Competition Effects and Industrial Productivity:  Lessons from Japanese Industry

2017 ◽  
Vol 16 (3) ◽  
pp. 214-249
Author(s):  
Masahito Ambashi
Keyword(s):  
Growth Rate ◽  
Causal Relation ◽  
Manufacturing Industries ◽  
Factor Productivity ◽  
Competition Effect ◽  
Tfp Growth ◽  
Industrial Productivity ◽  
Japanese Industry ◽  
Schumpeterian Hypothesis ◽  
Positive Effect

This study mainly investigates the causal relation between the degree of competition, which is measured by the Lerner index, and the total factor productivity (TFP) growth rate on the basis of the Japanese industry-level panel data from 1980 to 2008. While the main finding uncovers a positive effect of competition on the TFP growth rate in manufacturing industries throughout the sample period, 1980–2008, the observed effect for non-manufacturing industries at this time is slightly negative. This unique finding of a negative competition effect suggests that the Schumpeterian hypothesis may be applicable in non-manufacturing industries.

scholarly journals What Is the Focus of Structural Reform in China?—Comparison of the Factor Misallocation Degree within the Manufacturing Industry with a Unified Model

2018 ◽  
Vol 10 (11) ◽  
pp. 4051 ◽  
Author(s):  
Laiqun Jin ◽  
Changwei Mo ◽  
Bochao Zhang ◽  
Bing Yu
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Severity Index ◽  
Unified Model ◽  
Structural Reform ◽  
Industrial Enterprise ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Production Factors

The misallocation of production factors, with structural misallocation as an important aspect, is a key instigator of low total factor productivity (TFP) growth rate in China, but one important question is which structural misallocation of what factor is more serious in China. Using China’s manufacturing industrial enterprise data from 1998 to 2013, we calculated and compared the factors misallocation degree among industries, ownerships and regions. The results indicated that, the misallocation among industries was most serious, which led to a TFP loss of 8.12% annually. The misallocation among ownerships ranked second, which led to a TFP loss of 5.49%. The least degree of the misallocation recorded among provinces led to TFP loss of 3.05%. By using the relative severity index, the rank is the same. As to the capital, the misallocation among ownerships was most serious, which led to TFP loss of 4.62%. But as to the labor, the misallocation among industries was most serious, which led to TFP loss of 4.58%. Moreover, the misallocation among ownerships alleviated rapidly from 1998 to 2007, while alleviated slower among industries and regions. However, from 2008 to 2013, all three types of structural misallocation have become worse, especially in labor. These conclusions are important to identify the focus of structural reform in China.

scholarly journals Knowledge Spillover and R&D in Manufacturing Enterprises

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ting Yang ◽  
Bing Gao ◽  
Kejin Xie
Keyword(s):  
Knowledge Spillovers ◽  
Spillover Effect ◽  
Manufacturing Industries ◽  
Significant Positive Effect ◽  
Factor Productivity ◽  
Manufacturing Enterprises ◽  
City Regions ◽  
The Mean ◽  
Interaction Term ◽  
Positive Effect

Based on the database of Chinese industrial industries, a model is constructed to empirically analyze the interaction between knowledge spillovers and R&D in manufacturing industries; the mean productivity values of county and city regions have a significant positive effect on firms' R&D, which gradually decreases; an interaction term between the number of neighboring firms and the average total factor productivity of industries in different regional scopes is added, and the greater the number of neighboring firms in the neighborhood, the greater the spillover effect on research and development. In order to increase the innovation input of companies, they need to be given the space to fully exchange ideas.

