On the inconsistency of the Malmquist–Luenberger index

Aparicio, Juan; Pastor, Jesús T.; Zofío, José L.

doi:10.1016/j.ejor.2013.03.031

Cited by 75 publications

(39 citation statements)

References 18 publications

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“…This could help management team detect any possible shortcoming in the system and provide possible solution strategies. The results of this survey are consistent with other existing works in the literature (Aparicio et al, 2013;Ahn & Min, 2014;Gharneh et al, 2014;Kerstens & Van de Woestyne, 2014).…”

Section: Discussionsupporting

confidence: 82%

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Productivity measurement using DEA and Malmiquest index

Khosravi¹,

Najafi²,

Rafiei³

2015

10.5267/j.msl

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Performance measurement is one of the primary tasks in every organization and it helps organization setup appropriate targets for future. There are several ways to assess the performance of the organization, and data envelopment analysis (DEA) is one of the most important methods. DEA is an appropriate technique to measure the efficiency of decisionmaking units, but does not provide complete information about the unit's productivity. Malmquist productivity index is one of the methods for measuring productivity and the main advantage of this index is the possibility of the productivity changes over time. In this study, DEA is used to measure service performance and economic foundation so that the performance of four companies within a 3-month period were evaluated. In this work, the cost of the system and investment were used as inputs, revenue and profit were considered as the output and constant returns to scale (CCR) was used to analyze the system, which yields 0.9 for all units when all input/output data were considered in crisp form. The efficacy of different units using interval data also showed different productivity compared with the crisp model. The productivity measures of the companies in three-month period were studied using the Malmquist based productivity index and the results were analyzed.Growing Science Ltd. All rights reserved. 5

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Section: Discussionsupporting

confidence: 82%

“…There are several applications of Malmquist productivity index (Aparicio et al, 2013;Kerstens & Van de Woestyne, 2014). Woo et al (2015) investigated the environmental efficiency of renewable energy from the static as well as the dynamic perspective in 31 OECD countries to learn more about the effect of renewable energy across countries.…”

Section: Introductionmentioning

confidence: 99%

Productivity measurement using DEA and Malmiquest index

Khosravi¹,

Najafi²,

Rafiei³

2015

10.5267/j.msl

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“…However, the geometric mean form of the MLPI has some drawbacks: it is not circular or transitive and it may face problems of spurious technical regress and linear programming infeasibility when measuring cross-period DDFs [32,44]. Based on this, a global benchmark technology should be defined as P G = P 1 ∪ P 2 ∪ P 3 .…”

Section: Ddf and Gml Productivity Indexmentioning

confidence: 99%

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

et al. 2018

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This paper employs directional distance function (DDF) and the global Malmquist-Luenberger (GML) productivity index to measure the green total factor productivity (GTFP) growth of China's 36 industrial sectors from 2000 to 2014. Based on this, this paper ascertains the determinants of GTFP from the perspectives of institution, technology, and structure, and the determinant factors that affect GTFP are empirically tested by a dynamic panel data (DPD) model. The research shows that, considering energy consumption and environmental undesirable outputs, the industrial GTFP goes backwards by 0.02% per year on average, and the contributions of GTFP to output growth are far from the target value of 50% in all industrial sectors, which indicates that the growth of industrial economy sacrifices resources and environment to a certain degree. In terms of the determinant factors of GTFP, environmental regulation does improve the GTFP, while environmental regulation is difficult to promote GTFP by the route of technological innovation. Compared with technology importation, the driving effect of independent research and development on GTFP is obvious, especially promoting the GTFP of moderately and lightly polluting industries, while the driving effect in heavily polluting industries is poor. Endowment structure and property right structure play a positive role in improving GTFP, but the impacts of capital structure and energy structure on GTFP are poor.

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“…The Malmquist-Luenberger productivity index (MLPI), which is introduced by Chung, Färe and Grosskopf [6] and calculated by using the estimated DDF scores, is widely used to measure productivity change or green TFP because it incorporates undesirable output production [3], though it has an inconsistency problem [19]. Chen and Golley [3] used MLPI to evaluate the changing patterns of "green" TFP growth of 38 Chinese industrial sectors during the period 1980-2010.…”

Section: Literaturementioning

confidence: 99%

Green Development Performance in China: A Metafrontier Non-Radial Approach

Song

2016

Sustainability

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This paper proposes a green development growth index (GDGI) for measuring the changes in sustainable development over time. This index considers a wide range of pollutants, and allows for the incorporation of group heterogeneity and non-radial slack in the conventional green development index. The GDGI is calculated based on a non-radial directional distance function derived by several data envelopment analysis (DEA) models, and was decomposed into an efficiency change (EC) index, a best-practice gap change (BPC) index and a technology gap change (TGC) index. The proposed indices are employed to measure green development performance in 30 provinces in China from 2000 to 2012. The empirical results show that China has a low level of green development, with a 2.58% increase per year driven by an innovation effect. China's green development is mainly led by the eastern region, and the technology gaps between the eastern region and the other two regions (the central and western regions) have become wider over the years. The group innovative provinces have set a target for resource utilization of non-innovative provinces in order to catch-up with the corresponding groups, while the metafrontier innovative provinces provide targets for the technology levels of other provinces to improve their green development performance.

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On the inconsistency of the Malmquist–Luenberger index

Cited by 75 publications

References 18 publications

Productivity measurement using DEA and Malmiquest index

Productivity measurement using DEA and Malmiquest index

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

Green Development Performance in China: A Metafrontier Non-Radial Approach

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