scholarly journals Components of Productivity Growth of the Manufacturing Industries of Petroleum and Coal Products in India: An Interstate Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 40-50
Author(s):  
Prasanta Kumar Roy
Keyword(s):  
Productivity Growth ◽  
Technological Progress ◽  
Statistical Tests ◽  
Stochastic Frontier ◽  
Manufacturing Industries ◽  
Efficiency Change ◽  
Allocation Efficiency ◽  
Scale Change ◽  
Reform Period ◽  
Time Invariant

The study applies stochastic frontier approach to estimate and decompose the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries of petroleum and coal products in fifteen major industrialized states in India as well as in All-India during the period from 1981-82 to 2010-11, during the entire period, during the pre-reform period (1981-82 to 1990-91) and post-reform period (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. The components of TFPG are: technological progress (TP), technical efficiency change (TEC), economic scale change (SC) and allocation efficiency change (AEC). According to the estimated results, technological progress (TP) is the major contributing factor to TFPG of the organized manufacturing industries of petroleum and coal products in India and in its fifteen major industrializes states during 1981-82 to 2010-11. Further, TFPG of the 2-digit manufacturing industries of petroleum and coal products in India and in its fifteen major industrialized states declined during the post-reform period and the decline in TFPG of these 2-digit industries during that period is mainly accounted for by the decline in TP of the same during that period. However, allocation efficiency change (AEC) and economic scale change (SC) of them remain very negligible or even negative too in many states under study. Further, TEC of them remain unchanged or it is time invariant in nature as statistical tests suggest. So that increase in the combined effect of AEC and SC of them could not offset the decrease in their TP during that period. As a result TFPG of the 2-digit manufacturing industries of petroleum and coal products declined in India and its fifteen major industrialized states during the post-reform period.

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.

Decomposition of Output and Productivity Growth in the Organised Manufacturing Industries in India: An Inter-State Analysis

2018 ◽  
Vol 66 (1-2) ◽  
pp. 25-41
Author(s):  
Prasanta Kumar Roy
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Output Growth ◽  
Manufacturing Industries ◽  
Growth Effect ◽  
Major Contributor ◽  
Factor Productivity ◽  
Reform Period

This article examines and applies the theoretical foundation of the decomposition of output and total factor productivity growth (TFPG) of the aggregate manufacturing industries in 15 major industrialised states in India as well as in all-India during the period from 1981–1982 to 2010–2011, during the entire period, during the pre-reform period (1981–1982 to 1990–1991) and post-reform period (1991–1992 to 2010–2011), and also during two different decades of the post-reform period, that is, during 1991–1992 to 2000–2001 and 2001–2002 to 2010–2011. Output growth is decomposed into input growth effect and TFPG where the three attributes of TFPG are adjusted scale effect, technological progress (TP) and technical efficiency change. A stochastic frontier model with a translog production function is used to estimate the growth attributes of output and total factor productivity (TFP). The empirical results show that input growth is the major contributor to output growth, whereas TP is found to be the major contributor to TFPG and the decline in TFPG of the organised manufacturing sector in India and in its major industrialised states during the post-reform period is mainly due to the decline in TP of the same during that period. JEL Codes: C23, D24, L6, O47

TFP growth, technological progress and efficiencies change

2011 ◽  
Vol 60 (4) ◽  
pp. 360-371 ◽  
Author(s):  
Mahamat Hamit‐Haggar
Keyword(s):  
Productivity Growth ◽  
Technological Progress ◽  
Labour Productivity ◽  
Stochastic Frontier ◽  
Manufacturing Industries ◽  
Production Model ◽  
Content Type ◽  
Tfp Growth ◽  
Canadian Manufacturing ◽  
Productivity Gap

