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

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  • Paul De Boer

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.

Suggested Citation

  • Paul De Boer, 2008. "Additive Structural Decomposition Analysis and Index Number Theory: An Empirical Application of the Montgomery Decomposition," Economic Systems Research, Taylor & Francis Journals, vol. 20(1), pages 97-109.
  • Handle: RePEc:taf:ecsysr:v:20:y:2008:i:1:p:97-109
    DOI: 10.1080/09535310801892066
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Choi, Ki-Hong & Ang, B.W., 2012. "Attribution of changes in Divisia real energy intensity index — An extension to index decomposition analysis," Energy Economics, Elsevier, vol. 34(1), pages 171-176.
    2. Jialing Zou & Weidong Liu & Zhipeng Tang, 2017. "Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012," Sustainability, MDPI, Open Access Journal, vol. 9(3), pages 1-14, March.
    3. repec:eee:ecolec:v:142:y:2017:i:c:p:163-176 is not listed on IDEAS
    4. Mingxiang Deng & Wei Li & Yan Hu, 2016. "Decomposing Industrial Energy-Related CO 2 Emissions in Yunnan Province, China: Switching to Low-Carbon Economic Growth," Energies, MDPI, Open Access Journal, vol. 9(1), pages 1-19, January.
    5. de Boer, P.M.C., 2008. "Energy decomposition analysis: the generalized Fisher index revisited," Econometric Institute Research Papers EI 2008-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. repec:eee:energy:v:151:y:2018:i:c:p:748-759 is not listed on IDEAS
    7. Edward, T. Huw & Lücke, Matthias, 2011. "Decompositions of wage inequality and growth in an advanced economy open to trade," Kiel Working Papers 1749, Kiel Institute for the World Economy (IfW).
    8. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    9. repec:gam:jeners:v:9:y:2016:i:1:p:23:d:61618 is not listed on IDEAS
    10. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    11. repec:eee:enepol:v:107:y:2017:i:c:p:585-599 is not listed on IDEAS
    12. Saari, M. Yusof & Dietzenbacher, Erik & Los, Bart, 2015. "Sources of Income Growth and Inequality Across Ethnic Groups in Malaysia, 1970–2000," World Development, Elsevier, vol. 76(C), pages 311-328.

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