Productivity Analysis of Coal-fired Thermal Power Plants in India Using Malmquist Index Approach

2019 ◽  
Vol 21 (6) ◽  
pp. 1338-1353
Author(s):  
Amritpal Singh Dhillon ◽  
Hardik Vachharajani
Keyword(s):  
Total Factor Productivity ◽  
Power Plants ◽  
Thermal Power ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Tfp Growth

The sustainable socio-economic growth of any country depends on the availability of adequate and reliable power at reasonable rates. This is even true in case of a rapidly developing country like India where coal-based power plants account for the majority of electricity generation. Making use of data envelopment analysis (DEA) and Malmquist productivity index (MPI), this study analyses the productivity change of coal-fired power plants during 2002–2012. Productivity change is further decomposed into technical efficiency change (EFFCH), technological change (TECHCH), scale efficiency change (SECH), pure technical change (PECH) and total factor productivity change (TFPCH). The study revealed that 0.70 per cent of average annual total factor productivity (TFP) growth was witnessed from 2002–2003 to 2011–2012 indicating overall progress. The contribution of TECHCH in TFP growth is positive, that is, 1.3 per cent per annum. It demonstrates that expansion of the efficient frontier. However, there was a decrease in technical EFFCH of −0.6 per cent per year, indicating the adverse sign of progress. Plants in the central sector achieved maximum growth of 4.6 per cent annually. A total of 54.05 per cent of plants have recorded negative TFP growth. Power plants between 500 and 999 MW achieved the highest operational performances in all indices except SECH.

scholarly journals ANALISIS TINGKAT EFISIENSI SEKTOR PARIWISATA DI JAWA TENGAH TAHUN 2017 DAN 2019

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Yennie Glorya Panjaitan ◽  
Edy Yusuf Agung Gunanto
Keyword(s):  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Efficiency Change

Sektor pariwisata sebagai salah satu sektor yang diandalkan bagi penerimaan daerah maka pemerintah Provinsi Jawa Tengah dituntut untuk dapat menggali dan mengelola potensi wisata yang dimiliki. Penelitian ini bertujuan untuk menganilisis tingkat efisiensi dan produktivitas pada sektor pariwisata di Jawa Tengah antara tahun 2017 dan 2019 dengan sampel 35 Kabupaten/Kota. Analisis dilakukan dengan menggunakan konsep efisiensi yang didasarkan pada teori produksi, pengukuran nilai efisiensi dan produktivitas diperoleh menggunakan metode analisis Data Envelopment Analysis (DEA) dan Malmquist Productivity Index (MPI). Asumsi yang digunakan adalah variable return to scale (VRTS) dan model orientasi output (output oriented). Dengan variable input objek wisata, restoran dan rumah makan, biro perjalanan wisata dan jumlah hotel bintang serta melati. Variabel output dalam penelitian ini adalah wisatawan dan pendapatan sektor pariwisata. Hasil akhir penelitian menunjukkan bahwa terdapat 16 Kabupaten/Kota (45,8%) di tahun 2017, 18 Kabupaten/Kota (51,4%) di tahun 2019 yang mencapai efisiensi teknis penuh. Total Factor productivity change mengindikasikan bahwa 22 Kabupaten/Kota (62,8%) mendekati frontier baik pada frontier produksi maupun frontier efisiensi dan dari scale efficiency change mengindikasikan bahwa terdapat 17 Kabupaten/Kota (48,57%) mengalami perbaikan efisiensi teknis selama periode 2017 ke 2019.

scholarly journals Measurement of Total Factor Productivity in Commercial Airlines Sector using Malmquist Index and Data Envelopment Model

2020 ◽  
Vol 8 (6) ◽  
pp. 2168-2173
Keyword(s):  
Panel Data ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Year 2013

