Research on the Influence of Biased Technological Progress on Total Factor Productivity of Regional Road Transport in China

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Rui Huang
Keyword(s):  
Total Factor Productivity ◽  
Technological Progress ◽  
Road Transport ◽  
Factor Productivity

scholarly journals Technological Bias and Its Influencing Factors in Sustainable Development of China’s Transportation

2020 ◽  
Vol 12 (14) ◽  
pp. 5704
Author(s):  
Shuai Zhang ◽  
Xiaoman Zhao ◽  
Changwei Yuan ◽  
Xiu Wang
Keyword(s):  
Sustainable Development ◽  
Technological Change ◽  
Total Factor Productivity ◽  
Influencing Factors ◽  
Technological Progress ◽  
Transportation Industry ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Biased Technological Change ◽  
Technological Bias

The bias of technological progress, particularly relating to energy saving and carbon emissions reduction, plays a significant role in the sustainable development of transportation, and has not yet received sufficient attention. The objectives of this paper were to examine the bias of technological change (BTC), input-biased technological change (IBTC), and output-biased technological change (OBTC), and their influencing factors in the sustainable development of China’s regional transportation industry from 2005 to 2017. A slack-based measure (SBM) Malmquist productivity index was adopted to measure the BTC, IBTC, and OBTC by decomposing green total factor productivity. The results revealed that: (1) Continuous technological bias progress and input-biased technological progress existed in China’s transportation development from 2005 to 2017, making an important contribution to green total factor productivity. The output-biased technological change was close to 1, indicating a slight impact on the sustainable development of the transportation industry; (2) The bias of technological progress in eastern regions was slightly greater than that in central regions, and obviously greater than that in western regions. Moreover, different provinces experienced different types of technological bias change, with four major types observed during the research period; (3) The input-biased technology of a majority of provinces tended to invest more capital relative to labor, using more capital comparing to energy, and consume more energy relative to labor, while the output-biased technology of most provinces tended to produce desirable outputs (value added in transportation) and reduce the byproduct of CO2 relatively; (4) Average years of education, green patents in transportation, industrial scale, and local government fiscal expenditure in transportation significantly contributed to promoting the bias of technological progress, which was inhibited by the R&D investment. This study provides further insight into the improvement of sustainable development for China’s transportation, thereby helping to guide the government to promote green-biased technological progress and optimize the allocation of resources.

scholarly journals Sources of Total-Factor Productivity and Efficiency Changes in China’s Agriculture

Agriculture ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 279 ◽  
Author(s):  
Jianxu Liu ◽  
Changrui Dong ◽  
Shutong Liu ◽  
Sanzidur Rahman ◽  
Songsak Sriboonchitta
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technological Progress ◽  
Agricultural Development ◽  
Agricultural Productivity ◽  
Development Projects ◽  
Production Environment ◽  
Factor Productivity ◽  
Provincial Level ◽  
Constant Returns To Scale

The core of agricultural development depends on agricultural production efficiency improvement, and total-factor productivity growth is its significant embodiment. Hence, it is essential to address the question of “how to improve China’s agricultural productivity and efficiency in order to achieve growth and sustainability of agriculture in the future”. This paper estimates indices of China’s agricultural technical efficiency (TE) scores, total-factor productivity (TFP), and its two components, technological change/progress (TC) and technical efficiency change (EC), using provincial-level panel data of 30 provinces from 2002 to 2017 by applying a stochastic frontier approach (SFA). The paper also identifies determinants of TE, TC, and TFP using selected indicators from four hierarchical levels of the economy, i.e., farm level, production environment level, provincial level, and the state level, by applying a system-GMM method. Results reveal that agricultural labor, machinery, agricultural plastic film, and pesticides are the significant drivers of agricultural productivity, with no significant role of land area under cultivation. Constant returns to scale exist in China’s agriculture. The agricultural technical efficiency level fluctuated between 80% and 91% with a stable trend and a slight decline in later years, while TFP improved consistently over time, mainly driven by technological progress. Among the determinants, government investment in agricultural development projects significantly drives TC and TE, while the experienced labor force significantly increases TE. The disaster rate significantly reduces TE but promotes TC and TFP. The literacy rate significantly improves TC and TFP. However, government expenditures in “agriculture, forestry, and water” significantly reduce TE, TC, and TFP. Policy recommendations include (1) increased levels of mechanization and agriculture film use while avoiding an increase in pesticide use, (2) a continued increase in government expenditure in agricultural development projects, R&D to improve technological progress, and diffusion of modern agricultural technologies, and (3) investment in education targeted at the farming population in order to continue the growth in the productivity and sustainability of China’s agriculture.

