Additive Structural Decomposition Analysis and Index Number Theory: An Empirical Application of the Montgomery Decomposition

Abstract

In recent years, a large number of empirical articles on structural decomposition analysis, which aims at disentangling an aggregate change in a variable into its r factors, has been published in this journal. Commonly used methods are the average of the two polar decompositions and the average of all r! elementary decompositions (Dietzenbacher and Los, 1998, D&L). We propose to use instead the 'ideal' Montgomery decomposition, which means that it satisfies the requirement of factor reversal imposed in index number theory. We prefer it to the methods previously mentioned. The average of the two polar decompositions is not 'ideal', so that the outcome depends on the ordering of the factors. The average of all elementary decompositions is 'ideal', but requires the computation of an ever increasing number of decompositions when the number of factors increases. Application to the example of D&L (four factors) shows that the three methods yield results that are close to each other.Additive decomposition, index number theory, structural decomposition analysis,

Decomposing Industrial Energy-Related CO2 Emissions in Yunnan Province, China: Switching to Low-Carbon Economic Growth

“…In this study, changes in the CO 2 emissions have been decomposed into seven factors. De Boer [27] has developed the detailed model on which our work is based by utilizing two factors as an example. By extending De Boer's development in a similar manner to seven factors, we obtain the following: e " cJQLwvgp, where…”

“…However, difficulty in applying the model arises when there is a zero or negative number in the dataset. To solve this problem, we follow the practice of De Boer [27] and replace the zero value in the preliminary dataset with 10´2 9 . For negative numbers, we follow the processing method of Ang and Liu [35].…”

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“…Since the number of factors is seven in this study and the LMDI method is easy to use and has a simple computation in this situation, the LMDI method was selected in the decomposition analysis following the suggestions by Su and Ang [16]. At present, the SDA-LMDI model has been widely used in different countries and regions to analyze the driving forces behind changes in the economic systems [27], energy consumption and energy intensity [28,29] and greenhouse gas (GHG) emissions [30]. All of the previous research indicated that the SDA-LMDI model is applicable to this study.…”

Main drivers of changes in CO2 emissions in the Spanish economy: A structural decomposition analysis

“…= 720 (Dietzenbacher & Los, 1998). (Boer, 2008) uses the Montgomery's decomposition. However, the main disadvantage is the high number of calculations that are needed.…”