Regional determinants of total factor productivity in Japan: stochastic frontier analysis

2017 ◽  
Vol 58 (3) ◽  
pp. 579-596 ◽  
Author(s):  
Akihiro Otsuka
Keyword(s):  
Total Factor Productivity ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Factor Productivity ◽  
Frontier Analysis

Frontier Technology, Trade, FDI,  and TFP in the Electrical and Electronic Industries: Exporting or Processing Trade?

2022 ◽  
pp. 1-23
Author(s):  
Noor Aini Khalifah
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Trade Intensity ◽  
Factor Productivity ◽  
Processing Trade ◽  
Export Intensity ◽  
Frontier Analysis ◽  
Catching Up

Abstract Does “openness” determine “catching-up” of establishments to frontier technology and total factor productivity (TFP) in Malaysia's electrical and electronic (E&E) industries? We contribute to this debate by applying a new measurement of processing trade intensity. Utilizing stochastic frontier analysis and Levinsohn and Pertrin (LP) TFP, we investigate determinants of technical efficiency (TE) and TFP. The results show that processing trade intensity and not export intensity determines TE and TFP for the overall sample and subsample of foreign establishments. In the processing trade subsample, export intensity is negatively related to TE and unrelated to TFP, obtaining an unconventional result that exporters are inefficient and not associated with TFP. The results show that higher foreign ownership shares of establishments are negatively associated with LP TFP.

scholarly journals ANALYSIS OF TOTAL FACTOR PRODUCTIVITY GROWTH IN THE INDUSTRY OF TEXTILE AND TEXTILE PRODUCTS IN INDONESIA

2021 ◽  
Vol 6 (1) ◽  
pp. 15
Author(s):  
Muhamad Nur Wafi ◽  
Dyah Wulan Sari, Ph.D
Keyword(s):  
Total Factor Productivity ◽  
Textile Industry ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Global Market ◽  
Factor Productivity ◽  
Raw Data ◽  
Average Growth ◽  
Frontier Analysis ◽  
Textile Products

This study aims to analyze the growth of TFP in the textile industry and textile product (TPT) in Indonesia. Productivity analysis is carried out to determine the extent of performance development and how efficient the textile industry in Indonesia. Calculation of the growth value of Total Factor Productivity (TFP) by decomposing the components of TFP namely TEC, TC, and SEC using the Stochastic Frontier Analysis (SFA). This study uses the type of firm level TPT data in the years 2010-2014. The data used is secondary data which is the result of an annual survey of large and medium manufacturing industry companies conducted by the Central Statistics Agency (BPS) in the form of raw data. The data is in the form of unbalance raw data which is then selected and adjusted to balance data. With 2 industry groups namely the textile industry (ISIC 13) and the garment industry (ISIC 14). Based on the results of the study showed that the average value of TFP growth in 2010-2014 experienced negative growth or <1, this is due to the average growth value of TEC, SEC, and TC which decreased and tended to have negative values in the study period. This shows that the level of efficiency, use of technology, and scale of efficiency of the textile industry tends to be weak in the 2010-2014 period. The reduced level of industrial productivity can affect the decline in the competitiveness of textile products in the global market. Keywords: Textile Industry and Textile Products (TPT), Total Factor Productivity (TFP), Stochastic Frontier Analysis (SFA)Jel : L67, C23; O47

scholarly journals Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

2021 ◽  
Author(s):  
Yingyu Zhu ◽  
Yan Zhang ◽  
Huilan Piao
Keyword(s):  
Total Factor Productivity ◽  
Stochastic Frontier Analysis ◽  
Carbon Sink ◽  
Stochastic Frontier ◽  
Empirical Literature ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Frontier Analysis ◽  
Agricultural Mechanization ◽  
The Impact