Estimation of Total Factor Productivity Growth (TFPG) of Major Cereal Crops in Rajasthan

2021 ◽  
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Production Function ◽  
Output Growth ◽  
Productivity Index ◽  
Cereal Crops ◽  
Total Output ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Moderate Growth

Cereal crops provide essential nutrients and energy in the everyday human diet through direct human consumption and meat production since they comprise a major livestock feed. In the current study, the Tornqvist Theil Index was used to compute the total output index, total input index, and total factor productivity index. The Tornqvist Index is exact for the homogenous translog production function that can deliver a second-order approximation to an arbitrary twice differentiable homogenous production function. This study has indicated moderate TFP in wheat (1.45percent), and the contribution of TFP to output growth was high, about 87 percent for wheat in Rajasthan state. The annual compound growth rate of the TFP of barley increased at the rate of 1.65 percent per annum (moderate growth), and the contribution of TFP to output growth was average, at about 63.47. In comparison, the compound growth rate of TFP of annual maize crop increased at 1.80 percent per annum (moderate growth), while its TFP to output growth was about 73.09 percent. The annual compound growth rate of the TFP of bajra increased by 2.56 percent per year. The contribution of TFP to output growth was 61.29 percent for bajra in Rajasthan. The real cost of production of barley and maize increased by 0.88 and 1.59 percent, which decreased for wheat and bajra by -0.93 and -0.21 percent per annum, respectively. It was revealed that in the bajra crop, Rajasthan state showed good performance of TFP growth among the selected cereal crops. The technology, including agronomical practices, plant protection measures, and mechanization, helped to sustain TFP growth in the bajra crop.

Trade Liberalisation and Industrial Productivity: Evidence from Manufacturing Industries in Pakistan

2017 ◽  
Vol 56 (4) ◽  
pp. 319-348
Author(s):  
Gulzar Ahmed ◽  
Muhammad Arshad Khan ◽  
Tahir Mahmood ◽  
Muhammad Afzal
Keyword(s):  
Total Factor Productivity ◽  
Negative Impact ◽  
Raw Materials ◽  
Manufacturing Sector ◽  
Industrial Sector ◽  
Manufacturing Industries ◽  
Trade Liberalisation ◽  
Factor Productivity ◽  
Industrial Productivity ◽  
The Impact

This study examines the impact of trade liberalisation on the industrial productivity for a panel of twenty seven 3-digit manufacturing industries in Pakistan over the period 1980-2006. Using a variant of the Cobb-Douglas production function for industrial sector, we estimated output elasticities. The results show positive output elasticities with respect to labour, capital and raw materials for the pre-trade liberalisation period (1981 –1995) as well as post-trade liberalisation period (1996-2006). For the pre-liberalisation period, we observe positive output elasticity with respect to energy, while it turns out to be negative in the post-liberalisation period probably due to energy crisis in Pakistan. In the second stage, we calculate total factor productivity (TFP) and examine the impact of trade liberalisation on TFP for pre-and post-trade liberalisation periods. The results reveal that trade liberalisation proxied by import duty has positive but negligible impact on the TFP in the pre-as well as post-liberalisation periods. On the other hand, effective rates of protection exert large negative impact on the TFP in the post-liberalisation than the pre-liberalisation period. JEL Classifications: F14, F13, O53, L60 Keywords: Trade Liberalisation, Total Factor Productivity, Manufacturing Sector of Pakistan

scholarly journals Competition, selection, and productivity growth in the Chilean manufacturing industry

2020 ◽  
Vol 29 (3) ◽  
pp. 877-892
Author(s):  
Roberto Álvarez ◽  
Aldo Gonzalez
Keyword(s):  
Productivity Growth ◽  
Manufacturing Industry ◽  
Factor Productivity ◽  
Manufacturing Plants ◽  
Tfp Growth ◽  
Key Driver ◽  
Quantitative Impact ◽  
The Impact ◽  
Firm Selection ◽  
Positive Effect

Abstract Competition is considered as a key driver of productivity growth. However, the empirical evidence on its impact is scant in developing countries. Using information from manufacturing plants for the period 1995–2007, we analyze the impact of competition on firm selection and productivity growth in Chile. Our results indicate that competition has a positive effect on total factor productivity (TFP) growth, especially for laggard firms. We find weaker evidence that competition affects the probability of exit for low-productivity firms. In general, these results for productivity growth are robust to alternative methodologies for calculating productivity and to the inclusion of other variables that may affect firms’ TFP growth. We find support for Schumpeterian forces, but the quantitative impact is small.