This study attempts to measure productivity change of Airlines companies in private and public sector in India for a period of four years (2011-2016). In this study the nature and productivity change is probed using the Malmquist Productivity Index. This index has the constituents which are used to measure the performance in terms of change in Scale Efficiency, change in Technical Efficiency, change in Technological Change and Total Factor Productivity. The paper compares efficiencies for the companies in public and private commercial airlines sector in India. Five Airlines companies are included in the study. The research includes Total Annual Income as an output variable and Total Expenditure, Employee Compensation, Sales & Distribution Expenditure and Marketing expenses as Input variables. A panel data with 30 observations has been used for analysis. The panel data is used to arrive to MPI estimates, with a total of five commercial airlines companies in India. The Total Factor Productivity change in the airlines sector depends upon the change in the efficiency and productivity of the companies. From the study it is evident that the Total Factor Productivity change has not changed significantly over the last six years for all the companies under study. The Technical Efficiency was the highest in the year 2013-14 which then dropped in the subsequent year. The Total Factor Productivity change is mainly due to change in scale efficiency of the companies since the pure efficiency has shown no significant change during the period under study. The Total Factor Efficiency dropped by almost 50% in the case of Air India in the year 2015-16. This drop is attributed to the deterioration in the technical efficiency of the company. The overall Total Factor Productivity of Air India is the highest. This can be attributed to positive change in the company’s Technical Efficiency especially in the year 2013-14. It is evident that all the airlines companies under study have not emphasized on improving scale efficiency as well as pure efficiency. These companies can improve their overall productivity by bringing in efficiency in the scale of operations as well as focus on improving efficiency on factors other than scale of operations. The commercial airlines companies in India need to improve their scale efficiency and pure efficiency to improve their total factor productivity.

Measuring total factor productivity change of microfinance institutions in India using Malmquist productivity index

2018 ◽  
Vol 12 (1) ◽  
pp. 105-130 ◽  
Author(s):  
Dilip Ambarkhane ◽  
Ardhendu Shekhar Singh ◽  
Bhama Venkataramani
Keyword(s):  
Total Factor Productivity ◽  
Financial Institutions ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Factor Productivity ◽  
Content Type ◽  
The Poor

PurposeMicrofinance institutions (MFIs) provide small loans and other financial services to the poor. These institutions are established for helping the poor to raise income levels and to reduce poverty. Recently, MFIs are required to reduce their dependence on grants and subsidies. Consequently, they face conflicting objectives of improving reach and profitability. These can be achieved by improving productivity. This paper aims to investigate productivity change in 21 major MFIs in India which are rated by Credit Rating and Information Services of India Limited in 2014.Design/methodology/approachThis paper attempts to examine total factor productivity change in 21 major Indian MFIs during the period from 2014 to 2016 using Malmquist productivity index. The inputs and outputs are selected considering objectives of outreach and financial sustainability. The authors have categorized MFIs in three categories, namely, large, medium and small, depending on asset size.FindingsIt is revealed that large MFIs are able to catch up with industry best practices by improving their systems and processes, but they need to improve scale efficiency. The Reserve Bank of India has recently initiated a policy of granting banking licenses to those financial institutions which have good outreach and are financially strong. It can be used for shortlisting MFIs before granting permission to operate as banks. The method can also be used for benchmarking them for productivity. It can also be replicated in other countries.Originality/valueIn India, MFIs are playing important role in economic development by providing microcredit to the poor. However, very few studies have been undertaken regarding productivity of MFIs in India. The present study intends to fill this gap. It will facilitate benchmarking of MFIs as competitive and sustainable financial institutions catering to the requirements of small borrowers.

scholarly journals Bank Productivity And Sources Of Efficiency Change: A Case Of The Four Largest Banks In South Africa

2013 ◽  
Vol 12 (2) ◽  
pp. 127
Author(s):  
Gerhardus Van der Westhuizen
Keyword(s):  
South Africa ◽  
Technological Change ◽  
South African ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change

The Malmquist productivity index was utilised to estimate the total factor productivity and productivity change of the four largest banks in South Africa for the period 1994 to 2010. Total factor productivity change can be decomposed into efficiency change and technological change, which allow for determining the sources of total factor productivity change. Various changes in the South African banking scene impacted on the average productivity of the banks. The four banks experienced, on average, regress in total factor productivity as well as regress in technological change, the latter indicating a lack of innovation. The four banks operated, on average, in the proximity of fully technical efficiency. For various reasons, South Africa still has a large unbanked community.

scholarly journals Productivity Change of Microfinance Institutions in Bosnia and Herzegovina

2019 ◽  
Vol 14 (2) ◽  
pp. 23-33
Author(s):  
Velid Efendić ◽  
Nejra Hadžiahmetović
Keyword(s):  
Total Factor Productivity ◽  
Bosnia And Herzegovina ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Microfinance Institutions ◽  
Factor Productivity ◽  
Strategic Objectives ◽  
Average Decrease ◽  
Recent Financial Crisis

Abstract The main aim of this paper is to investigate the productivity changes of microfinance institutions (MFIs) in Bosnia and Herzegovina (BiH) during and after the recent financial crisis. The study covers the period starting from 2008 until 2015. Using the Malmquist Productivity Index (MPI) over the sample of 10 MFIs and a balanced panel dataset of 80 observations, this study explores technical and technological change as well as total factor productivity (TFP) change. The empirical findings indicate a decline in TFP in most of the analyzed periods with an average decrease of 2.5%. The study reveals an average technological decline in the industry of 1.7%, while technical efficiency change is recorded at the level of -0.8%. Overall, crisis efficiency recovery occurred during the period between 2009 and 2013. However, due to technological inefficiencies, average total factor productivity change remains negative. Hence, policy makers need to enhance the technological progress in order to meet their strategic objectives in BiH MFIs.

