IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/10091.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/10091/EI%202007-16.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Ana-Isabel Guerra & Ferran Sancho, 2013. "A Linear Price Model With Extractions," EcoMod2013 5113, EcoMod.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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, vol. 11(12), pages 1-18, June.
    9. 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.
    10. 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.
    11. 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.
    12. Kirill Muradov, 2021. "Structural decomposition analysis with disaggregate factors within the Leontief inverse," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-17, December.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues," Energy, Elsevier, vol. 123(C), pages 47-63.
    2. 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.
    3. 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.
    4. Su, Bin & Ang, B.W., 2014. "Attribution of changes in the generalized Fisher index with application to embodied emission studies," Energy, Elsevier, vol. 69(C), pages 778-786.
    5. Banie Naser Outchiri, 2020. "Contributing to better energy and environmental analyses: how accurate are decomposition analysis results?," Cahiers de recherche 20-11, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    6. de Boer, Paul, 2009. "Generalized Fisher index or Siegel-Shapley decomposition?," Energy Economics, Elsevier, vol. 31(5), pages 810-814, September.
    7. Erik Dietzenbacher & Jesper Stage, 2006. "Mixing oil and water? Using hybrid input-output tables in a Structural decomposition analysis," Economic Systems Research, Taylor & Francis Journals, vol. 18(1), pages 85-95.
    8. Roca, Jordi & Serrano, Monica, 2007. "Income growth and atmospheric pollution in Spain: An input-output approach," Ecological Economics, Elsevier, vol. 63(1), pages 230-242, June.
    9. Fernández, Esteban & Fernández, Paula, 2008. "An extension to Sun's decomposition methodology: The Path Based approach," Energy Economics, Elsevier, vol. 30(3), pages 1020-1036, May.
    10. Tian, Kailan & Dietzenbacher, Erik & Yan, Bingqian & Duan, Yuwan, 2020. "Upgrading or downgrading: China's regional carbon emission intensity evolution and its determinants," Energy Economics, Elsevier, vol. 91(C).
    11. Wang, Yafei & Zhao, Hongyan & Li, Liying & Liu, Zhu & Liang, Sai, 2013. "Carbon dioxide emission drivers for a typical metropolis using input–output structural decomposition analysis," Energy Policy, Elsevier, vol. 58(C), pages 312-318.
    12. Mazzanti, Massimiliano & Montini, Anna, 2010. "Embedding the drivers of emission efficiency at regional level -- Analyses of NAMEA data," Ecological Economics, Elsevier, vol. 69(12), pages 2457-2467, October.
    13. Avelino, André F.T. & Franco-Solís, Alberto & Carrascal-Incera, André, 2021. "Revisiting the Temporal Leontief Inverse: New Insights on the Analysis of Regional Technological Economic Change," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 79-89.
    14. Leying Wu & Zheng Wang, 2017. "Examining drivers of the emissions embodied in trade," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    15. 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.
    16. Wang, Zhenguo & Su, Bin & Xie, Rui & Long, Haiyu, 2020. "China’s aggregate embodied CO2 emission intensity from 2007 to 2012: A multi-region multiplicative structural decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    17. Fernández González, P. & Presno, M.J. & Landajo, M., 2015. "Regional and sectoral attribution to percentage changes in the European Divisia carbonization index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1437-1452.
    18. 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.
    19. Philipp Schepelmann & An Vercalsteren & José Acosta-Fernandez & Mathieu Saurat & Katrien Boonen & Maarten Christis & Giovanni Marin & Roberto Zoboli & Cathy Maguire, 2020. "Driving Forces of Changing Environmental Pressures from Consumption in the European Food System," Sustainability, MDPI, vol. 12(19), pages 1-30, October.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:10091. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.