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

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.

2022 ◽  
pp. 1-23
Author(s):  
Noor Aini Khalifah

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.


2020 ◽  
Vol 8 (6) ◽  
pp. 2168-2173

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.


2020 ◽  
Vol 4 (2) ◽  
pp. 1-1
Author(s):  
Maha Kalai ◽  
Kamel Helali

In this study, we use the stochastic frontier production approach to split the total productivity growth sources into technical progress and technical efficiency changes of the economic sectors in Tunisia between 1961 and 2014. Based on the sectors’ evolution, the analysis is centred on the technological progress trend, the technical efficiency change, and the role of productivity change in the economic growth. The empirical results show that the production factors have a significant effect on productivity. The review of the total factor productivity growth sources reveals that the contribution of technological progress is the main source of this growth.


2013 ◽  
Vol 12 (2) ◽  
pp. 127
Author(s):  
Gerhardus Van der Westhuizen

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.


2014 ◽  
Vol 12 (3) ◽  
pp. 417-429 ◽  
Author(s):  
Primož Pevcin

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.


2010 ◽  
Vol 56 (No. 3) ◽  
pp. 108-115
Author(s):  
P. Bielik ◽  
D. Hupková ◽  
M. Vadovič ◽  
V. Benda

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.


2011 ◽  
Vol 3 (5) ◽  
pp. 296-310
Author(s):  
Indrajit Bairagya

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.


2008 ◽  
Vol 40 (01) ◽  
pp. 137-149 ◽  
Author(s):  
Nigel Key ◽  
William McBride ◽  
Roberto Mosheim

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.


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