scholarly journals Agricultural basic industry subjects productivity development examination in the region Trnava (SR) by adopting the Malmquist indexes

2010 ◽  
Vol 56 (No. 3) ◽  
pp. 108-115
Author(s):  
P. Bielik ◽  
D. Hupková ◽  
M. Vadovič ◽  
V. Benda
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Future Trend ◽  
Factor Productivity ◽  
Technological Changes ◽  
Efficiency Change ◽  
Basic Industry ◽  
Factors Influencing ◽  
Two Factors ◽  
Malmquist Indexes

Analysis of the productivity and efficiency development could be used to asses the trend and factors influencing this process. The main goal of this paper is estimation of the Total Factor Productivity (TFP) development of agricultural farms in the Trnava region in the period 2002–2006. Results of this analysis could be used to detect the trend in the TFP development. The results of the analysis confirmed there is no evident trend in the average TFP indicators. This could be explained by the variation of technical efficiency change and technological changes during this period. These two factors represent the components of the TFP indicator. According to the present development of the TFP indicator, it is not possible to unambiguously forecast the future trend.

scholarly journals Distinction between Informal and Unorganized Sector: A Study of Total Factor Productivity Growth for Manufacturing Sector in India

2011 ◽  
Vol 3 (5) ◽  
pp. 296-310
Author(s):  
Indrajit Bairagya
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Informal Sector ◽  
Productivity Growth ◽  
Manufacturing Sector ◽  
Total Factor Productivity Growth ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Unorganized Sector

Since its very onset, the concept and definition of the informal sector has been a subject of debate both at the national and international levels. Existing literature uses the terms ‘informal sector’ and ‘unorganized sector’ interchangeably. However, in India, the characteristics of enterprises in the informal and non-informal unorganized manufacturing sectors are different and, thus, it is not justifiable to consider the informal and unorganized sector interchangeably for the manufacturing sector. Thus, the objective of this paper is to test the hypothesis on whether or not the total factor productivity growth (TFPG) of the informal manufacturing sector is different from the non-informal unorganized manufacturing sector. TFPG is decomposed into technical efficiency change and technological change. Later, technical efficiency change is further decomposed by pure efficiency change and scale efficiency change. Results show that the average TFPG of the non-informal sector is higher than the informal sector. The informal sector heavily concentrates in own account small enterprises, whereas the non-informal unorganized sector concentrates only in directory manufacturing enterprises (DME). Due to large in size, DME avails the advantages of economies of scale, which, in turn, helps the units for more growth in terms of total factor productivity growth. The main reason for productivity decrease of the enterprises, besides technology regress and the lack of adequate investments, is the limitation of activities and scale along with the optimal allocation of resources. This study provides a basis on how policies can be designed for enhancing the total factor productivity growth of the informal sector.

AGRICULTURAL TOTAL FACTOR PRODUCTIVITY CHANGES IN THE NEW AND THE OLD EUROPEAN UNION MEMBERS

2015 ◽  
Author(s):  
Tomasz KIJEK ◽  
Anna NOWAK ◽  
Armand KASZTELAN ◽  
Artur KRUKOWSKI
Keyword(s):  
European Union ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Factor Productivity ◽  
Technological Changes ◽  
Total Productivity ◽  
The Eu

The aim of this study was the evaluation of agricultural total factor productivity changes between new member countries which have acceded to EU after 2004 and so-called ‘old 15’ EU members. The analysis covered the years 2007–2013. The study is based on Malmquist productivity index divided into technological change and changes in technical efficiency. The results showed a slight increase in the agricultural total factor productivity in the EU countries in the years 2007–2013 (0.1 %, which mainly resulted from a slight increase in technical efficiency in agriculture(0.4 % ), while at the same time adverse technological changes. Among all the countries of the ‘old 15’, only Denmark, the Netherlands, Finland, United Kingdom and Sweden reported increased index of productivity. In the group of countries that joined the EU after 2004, the total productivity growth took place in such countries as Bulgaria, Cyprus, Czech Republic, Malta, Slovakia and Hungary. The reason for this increase was primarily changes in technical efficiency.

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.

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.

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.

Productivity Changes in Slovenian Urban Municipalities

2014 ◽  
Vol 12 (3) ◽  
pp. 417-429 ◽  
Author(s):  
Primož Pevcin
Keyword(s):  
Technological Change ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Productivity Improvement ◽  
Urban City ◽  
Productivity Indices ◽  
Annual Means

Paper estimates productivity change in 10 Slovenian urban (city) municipalities during the period 2009-2012. Total factor productivity (TFP) change is estimated with input-orientated Malmquist productivity indices, which enables the productivity change to be decomposed into technical efficiency change and into technological change. The results indicate that average TFP annual means decreased by 5.8 % during the report period, and only one urban municipality experienced productivity improvement. Besides, technological regress can be identified as the main source of TFP decrease during the report period.

scholarly journals Measuring the Performance of River Basins: Application of Total Factor Productivity Approach to Andhra Pradesh, India

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
K. Palanisami ◽  
C. R. Ranganathan
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Andhra Pradesh ◽  
River Basins ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Production Frontier ◽  
Agricultural Output ◽  
Tfp Growth

Total Factor Productivity (TFP) measures the growth in output which is not accounted for by inputs. Data Envelopment Analysis which forms the basis for the computation of Malmquist TFP index is used in this paper to study the performance of different river basins in Andhra Pradesh state, India. The results indicated that average technical efficiency of all the basins is only 66%. In all the basins there was a decline in growth of agricultural output during the first two decades, viz., 1979-1980 to 1988-1989 and it had picked in the last decade (1999-2000). All the river basins have TFP change greater than 1 indicating progress in agricultural productivity. Out of the 40 river basins, 14 river basins have technical efficiency change less than 1 indicating decline in TFP growth whereas all the basins have technical changes greater than 1 implying that there is shift in production frontier over years. In general, within the TFP, technical change contributed more than technical efficiency change. Looking at the future options for increasing the agricultural output in the river basins, it is important to focus on improving the TFP growth compared to increasing the quantities of physical inputs.

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

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