scholarly journals Does Corporate Financialization Have a Non-Linear Impact on Sustainable Total Factor Productivity? Perspectives of Cash Holdings and Technical Innovation

2021 ◽  
Vol 13 (5) ◽  
pp. 2533
Author(s):  
Hui Wang ◽  
Qing Wang ◽  
Xia Sheng
Keyword(s):  
Total Factor Productivity ◽  
Threshold Model ◽  
Threshold Effect ◽  
Sustainable Growth ◽  
Technical Innovation ◽  
Financial Assets ◽  
Factor Productivity ◽  
Positive Effects ◽  
Optimal Thresholds ◽  
Positive Effect

This study explores the conditions under which financialization may foster sustainable total factor productivity (TFP). We examine the inverted U-shaped relationship between corporate financialization and TFP by employing a panel threshold model using microeconomic non-financial panel data from Chinese firms in the 2007 to 2018 period. Our results suggest that the turning point is more significant in holding short-term financial assets and state-owned enterprises. The threshold effect suggests that technical innovation determines the optimal threshold at which TFP is affected by financialization. Further, financialization is considered an alternative to cash in order to increase the value of capital, leading to a positive effect on TFP. Contrary to their positive effects below the optimal thresholds, financialization exceeds a certain level, displaces technical innovation, and becomes detrimental to TFP. Our analysis thus establishes the importance of sustainable growth of TFP and minimize the adverse effect of financialization.

scholarly journals Research on Green Total Factor Productivity of Logistics Industry and its threshold effect of Environmental Regulation

2021 ◽  
Vol 235 ◽  
pp. 02022
Author(s):  
Wanchun Li
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Threshold Model ◽  
Intermediate Stage ◽  
Threshold Effect ◽  
Inhibitory Effect ◽  
Regulation System ◽  
Factor Productivity ◽  
Logistics Industry ◽  
The Government

This paper is based on the input-output panel data of logistics industry in 30 provinces and regions in China from 2005 to 2017, using nonparametric DEA model to evaluate the green total factor productivity of logistics industry, and build a panel threshold model to empirically test the nonlinear impact of environmental egulation. It is found that environmental regulation has a double threshold effect on green total factor productivity of logistics industry, the estimated threshold values are 89.85 and 211.27 respectively; when environmental regulation is at a low level below 89.85, environmental regulation has a positive effect of 2.09% on green total factor productivity of logistics industry, when environmental regulation is in the intermediate stage of 89.85 to 211.27, environmental regulation has a positive improvement effect of 6.41% on green total factor productivity of logistics industry; when environmental regulation is at a higher level than 211.27, environmental regulation has a negative inhibitory effect of 1.57% on green total factor productivity of logistics industry. Based on the empirical conclusion, this paper puts forward: First, using the performance assessment as the baton to urge the local government to establish an effective environmental regulation system; second, the government should plan to guide the green transformation and upgrading of the logistics industry to avoid “one size fits all” environmental regulation.

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 Improvement of Different Types of Environmental Regulations on Total Factor Productivity: A Threshold Effect Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xubin Lei ◽  
Shusheng Wu
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Environmental Regulations ◽  
Threshold Model ◽  
Threshold Effect ◽  
Impact Behavior ◽  
Factor Productivity ◽  
Data Set ◽  
Different Types ◽  
The Impact

Based on the distinction of different types of environmental regulations, this paper attempts to test the threshold effect of environmental regulation on the total factor productivity (TFP) by employing a panel threshold model and a province-level panel data set during 2006–2016. Research results show that the influence of command-and-control and market incentive environmental regulation on the total factor productivity has a single threshold conversion characteristic of foreign direct investment (FDI) and financial scale, but the impact behavior and influence degree around the threshold are inconsistent. The effect of voluntary conscious environmental regulation on the total factor productivity has a single threshold conversion feature of human capital, and moderately enhanced intensity of environmental regulation is conducive to promoting the total factor productivity after crossing the threshold. Finally, in order to enhance the regional total factor productivity, relevant policy recommendations are proposed.