scholarly journals TOTAL FACTOR PRODUCTIVITY GROWTH IN CHINA’S CORN FARMING: AN APPLICATION OF GENERALIZED PRODUCTIVITY INDICATOR

2021 ◽  
Vol 22 (5) ◽  
pp. 1189-1208
Author(s):  
Zhiyong Niu ◽  
Yining Zhang ◽  
Tianxiang Li ◽  
Tomas Baležentis ◽  
Dalia Štreimikienė ◽  
...  
Keyword(s):  
Total Factor Productivity ◽  
Technological Progress ◽  
General Setting ◽  
Scale Efficiency ◽  
Production Data ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Corn Production ◽  
Tfp Growth ◽  
Input And Output

Total factor productivity (TFP) growth measures usually focus on a certain direction of optimization and ignore the general setting encompassing the input and output orientations simultaneously. This paper uses the generalized Luenberger-Hicks-Moorsteen (LHM) TFP indicator which is additively complete and can be decomposed by three mutually exclusive elements. The input- and output-oriented analysis is undertaken in order to derive the generalized TFP measured. The paper uses the corn production data from 19 Chinese provinces over the period of 2004–2017. This research is important as China is the second largest corn producer in the world. The TFP growth was observed for Chinese corn farming the rate of 0.56% per year. The technological progress (0.48%) was the major source of the TFP growth, whereas the importance of the technical efficiency change (0.09%) and scale efficiency change (–0.01%) was negligible.

scholarly journals The Influence of Complexity of Imported Products on Total Factor Productivity

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Nan Wang ◽  
Wei Liu ◽  
Shanwu Sun ◽  
Qingjun Wang
Keyword(s):  
Total Factor Productivity ◽  
Technological Progress ◽  
Negative Impact ◽  
Positive Impact ◽  
Factor Productivity ◽  
Product Complexity ◽  
Technological Efficiency ◽  
The World ◽  
Belt And Road ◽  
Import Trade

The research results show that, all over the world, the increase in complexity of China’s imported products has significantly promoted the growth of total factor productivity and technological progress but has no obvious impact on technological efficiency. In “Belt and Road” samples, the increase in import product complexity did not improve the total factor productivity and technological progress, which had a negative impact on technical efficiency. Whether it is anywhere in the world or in the scope of “Belt and Road” countries, the import product density has a significantly positive impact on total factor productivity but has no significant effect on the promotion of technological progress and efficiency. Therefore, it is necessary to focus on adjusting the import trade structure of “Belt and Road” countries. Relying on the domestic consumer market, the manufacturing imports from countries along the “Belt and Road” route should be expanded so as to stimulate the promotion of domestic industrial total factor productivity.

scholarly journals Going Green or Going Away? A Spatial Empirical Examination of the Relationship between Environmental Regulations, Biased Technological Progress, and Green Total Factor Productivity

2018 ◽  
Vol 15 (9) ◽  
pp. 1917 ◽  
Author(s):  
Xueli Wang ◽  
Caizhi Sun ◽  
Song Wang ◽  
Zhixiong Zhang ◽  
Wei Zou
Keyword(s):  
Economic Development ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Capital Stock ◽  
Technological Progress ◽  
Environmental Regulations ◽  
Factor Productivity ◽  
Total Capital ◽  
Green Gdp ◽  
The Relationship

