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Multiplicative decomposition and index number theory: an empirical application of the Sato-Vartia decomposition

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  • de Boer, P.M.C.

Abstract

In De Boer (2006) the additive decomposition of the aggregate change in a variable into its factors was considered. I proposed to use the "ideal" Montgomery decomposition, developed in index number theory, rather than the commonly used methods in structural decomposition analysis and applied it to the example analyzed by Dietzenbacher and Los (1998) (D&L). In this paper I consider the multiplicative decomposition and argue that from a theoretical point of view the "ideal" Sato-Vartia decomposition is to be preferred to the geometric average of the polar decompositions and that from a computational point of view it is to be preferred to the geometric average of all elementary decompositions. Application to the example of D&L reveals that the three methods yield results that are very close to each other.

Suggested Citation

  • de Boer, P.M.C., 2007. "Multiplicative decomposition and index number theory: an empirical application of the Sato-Vartia decomposition," Econometric Institute Research Papers EI 2007-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:10091
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    References listed on IDEAS

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    1. Erik Dietzenbacher & Bart Los, 1998. "Structural Decomposition Techniques: Sense and Sensitivity," Economic Systems Research, Taylor & Francis Journals, vol. 10(4), pages 307-324.
    2. de Boer, P.M.C., 2006. "Structural decomposition analysis and index number theory: an empirical application of the Montgomery decomposition," Econometric Institute Research Papers EI 2006-39, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Mark De Haan, 2001. "A Structural Decomposition Analysis of Pollution in the Netherlands," Economic Systems Research, Taylor & Francis Journals, vol. 13(2), pages 181-196.
    4. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, May.
    5. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
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    Cited by:

    1. Hong, Jae Pyo & Byun, Jeong Eun & Kim, Pang Ryong, 2016. "Structural changes and growth factors of the ICT industry in Korea: 1995–2009," Telecommunications Policy, Elsevier, vol. 40(5), pages 502-513.
    2. Martin Lábaj & Karol Morvay & Martin Hudcovský, 2015. "Labour Elasticity in V4 countries: Structural decomposition analysis," Department of Economic Policy Working Paper Series 009, Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava.
    3. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    4. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    5. Daniel Dujava, 2012. "Príčiny zaostávania nových členských krajín EÚ: empirická analýza na základe Montgomeryho dekompozície [Causes of Lagging Behind of New Member States of EU: Empirical Analysis by Montgomery Decompo," Politická ekonomie, Prague University of Economics and Business, vol. 2012(2), pages 222-244.
    6. 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.
    7. Ling Yang & Michael L. Lahr, 2019. "The Drivers of China’s Regional Carbon Emission Change—A Structural Decomposition Analysis from 1997 to 2007," Sustainability, MDPI, Open Access Journal, vol. 11(12), pages 1-1, June.
    8. Cazcarro, Ignacio & Duarte, Rosa & Sánchez-Chóliz, Julio, 2013. "Economic growth and the evolution of water consumption in Spain: A structural decomposition analysis," Ecological Economics, Elsevier, vol. 96(C), pages 51-61.
    9. Wang, H. & Ang, B.W. & Su, Bin, 2017. "A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity," Ecological Economics, Elsevier, vol. 142(C), pages 163-176.
    10. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei, 2018. "How does information and communication technology affect China's energy intensity? A three-tier structural decomposition analysis," Energy, Elsevier, vol. 151(C), pages 748-759.

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