PurposeThe purpose of this paper is to apply a stochastic Frontier production model to Canadian manufacturing industries, to investigate the sources of total factor productivity (TFP) growth. As productivity (growth) appears to be the single most important determinant of a nation's living standard or its level of real income over long periods of time, it is important to better understand the sources of productivity growth. In Canada, TFP growth is the major contributing factor (relative to changes in capital intensity) to labour productivity growth, particularly in manufacturing sector. However, the TFP gap is also the main source of labour productivity gap between Canada and other industrialized (Organization for Economic Co‐operation and Development) countries in recent years.Design/methodology/approachIn this paper, a stochastic Frontier production model is applied to Canadian manufacturing industries to investigate the sources of TFP growth. Using a comprehensive panel data set of 18 industries over the period 1990‐2005 and the approach proposed by Kumbhakar et al. and Kumbhakar and Lovell, TFP growth is decomposed into technological progress (TP), changes in technical efficiency, changes in allocative efficiency and scale effects.FindingsThe decomposition reveals that during the period under study, TP has been the main driving force of productivity growth, while negative efficiency changes observed in certain industries have contributed to reduce average productivity growth. In addition, the empirical results show that research and development expenditure, information and communications technology investment, as well as trade openness exert a positive impact on productivity growth through the channel of efficiency gains.Originality/valueThe author argues that the decomposition carried out in this study may be very helpful to elicit the correct diagnosis of Canada's productivity problem and develop effective policies to reverse the situation, thereby reducing Canada's lagging productivity gap.

Technical efficiency and input-driven growth in Indian engineering goods industry during post-reform period: stochastic frontier approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vasim Akram ◽  
Asheref Illiyan
Keyword(s):  
Technical Efficiency ◽  
High Performance ◽  
Stochastic Frontier ◽  
Capacity Utilization ◽  
Output Growth ◽  
Infrastructure Development ◽  
Efficiency Change ◽  
Stochastic Frontier Approach ◽  
Content Type ◽  
Reform Period

PurposeThe purpose of this study is to examine the performance of Indian engineering goods industry by measuring the technical efficiency and input-driven growth.Design/methodology/approachThe study used the panel data of six firms from the period of 1991–92 to 2014–15 compiled from Annual Survey of Industries (ASI), India and output-oriented econometric techniques such as pooled OLS model, and stochastic frontier approach has been applied to measure the technical efficiency.FindingsThe results suggest that the prime sources of high performance in engineering goods industry, which has recorded 8.8% output growth, are primarily contributed by inputs driven growth (8.2%) during the post-reform period, while the effect of technological change is minimal (0.1%) and technical efficiency change is negative (−0.2%). It was due to sluggishness, outdated technology and underutilization of resources in Indian economy.Research limitations/implicationsThis research paper is limited to engineering goods industry based on concorded macro data. The recommendations are that India should pursue policies and programs which may focus on technology acquisition, skill enhancement of labor, better capacity utilization, R&D and infrastructure development that may augment the technical change and technical efficiency change of the sector.Originality/valueThis research provides robust and significant estimates of technical efficiency and adds valuable insights to the existing literature by identifying the potential areas that improves the performance of Indian engineering goods industry.

scholarly journals Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models

2015 ◽  
Vol 44 (1) ◽  
pp. 124-148 ◽  
Author(s):  
Magnus A. Kellermann
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Unobserved Heterogeneity ◽  
Statistical Tests ◽  
Stochastic Frontier ◽  
Total Factor Productivity Growth ◽  
Factor Productivity ◽  
Nested Models ◽  
Relative Contribution ◽  
Stochastic Frontier Models

This study examines in an empirical comparison how different econometric specifications of stochastic frontier models affect the decomposition of total factor productivity growth. We estimate nine stochastic frontier models, which have been widely used in empirical investigations of sources of productivity growth. Our results show that the relative contribution of components to total factor productivity growth is quite sensitive to the choice of econometric model, which points to the need to select the “right” model. We apply various statistical tests to narrow the range of applicable models and identify additional criteria upon which to base the choice of non-nested models.

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