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China&rsquo;s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China&rsquo;s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

scholarly journals Does Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

2021 ◽  
Author(s):  
Yingyu Zhu ◽  
Yan Zhang ◽  
Huilan Piao
Keyword(s):  
Total Factor Productivity ◽  
Stochastic Frontier Analysis ◽  
Carbon Sink ◽  
Stochastic Frontier ◽  
Empirical Literature ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Frontier Analysis ◽  
Oriented Distance ◽  
The Impact

Mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Using the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of planting industry based on net carbon sink, and empirically studies the impact of mechanization on the planting green total factor productivity. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

scholarly journals How Technologically Progressive Was Germany in the Interwar Period? Evidence on Total Factor Productivity in Coal Mining

2016 ◽  
Vol 76 (4) ◽  
pp. 1113-1151
Author(s):  
Tobias A. Jopp
Keyword(s):  
Coal Mining ◽  
Total Factor Productivity ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Extended Period ◽  
Hard Coal ◽  
German Industry ◽  
Factor Productivity ◽  
Frontier Analysis ◽  
Tfp Growth

The discussion of the rationalization wave in German industry (1924–1929) still lacks proper industry-level estimates of the rate of technological progress. To close part of this gap, this article investigates total factor productivity (TFP) growth in hard coal mining over the extended period 1913–1938. Stochastic Frontier Analysis is applied to a sample of firms from the Ruhr coal district. TFP grew positively overall and specifically from 1924–1929. Surprisingly, however, TFP growth was even faster from 1933–1938, suggesting that the Nazi economy heavily capitalized on the Weimar rationalization movement, the effects of which are usually not traced beyond 1932.

scholarly journals Productive Performance of Indian Mining Industry: A Stochastic Frontier Decomposition

2017 ◽  
Vol 7 (1) ◽  
pp. 80-88
Author(s):  
Auro Kumar Sahoo ◽  
Dukhabandhu Sahoo ◽  
Naresh Chandra Sahu
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Mining Industry ◽  
Factor Productivity ◽  
Stochastic Production Frontier ◽  
Frontier Analysis ◽  
Tfp Growth ◽  
Stochastic Production

This paper has estimated the Total Factor Productivity (TFP) growth of Indian mining Industry for the period 1989-2014 based on a decomposed formulation of stochastic production frontier. Productivity growth and its decomposed components have been compared over the study period. It is found that the annual average TFP growth of mining industry rose up from 3.66 % during 1989-2005 to 8.76 % during 2006-2014. Further, the result of decomposition reflects that the major source of productivity growth has changed from Technological Progress (TP) in initial years to Technical Efficiency Change (TEC) in recent years. In view of this, it could be suggested that mining industry in India requires to focus on investment in innovation and up-gradation of existing technology to further enhance productivity. Keywords: Total Factor productivity; Mining Industry; Panel data; Stochastic Frontier Analysis (SFA) 

A Stochastic Frontier Approach to Assessing Total Factor Productivity Change in China’s Star-Rated Hotel Industry

2020 ◽  
Vol 45 (1) ◽  
pp. 109-132
Author(s):  
Hongwei Liu ◽  
Henry Tsai
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Stochastic Frontier ◽  
Hotel Industry ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Frontier Analysis ◽  
Scale Change ◽  
Economic Regions

Using a stochastic frontier analysis approach and a flexible translog production function considering neutral technological progress, this study assesses technical efficiency change, technological change, and scale change, and further measures the total factor productivity (TFP) change and its convergence of China’s star-rated hotel industry in 31 provinces, municipalities, and regions from 2001 to 2015. The results show that the TFP change of China’s star-rated hotel industry was generally favorable and boosted by both the technical efficiency change and technical change; nevertheless, the scale change hindered and largely caused fluctuations in the TFP change. From a regional economic perspective, the TFP change of the star-rated hotel industry in most of the eight comprehensive economic regions examined was rather stable. While few comprehensive economic regions existed absolute convergence, all of the regions showed significant conditional convergence except for the Eastern Coastal region.

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