China’s economic development has resulted in significant resource consumption and environmental damage. However, technological progress is important for achieving coordinated economic development and environmental protection. Appropriate environmental regulation policies are also important. Although green total factor productivity, environmental regulations, and technological progress vary by location, few studies have been conducted from a spatial perspective. However, spatial spillover effects should be taken into consideration. This study used energy consumption, the sum of physical capital stock and ecological service value as total capital stock, the number of employed people as inputs, sulfur dioxide emissions as undesired outputs, and green GDP as total output to obtain green TFP through a slacks-based measure (SBM) global Malmquist-Luenberger Index. This study also estimated China’s biased technological progress under environmental constraints from 2004 to 2015 based on relevant data (e.g., green GDP, total capital stock, and employment figures). The relationship between green total factor productivity (GTFP), technological progress, and environmental regulation was then examined using a spatial Durbin model. Results were as follows: (1) Based on the complementary elements, although the labor costs gradually increase, the rapid accumulation of capital leads to technological progress that is biased toward capital. However, technological progress in the labor bias can significantly increase GTFP. (2) There is a u-shaped relationship between existing environmental regulations and GTFP. Technological progress can significantly promote GTFP in the surrounding areas through existing environmental regulations. (3) Under spatial weight, the secondary industry coefficient was negative while human capital stock and FDID had positive effects on GTFP. Technological progress is the source of economic growth. It is therefore necessary to promote biased technological development and improve labor-force skills while implementing effective environmental regulation policies.

scholarly journals Components of Total Factor Productivity Growth of the 2-Digit Manufacturing Industries in Karnataka: A Stochastic Frontier Approach

2020 ◽  
Vol 19 (1) ◽  
pp. 47-74
Author(s):  
Prasanta Kumar Roy ◽  
Sebak Kumar Jana ◽  
Devkumar Nayek
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Technological Progress ◽  
Stochastic Frontier ◽  
Manufacturing Industries ◽  
Total Factor Productivity Growth ◽  
Factor Productivity ◽  
Stochastic Frontier Approach ◽  
Entire Study ◽  
Reform Period

The study estimates the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries in Karnataka during the period from 1981-82 to 2010-11, during the entire study period, during the pre & post reform period (1981-82 to 1990-91 and 1991-92 to 2010-11) and also during two different decades of the post-reform period, i.e., during 1991-92 to 2000-01 and 2001-02 to 2010-11 using stochastic frontier approach. Technological progress is found to be the major driving force of TFPG and the decline in TFPG of the state’s manufacturing industries during the post-reform period is mainly accounted for by the decline in technological progress (TP) of the same during that period.

scholarly journals The Determinants of Economic Growth in Hungary, Poland, Slovakia and the Czech Republic During the Years 1995-2010

Equilibrium ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. 7-26 ◽  
Author(s):  
Katarzyna Anna Baran
Keyword(s):  
Economic Growth ◽  
Czech Republic ◽  
Total Factor Productivity ◽  
Capital Accumulation ◽  
Technological Progress ◽  
Growth Accounting ◽  
Parametric Approach ◽  
Factor Productivity ◽  
The Czech Republic ◽  
Non Parametric

The main goal of the research is to obtain a comprehensive examination of the economic growth determinants in Hungary, Poland, Slovakia and the Czech Republic (CEEC-4) since 1995. For this purpose, two methodological approaches have been applied: the Solow growth accounting and the non-parametric approach. At the beginning of the analysis, in order to obtain a general overview of the sources of economic growth in the former transition countries of Central Eastern Europe, the Solow growth accounting has been conducted. It decomposes the growth rate of output into contributions from changes in the quantity of the physical capital stock, the amount of labour input and some other unexplained factor commonly interpreted as reflecting technological progress and called the “Solow residual” or “Total Factor Productivity (TFP)”. The hypothesis that technological progress together with strong capital accumulation were the dominant factors behind the economic growth and convergence process in the Central Eastern European countries before the crisis is tested. As the Solow growth accounting does not reveal the driving forces behind the technological progress and, thus, a large part of the growth decomposition remains unexplained in the transition economies, the non-parametric approach has been employed to shed more light on the ultimate sources of economic growth in the CEEC-4. The non-parametric (production-frontier) method enables the further decomposition of changes in total factor productivity into changes in the efficiency of production and technological changes. Furthermore, it allows accounting for human capital accumulation, since improvements in quality of labour are also reflected in TFP growth.

Sign in / Sign up

Export Citation Format

Share Document