scholarly journals Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data

2021 ◽  
Vol 13 (2) ◽  
pp. 472
Author(s):  
Shiying Hou ◽  
Liangrong Song
Keyword(s):  
Total Factor Productivity ◽  
Market Integration ◽  
Economic Transformation ◽  
Green Economy ◽  
Factor Productivity ◽  
Spatial Econometric ◽  
Positive Effects ◽  
Spatial Econometric Models ◽  
Level Data ◽  
Positive Effect

The development of market integration has an important effect on regional green total factor productivity (GTFP). Based on the panel data of 30 provinces in China from 2008 to 2017, this paper studies the spatial effect and transmission mechanism of market integration on regional green total factor productivity by calculating the Malmquist–Luenburger index and using spatial econometric models. It was found that market integration can promote the improvement of regional green total factor productivity. This positive effect is not only directly reflected in the region, it also indirectly promotes the growth of GTFP in nearby regions. In addition, market integration has shown significant positive effects on efficiency improvement and technological progress, and market integration has affected regional green total factor productivity through them. The above conclusions are of great significance for China to develop a green economy and promote high-quality economic transformation.

scholarly journals The Relationship between Environmental Regulation and Green Total Factor Productivity in China: An Empirical Study Based on the Panel Data of 177 Cities

2020 ◽  
Vol 17 (15) ◽  
pp. 5287 ◽  
Author(s):  
Mingliang Zhao ◽  
Fangyi Liu ◽  
Wei Sun ◽  
Xin Tao
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Inflection Point ◽  
Factor Productivity ◽  
Positive Effects ◽  
Development Level ◽  
The Mean ◽  
The Relationship

Promoting the coordinated development of industrialization and the environment is a goal pursued by all of the countries of the world. Strengthening environmental regulation (ER) and improving green total factor productivity (GTFP) are important means to achieving this goal. However, the relationship between ER and GTFP has been debated in the academic circles, which reflects the complexity of this issue. This paper empirically tested the relationship between ER and GTFP in China by using panel data and a systematic Gaussian Mixed Model (GMM) of 177 cities at the prefecture level. The research shows that the relationship between ER and GTFP is complex, which is reflected in the differences and nonlinearity between cities with different monitoring levels and different economic development levels. (1) The relationship between ER and GTFP is linear and non-linear in different urban groups. A positive linear relationship was found in the urban group with high economic development level, while a U-shaped nonlinear relationship was found in other urban groups. (2) There are differences in the inflection point value and the variable mean of ER in different urban groups, which have different promoting effects on GTFP. In key monitoring cities and low economic development level cities, the mean value of ER had not passed the inflection point, and ER was negatively correlated with GTFP. The mean values of ER variables in the whole sample, the non-key monitoring and the middle economic development level cities had all passed the inflection point, which gradually promoted the improvement of GTFP. (3) Among the control variables of the different city groups, science and technology input and the financial development level mainly had positive effects on GTFP, while foreign direct investment (FDI) and fixed asset investment variables mainly had negative effects.

Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries

2003 ◽  
Vol 33 (9) ◽  
pp. 1653-1660 ◽  
Author(s):  
Atakelty Hailu ◽  
Terrence S Veeman
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Productive Efficiency ◽  
Set Covering ◽  
Data Envelopment Analysis Model ◽  
Factor Productivity ◽  
Data Set ◽  
Term Survival ◽  
Positive Effects ◽  
Logging Industry

The Canadian boreal logging industry has attracted little or no attention from economic researchers in spite of its importance for the competitiveness and long-term survival of other forest-based industries. This article uses a panel data set covering the period from 1977 to 1995 to analyze technical efficiency, technical change, and total factor productivity growth in the logging industries for six boreal provinces. The production technology is represented using a data envelopment analysis model. A transitive measure of productivity change that combines technical progress and changes in the degree of productive efficiency is computed. The empirical investigation reveals that logging activities in the boreal region are characterized by substantial efficiency differentials among the regions. Results from a Tobit analysis of efficiency differentials indicate that forest resource characteristics such as forest density and proportion of hardwood production were found to have positive effects. There was also evidence of significant positive scale effects. Engineering construction per area seems to be negatively related to efficiency. Total factor productivity in the boreal logging industry progressed at an average annual rate of 1.56%.