Evaluation analysis on industrial green total factor productivity and energy transition policy in resource-based region

2021 ◽  
pp. 0958305X2110054
Author(s):  
Yaozu Xue
Keyword(s):  
Environmental Pollution ◽  
Total Factor Productivity ◽  
Pollution Control ◽  
Energy Transition ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Shanxi Province ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Positive Effect

Based on the input-output panel data of industrial sectors of Shanxi Province, which is the only province-wide resource-based region in China, this paper uses the non-parametric DEA model and the Malmquist productivity index to construct the DEA-Malmquist model for evaluation analysis of the green total factor productivity (GTFP) and its decomposition value of the sub-sector, and then through fixed effect panel regression model studies the ways of energy transition of the SDG’s. The results show that the technological progress index has the greatest contribution to the growth of GTFP, while the scale efficiency index has the lowest contribution. And the amount of investment in environmental pollution control has a significant positive relationship with the GTFP of the three major polluting industries in Shanxi. Among them, investment in environmental pollution control has the greatest positive effect on the GTFP of light polluting industries characterized by high technology, high added value and low emissions; investment in environmental pollution control has the largest positive effect on the GTFP of heavy polluting industries with heavy chemical industry and pollution-intensive industries; investment in environmental pollution control has the weakest positive effect on the GTFP of the medium polluting industries that manufacture life service products and some heavy industrial products. Based on these results, the paper puts forward effective policy for the energy transition of resource-based regions.

scholarly journals Decomposition of Total Factor Productivity Change in the U.S. Hog Industry

2008 ◽  
Vol 40 (01) ◽  
pp. 137-149 ◽  
Author(s):  
Nigel Key ◽  
William McBride ◽  
Roberto Mosheim
Keyword(s):  
Total Factor Productivity ◽  
Growth Rates ◽  
Farm Size ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Factor Productivity ◽  
Hog Industry ◽  
Frontier Analysis ◽  
Tfp Growth ◽  
The U.S

The U.S. hog industry has experienced dramatic structural changes and rapid increases in farm productivity. A stochastic frontier analysis is used to measure hog enterprise total factor productivity (TFP) growth between 1992 and 2004 and to decompose this growth into technical change and changes in technical efficiency, scale efficiency, and allocative efficiency. Productivity gains over the 12-year period are found to be explained almost entirely by technical progress and by improvements in scale efficiency. Differences in TFP growth rates in the Southeast and Heartland regions were found to be explained primarily by differences in farm size growth rates.

scholarly journals Total factor productivity of manufacturing sector in India: A regional analysis for the State of Haryana

2012 ◽  
pp. 97-118
Author(s):  
Shallu Sehgal ◽  
Suparn Sharma
Keyword(s):  
Total Factor Productivity ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Regional Analysis ◽  
The State ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Liberalization Policy

Using pooled data for the period of 1981-82 to 2007-08 for different categories of organized sector’s manufacturing industries for the sample state of Haryana, the present undertaking seeks to analyze the inter-temporal and inter-industry comparison of total factor productivity (TFP) measured by Malmquist productivity index (MPI), which is an application of DEA to panel data to calculate the indices of TFP change, technology change, efficiency change. The general development pattern observed by the Haryana is definitely not a healthy sign of structural change in the economy. The analysis of the discussion reflects that while the tertiary sectors have maintained its lion’s share in GDP of India and Haryana as well, the declining trend in the share of primary sector and more or less stable contribution of the secondary sector is noticeable. The study reveals that technical efficiency change is the key driver of TFPG in the manufacturing sector of Haryana during pre reforms period, however, the picture has turned around during the post reforms period. A positive impact of liberalization policy on technological advancement of the manufacturing sector of the state has been experienced. But, during the post reforms period the state has realized inefficiency in the utilization of resources in hand and it is really an alarming sign indicating that the incapability of manufacturing sector of the state in question to cope up with the technological advancement.DOI: http://dx.doi.org/10.3126/ejdi.v13i0.7213 Economic Journal of Development Issues Vol.13 & 14 2011, pp.97-118

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