Total factor productivity growth and regional competitive analysis of China’s star-rated hotels

2018 ◽  
Vol 24 (6) ◽  
pp. 625-644 ◽  
Author(s):  
Hongwei Liu ◽  
Henry Tsai
Keyword(s):  
Growth Rate ◽  
Production Technology ◽  
Operational Efficiency ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Average Growth Rate ◽  
Annual Average ◽  
Optimal Production ◽  
Average Growth ◽  
Tfp Growth

This study investigates the total factor productivity (TFP) growth, technological progress, pure technical efficiency change, scale efficiency change, and mix efficiency change of star-rated hotels in China by employing a Hicks–Moorsteen index approach. The results show that the TFP of star-rated hotels in China had an annual average growth rate of 13.11%, mainly attributed to an annual average growth rate of operational efficiency of 21.85% and a mix efficiency growth rate of 13.52%. The growth rate of optimal production technology in the Western region markedly outperformed those in other regions and yet its growth rate of operational efficiency significantly underperformed. We also found that catch-up effects in the Central and Western regions were progressing in terms of operational efficiency and optimal production technology, respectively. The findings suggest that policy makers and practitioners should focus on TFP growth and its components, drawing the attention of star-rated hotels to upgrade their optimal production technology and enhance their operational efficiency as a means of improving TFP and competitiveness. Lastly, this study advances a new research perspective in efficiency assessment in the hotel industry by considering both financial and service production outputs.

scholarly journals Agglomeration and total factor productivity of China’s textile industry

2021 ◽  
Vol 72 (04) ◽  
pp. 443-448
Author(s):  
ZHANG JIANLEI ◽  
AN NA ◽  
CHENG LONGDI
Keyword(s):  
Growth Rate ◽  
Total Factor Productivity ◽  
Textile Industry ◽  
Annual Growth Rate ◽  
Factor Productivity ◽  
Important Indicator ◽  
Industrial Transfer ◽  
Tfp Growth ◽  
Agglomeration Effect ◽  
North Eastern

Agglomeration is an important characteristic in China’s textile industry development. But regional textile industry isseriously unbalanced, only eastern location entropy (LQ) is greater than 1 and is the highest of all, followed by thecentral, western and north-eastern regions. Total factor productivity (TFP) is an important indicator to measure theeconomic growth efficiency. The average annual growth rate (AAGR) of eastern textile industry TFP is the least andcentral TFP growth rate is the fastest. In order to investigate the relationship between agglomeration and TFP of China’stextile industry, especially at region level, this paper applies panel model to study how agglomeration influences TFPduring 2005–2018. The results show that increasing agglomeration degree restrains the TFP growth of China’s textileindustry. The coefficients of LQ on textile industry in China and four regions are all negative. There exists crowded effectin eastern textile industry. It has not formed the significant agglomeration effect in western and north-eastern textileindustry for very low agglomeration degree. So it implies that eastern textile industry can accelerate the implementationof industrial transfer and structural adjustment to lower agglomeration and maintain sustained profitability of textileenterprises. Western textile industry can strengthen agglomeration by undertaking industrial transfer from eastern regionto form agglomeration effect to promote TFP growth.

Estimation about Contribution from Information Technology in China’s Economy

2011 ◽  
Vol 58-60 ◽  
pp. 304-308
Author(s):  
Shi Li
Keyword(s):  
Economic Growth ◽  
Information Technology ◽  
Growth Rate ◽  
Factor Productivity ◽  
Economic Growth Rate ◽  
Tfp Growth ◽  
Role Of Information ◽  
Production Probability ◽  
China’S Economy

To interpret the role of information technology (IT) in China’s economy, the paper focuses on examining the growth contribution from information technology with Production Probability Frontier and Dual Method during the period from 1980 to 2003. Our results indicate that the Chinese economic growth rate devoted to IT investment has risen steadily since 1990. The contribution share of total factor productivity (TFP) from IT in China has increased sharply after 1995, while the TFP growth rate from non-IT sector dropped at that period.

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