed an increasing trend, but the conversion rate of production technology is low. Falling into the “efficiency trap” of highly advanced technology input and low-efficiency technology output should be avoided. (3) Affected by the mismatch of regional resources or industrial structure, government intervention showed an “opposite” mechanism in areas with different marine economic strengths. Government intervention in areas with higher marine economic strength was conducive to GTFP growth, whereas government intervention in areas with weaker marine economic strength would hinder GTFP growth.

scholarly journals The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

2021 ◽  
Vol 13 (15) ◽  
pp. 8581
Author(s):  
Chaoxun Ding ◽  
Ruidan Zhang
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Data Model ◽  
Industrial Structure ◽  
Positive Impact ◽  
Malmquist Index ◽  
Annual Growth Rate ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Urbanization Rate

Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.

scholarly journals Trade Openness and Green Total Factor Productivity in China: The Role of ICT-Based Digital Trade

2021 ◽  
Vol 9 ◽  
Author(s):  
Huan Zhang
Keyword(s):  
Total Factor Productivity ◽  
Scale Effect ◽  
Trade Openness ◽  
Estimation Method ◽  
Policy Implications ◽  
Structure Effect ◽  
Factor Productivity ◽  
Estimation Model ◽  
Promoting Effect ◽  
The Impact

The vigorous development of modern information and communication technology (ICT) has driven the digital trade featured by the ICT technique and industry as the carrier. This study empirically tests the impact of ICT-based digital trade openness on green total factor productivity (GTFP) by selecting ICT as the representative digital trade data of 30 provinces in China over the timespan 2002–2018. We employ the slack-based model and global Malmquist–Luenberger (SBM-GML) estimation method to calculate the provincial GTFP and explore the heterogeneous impact of digital trade openness on GTFP through the scale effect, technology effect, and structure effect. In terms of empirical results, the panel fixed model and panel quantile estimation model both suggest the same findings. With the continuous expansion of the scale of digital trade, its scale effect has a significant inhibitory effect on GTFP, whereas the structure effect combined with human capital and the technology effect correlated with technological research and development (R&D) have a significant promoting effect on GTFP. The panel quantile regression model reveals that the interaction intensity increases gradually from a low quantile to high quantile. Further robustness tests also verify the consistency and stability of the results. Finally, the study puts forward corresponding practical suggestions for the construction of a high-quality open pattern of digital trade and the coordinated development of GTFP. The specific policy implications include the following: (1) Emphasize on the penetration and connection effect of the new generation of ICT, and strengthen the construction of enterprise informatization. (2) Expand digital trade openness and broaden the field of industrial cooperation. (3) Optimize the industrial structure of digital trade, and accelerate the development of core industries of digital trade. (4) Gradually promote the transformation of digital trade from relying on quantity and scale to product quality.

scholarly journals KONSENTRASI SPASIAL, AGLOMERASI DAN PRODUKTIVITAS PERUSAHAAN INDUSTRI MANUFAKTUR INDONESIA

2021 ◽  
Vol 4 (2) ◽  
pp. 146-156
Author(s):  
Kusuma Wardani (Universitas Indonesia) ◽  
Muhammad Halley Yudhistira (Universitas Indonesia)
Keyword(s):  
Total Factor Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
High Tech ◽  
Factor Productivity ◽  
Localization Economies ◽  
Instrument Variable ◽  
Urbanization Economies ◽  
The Impact ◽  
The Relationship

AbstractThis study aims to analyze the impact of agglomeration in the form of localization economies and urbanization economies on the productivity of manufacturing industrial companies in Indonesia. Unlike previous studies, this study will look at the effect of technology level on the relationship between productivity and agglomeration by classifying research samples into low-tech and high-tech industries. In addition, this study also improves the estimation technique by addressing the endogeneity problem that has the potential to arise in estimating the relationship between productivity and agglomeration to be overcome by using instrument variable (IV). The study was conducted in two stages of estimation using company-level panel data from 2010 to 2014. First, productivity was measured at the company level using Total Factor Productivity (TFP). Then, the company productivity is estimated together with the company and industry characteristic variables, including the agglomeration measurement variable which represents localization economies and urbanization economies. The regression results show a positive impact from localization economies and a negative impact from urbanization economies.AbstrakPenelitian ini bertujuan menganalisis dampak aglomerasi berupa localization economies dan urbanization economies terhadap produktivitas perusahaan industri manufaktur di Indonesia. Berbeda dengan penelitian terdahulu yang juga meneliti dampak aglomerasi industri terhadap produktivitas perusahaan, pada penelitian ini akan melihat pengaruh tingkat teknologi terhadap hubungan produktivitas dan aglomerasi dengan mengklasifikasikan sampel penelitian ke dalam industri berteknologi rendah dan industri berteknologi tinggi. Selain itu, peneltian ini juga memperbaiki teknik estimasi dari penelitian sebelumnya dengan menangani masalah endogenitas yang berpotensi muncul dalam mengestimasi hubungan produktivitas dan aglomerasi akan diatasi dengan penggunaan instrument variable (IV). Penelitian dilakukan dalam dua tahap estimasi dengan menggunakan data panel level perusahaan dari tahun 2010 sampai 2014. Pertama, produktivitas diukur pada level perusahaan dengan menggunakan Total Factor Productivity (TFP). Kemudian, produktivitas perusahaan diestimasi bersama variabel karakteristik perusahaan dan industri, termasuk variabel pengukuran aglomerasi yang mewakili localization economies dan urbanization economies. Hasil regresi menunjukkan adanya dampak positif dari localization economies dan dampak negatif dari urbanization economies.

scholarly journals Total Factor Productivity Shock and Economic Growth in Selected Asean+3 Countries: A New Evidence Using a Panel Var

2021 ◽  
Vol 21 (3) ◽  
pp. 1366-1383
Author(s):  
Noorazeela Zainol Abidin ◽  
Ishak Yussof ◽  
Zulkefly Abdul Karim
Keyword(s):  
Economic Growth ◽  
Total Factor Productivity ◽  
Positive Impact ◽  
Production Costs ◽  
Panel Var ◽  
Factor Productivity ◽  
Data Set ◽  
Tfp Growth ◽  
The Impact ◽  
Panel Vector Autoregression

A comparison between countries shows that there is a difference in terms of economic growth achievement across nations. This difference is due to the contribution of capital growth, labor, and total factor productivity (TFP). Although the use of capital and labor plays a vital role in the production, the contribution of TFP growth is also indispensable, as it saves production costs. Nevertheless, in 1995-2000, most countries have experienced a negative growth of TFP in which can affect its contribution to economic growth. Therefore, the focal point of this study is to analyze the impact of TFP growth shock on economic growth in selected ASEAN+3 countries (i.e., Malaysia, Singapore, Thailand, Indonesia, Philippines, Cambodia, Vietnam, China, South Korea, and Japan), using the data set from 1981 to 2014. The study employed the panel vector autoregression (PVAR) method in analyzing the propagation of the shocks through impulse response function and variance decomposition. The main findings revealed that TFP growth shocks have a positive impact on economic growth. Besides, the results also showed that over the next ten years, the proportion of human capital variation would be more dominant in contributing to the economic growth for the selected ASEAN+3 countries. As the surge in TFP growth had a positive impact on economic growth, this finding indicated that each country needs to allocate more expenditure in the Research and Development (R&D) activities.

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