scholarly journals Research on Total Factor Productivity and Influential Factors of the Regional Water–Energy–Food Nexus: A Case Study on Inner Mongolia, China

2019 ◽  
Vol 16 (17) ◽  
pp. 3051 ◽  
Author(s):  
Chen ◽  
Ding ◽  
Wang ◽  
Yu
Keyword(s):  
Total Factor Productivity ◽  
Inner Mongolia ◽  
Influential Factors ◽  
Government Support ◽  
Factor Productivity ◽  
Tobit Regression ◽  
Negative Effects ◽  
Lower Efficiency ◽  
Positive Effects ◽  
Regional Water

With the supply of water, energy and food facing severe challenges, there has been an increased recognition of the importance of studying the regional water–energy–food nexus. In this paper, Inner Mongolia, including 12 cities in China, was selected as a research case. A super-efficiency slack based measure (SBM) model that considered the undesirable outputs was adopted to calculate the regional total factor productivity (TFP) and the Malmquist–Luenberger index was used to investigate the change trend of the TFP from 2007 to 2016 based on understanding the water–energy–food nexus. Finally, influential factors of the TFP were explored by Tobit regression. The results show that the 12 Inner Mongolia cities are divided into higher, moderate and lower efficiency zones. The higher efficiency zone includes Ordos, Hohhot, Xing’an, and Tongliao, and the lower efficiency zone includes Chifeng, Xilin Gol, Baynnur, Wuhai and Alxa. There is a serious difference in TFP between Inner Mongolia cities. During the study period, the TFP of the water–energy–food nexus in Inner Mongolia cities shows a rising trend, which is mainly driven by the growth of technical progress change. However, the average ML values of the lower and moderate efficiency zones were inferior to the higher efficiency zone in six of the ten years, so the difference between Inner Mongolia cities is growing. According to the Tobit regression, the mechanization level and degree of opening up have positive effects on the TFP, while enterprise scale and the output of the third industry have negative effects on the TFP. Government support does not have any significant impact on the TFP. Finally, suggestions were put forward to improve the TFP of the water–energy–food nexus in Inner Mongolia cities.

The impact of external debt on total factor productivity and growth in HIPCs: non-linear regression approaches

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sisay Demissew Beyene ◽  
Balázs Kotosz
Keyword(s):  
Total Factor Productivity ◽  
Threshold Model ◽  
External Debt ◽  
Seemingly Unrelated Regression ◽  
Complete Analysis ◽  
Threshold Values ◽  
Factor Productivity ◽  
Content Type ◽  
Non Linear ◽  
The Impact

Purpose The purpose of this study is to provide an empirical analysis of the impact of external debt on total factor productivity (TFP) and growth along with the TFP channel through which external debt affects the growth of heavily indebted poor countries (HIPCs). Design/methodology/approach This study uses panel data econometrics; basically, the seemingly unrelated regression (SUR) and alternative non-linear (panel threshold) models. For robustness check, it also uses panel-corrected standard errors, feasible generalized least squares and SUR (using alternative variables). Findings External debt significantly reduces both TFP and growth. Besides, it confirms that the relationship between external debt and TFP and gross domestic product growth is non-linear. Further external debt can affect the growth of HIPCs through the TFP channel. However, the threshold model result reveals weak evidence of threshold values although there are some threshold values of 67 and 54 for TFP and growth models, respectively. Originality/value To the best of the authors’ knowledge, this is the first study on most concerned countries (HIPCs) that shows a detailed and complete analysis of the TFP channel and the impact of external debt on growth. Thus, it provides appropriate and sound policies that consider the unique characteristics of the countries. Unlike most previous findings, this study does not support an inverted U-shape relationship between external debt and growth. Further, it provides insights into the relationships among TFP, external debt and growth. Moreover, it considers basic panel econometric tests like cross-sectional dependence, uses a non-linear simultaneous equations model along with the alternative non-linear model and is supported by different robustness checks.

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