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Fuentes, Hugo J.; Grifell‐Tatjé, Emili; Perelman, Sergio

doi:10.1023/a:1007852020847

Cited by 116 publications

(14 citation statements)

References 28 publications

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“…On the other hand, the groupfrontier technological change index and technological catch-up index can be estimated following the framework introduced by Fuentes et al [58]:…”

Section: Model Specification and Estimationmentioning

confidence: 99%

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

2017

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Under the concept of metafrontier, technology gap ratio is alternatively interpreted as potential energy efficiency. Combined with Malmquist index framework and Shephard energy distance function, we then develop a metafrontier Malmquist energy productivity index to analyze the total-factor energy productivity growth with four specific components: groupfrontier efficiency change index, groupfrontier technological change index, efficiency catch-up index and technological catch-up index. Methodologically, a newly developed two-step stochastic metafrontier analysis is applied to address the potentially biased estimation problems in the previous mixed approach. This analytical framework is used to evaluate the energy productivity growth of China's 35 sub-industries in industrial sector from 2001 to 2015. The main empirical results show that: (1) In terms of cumulative metafrontier Malmquist energy productivity growth, China's overall industry has witnessed a 25% growth and a U-shaped growing trend bottoming out in 2006; meanwhile, 19 sub-industries have suffered an energy productivity loss and the remaining 16 sub-industries have experienced an energy productivity gain. (2) From the technology heterogeneity perspective, light industry outperforms heavy industry in metafrontier Malmquist energy productivity growth, while the intra-group and inter-group energy productivity develops roughly in balance for overall industry. (3) The change of metafrontier Malmquist energy productivity is mainly driven by technological change components rather than efficiency change components. On average, groupfrontier technological change makes the biggest contribution to energy productivity growth, followed by technological catch-up, then efficiency catch-up, and groupfrontier efficiency change is last. (4) The metafrontier Malmquist energy productivity growth has shown a significant convergence in heavy industry and light industry, as well as overall industry.

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“…On the other hand, the groupfrontier technological change index and technological catch-up index can be estimated following the framework introduced by Fuentes et al [58]:…”

Section: Model Specification and Estimationmentioning

confidence: 99%

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

2017

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“…The distance functions involved in Eqs (6)- (7) can be approximated by employing SFA (Fuentes et al 2001;Coelli et al 2005). Let t and s denote the two adjacent time periods.…”

Section: Total Factor Productivity Changementioning

confidence: 99%

Stochastic Production Frontier for the Lithuanian Family Farms

Baležentis

2016

Journal of Business Economics and Management

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Abstract. The research follows neo-classical methodology to analyse the trends of the agricultural efficiency. The paper fits the stochastic production frontier to the micro data describing the performance of the Lithuanian family farms during [2004][2005][2006][2007][2008][2009] in order to define the current trends of efficiency and productivity in the sector. Indeed, this is the first application of stochastic frontiers to gauge the performance of Lithuanian family farms. The technical efficiency of the Lithuanian family farms fluctuated around 80%. The analysis confirmed that the livestock farms were peculiar with higher mean technical efficiency if compared to that of mixed or crop farms. The estimated partial output elasticities imply that the intermediate consumption was the most productive factor, whereas assets were four to six times less productive depending on the farming type. The land factor was peculiar with the lowest partial output elasticities. The research contributes to the wider discussion on the patterns of efficiency and productivity in a transition European Union Member States following the accession.

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“…The predicted value of the output distance function can be estimated as a conditional expectation Once the parameters of Equation (17) are estimated, the assembling parts of the Malmquist productivity index and its components can be calculated ( [4,8]). …”

Section: Parametric Estimation Of the Malmquist Indexmentioning

confidence: 99%

“…The parametric approach provides a solution to address the shortcomings of nonparametric techniques and has been adopted by some recent studies in the estimation of the Malmquist index ( [4,8,9]). In the parametric approach, the Malmquist index is not directly obtained through the estimation of distance functions under differrent returns to scale technologies.…”

Section: Introductionmentioning

confidence: 99%

“…In the parametric approach, the Malmquist index is not directly obtained through the estimation of distance functions under differrent returns to scale technologies. Instead the Malmquist index and its components are calculated based on the fitted distance function with globally variable returns to scale, evaluated at adjacent time periods' input and output quantity, as implemented by [4,[8][9][10]. In addition to statistical testing, the parametric approach has the advantages of accommodating random errors and enabling different interactions between outputs and inputs if a flexible functional form is chosen to closely approximate the underlying production technology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parametric Decomposition of the Malmquist Index in Output-Oriented Distance Function: Productivity in Chinese Agriculture

Yu¹,

Liao²,

Hongfang³

2014

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This paper decomposes the Malmquist productivity index into several assembling components: technical change (further break down into technical change magnitude, input bias and output bias), technical efficiency change, scale efficiency change, and output-mix effect. A translog output distance function is chosen to represent the production technology and each component of the Malmquist index is computed using the estimated parameters. This parametric approach allows us to statistically test the hypothesis regarding different components of the Malmquist index and the natural of production technology. The empirical application in Chinese agriculture shows that the average productivity grows at 2 percent per year during 1978-2010. This growth is mostly driven by technical change, which is found to be neutral.

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Untitled

Cited by 116 publications

References 28 publications

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

Stochastic Production Frontier for the Lithuanian Family Farms

Parametric Decomposition of the Malmquist Index in Output-Oriented Distance Function: Productivity in Chinese Agriculture

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