IDEAS home Printed from https://ideas.repec.org/p/qld/uqcepa/93.html
   My bibliography  Save this paper

An Economic Approach to Identifying the Drivers of Productivity Change in the Market Sectors of the Australian Economy

Author

Abstract

This paper uses a relatively new total factor productivity (TFP) index to measure productivity change in the Australian economy. Unlike the TFP indexes used by most statistical agencies, the index used in this paper satisfies a suite of common sense axioms from index number theory. It can also be exhaustively decomposed into measures of technical change, environmental change, and various types of efficiency change. This paper uses least squares methods to estimate these components. The main driver of productivity change in the Australian economy is found to have been scale-mix efficiency change. Scale-mix efficiency is a measure of how well a firm is capturing economies of scale and scope.

Suggested Citation

  • Christopher O`Donnell, 2014. "An Economic Approach to Identifying the Drivers of Productivity Change in the Market Sectors of the Australian Economy," CEPA Working Papers Series WP022014, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:93
    as

    Download full text from publisher

    File URL: https://economics.uq.edu.au/files/5136/WP022014.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christopher J. O’Donnell, 2016. "Nonparametric Estimates of the Components of Productivity and Profitability Change in U.S. Agriculture," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 515-541, Springer.
    2. Mary O'Mahony & Marcel P. Timmer, 2009. "Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database," Economic Journal, Royal Economic Society, vol. 119(538), pages 374-403, June.
    3. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    4. Nadiri, M Ishaq, 1970. "Some Approaches to the Theory and Measurement of Total Factor Productivity: A Survey," Journal of Economic Literature, American Economic Association, vol. 8(4), pages 1137-1177, December.
    5. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-141, April.
    6. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Julien, Jacques C. & Bravo-Ureta, Boris E. & Rada, Nicholas E., 2019. "Assessing farm performance by size in Malawi, Tanzania, and Uganda," Food Policy, Elsevier, vol. 84(C), pages 153-164.
    2. Ipatova, Irina, 2015. "The dynamics of total factor productivity and its components: Russian plastic production," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 21-40.

    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. O'Donnell, C.J., 2014. "Technologies, Markets and Behaviour: Some Implications for Estimating Efficiency and Productivity Change," 2014 Conference (58th), February 4-7, 2014, Port Macquarie, Australia 165867, Australian Agricultural and Resource Economics Society.
    2. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    3. Fabio Pieri & Michela Vecchi & Francesco Venturini, 2017. "Modelling the joint impact of R and D and ICT on productivity: A frontier analysis approach," DEM Working Papers 2017/13, Department of Economics and Management.
    4. Giraleas, Dimitris & Emrouznejad, Ali & Thanassoulis, Emmanuel, 2012. "Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 222(3), pages 673-683.
    5. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    6. C.J. O’Donnell, 2017. "Estimating Total Factor Productivity Change When No Price or Value-Share Data are Available," CEPA Working Papers Series WP012017, School of Economics, University of Queensland, Australia.
    7. Ceyhun Elgin & Selman Çakır, 2015. "Technological progress and scientific indicators: a panel data analysis," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 24(3), pages 263-281, April.
    8. Ipatova, Irina, 2015. "The dynamics of total factor productivity and its components: Russian plastic production," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 21-40.
    9. A. Peyrache & A. N. Rambaldi, 2012. "A State-Space Stochastic Frontier Panel Data Model," CEPA Working Papers Series WP012012, School of Economics, University of Queensland, Australia.
    10. Pieri, Fabio & Vecchi, Michela & Venturini, Francesco, 2018. "Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach," Research Policy, Elsevier, vol. 47(9), pages 1842-1852.
    11. A. Peyrache & A. N. Rambaldi, 2017. "Incorporating temporal and country heterogeneity in growth accounting—an application to EU-KLEMS," Journal of Productivity Analysis, Springer, vol. 47(2), pages 143-166, April.
    12. Alexander Yu. Apokin & Irina Ipatova, 2016. "How R&D Expenditures Influence Total Factor Productivity and Technical Efficiency?," HSE Working papers WP BRP 128/EC/2016, National Research University Higher School of Economics.
    13. O’Donnell, C.J., 2016. "Using information about technologies, markets and firm behaviour to decompose a proper productivity index," Journal of Econometrics, Elsevier, vol. 190(2), pages 328-340.
    14. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    15. Tom Kompas & Tuong Nhu Che & R. Quentin Grafton, 2004. "Technical efficiency effects of input controls: evidence from Australia's banana prawn fishery," Applied Economics, Taylor & Francis Journals, vol. 36(15), pages 1631-1641.
    16. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    17. Andriakopoulos, Konstantinos & Ladas, Augoustinos & Andriakopoulos, Panagiotis, 2020. "Bank efficiency and leasing in U.S.A. banking system," MPRA Paper 112645, University Library of Munich, Germany.
    18. Jan Kluge & Sarah Lappöhn & Kerstin Plank, 2023. "Predictors of TFP growth in European countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(1), pages 109-140, February.
    19. Giovanni Calice & Levent Kutlu & Ming Zeng, 2021. "Understanding US firm efficiency and its asset pricing implications," Empirical Economics, Springer, vol. 60(2), pages 803-827, February.
    20. Khanal, Aditya & Koirala, Krishna & Regmi, Madhav, 2016. "Do Financial Constraints Affect Production Efficiency in Drought Prone Areas? A Case from Indonesian Rice Growers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230087, Southern Agricultural Economics Association.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:qld:uqcepa:93. See general information about how to correct material in RePEc.

    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: SOE IT (email available below). General contact details of provider: https://edirc.repec.org/data/decuqau.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.