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Australia’s mining productivity decline: implications for MFP measurement

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  • Simon Zheng
  • Harry Bloch
Abstract
This paper investigates the mining sector’s poor productivity performance as measured by the growth accounting formula for multifactor productivity (MFP) index during the recent mining boom in Australia. We provide an alternative measure of productivity growth by estimating a translog variable cost function, with parameters that separate productivity growth due to technical change from that due to the effects of returns to scale, capacity utilisation and natural resource inputs. The results show that the average MFP growth in Australian mining based on the dual cost-function measure of technical change is 2 % over the sample period 1974–1975 to 2007–2008, rather than −0.2 % from the published index. The difference arises because declining natural resource inputs, the effects of capacity utilisation and returns to scale have all reduced the ‘true’ MFP growth. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Simon Zheng & Harry Bloch, 2014. "Australia’s mining productivity decline: implications for MFP measurement," Journal of Productivity Analysis, Springer, vol. 41(2), pages 201-212, April.
    • Handle: RePEc:kap:jproda:v:41:y:2014:i:2:p:201-212
    DOI: 10.1007/s11123-012-0329-4
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    References listed on IDEAS

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    1. Diewert, W. Erwin & Fox, Kevin J., 2008. "On the estimation of returns to scale, technical progress and monopolistic markups," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 174-193, July.
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    7. Vernon Topp & Leo Soames & Dean Parham & Harry Bloch, 2008. "Productivity in the Mining Industry: Measurement and Interpretation," Staff Working Papers 0807, Productivity Commission, Government of Australia.
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    Citations

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    Cited by:

    1. Hosseinzadeh, Ahmad & Smyth, Russell & Valadkhani, Abbas & Le, Viet, 2016. "Analyzing the efficiency performance of major Australian mining companies using bootstrap data envelopment analysis," Economic Modelling, Elsevier, vol. 57(C), pages 26-35.
    2. Ahmad Hosseinzadeh & Russell Smyth & Abbas Valadkhani & Amir Moradi, 2018. "What determines the efficiency of Australian mining companies?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(1), pages 121-138, January.
    3. Dai, Xiaoyong & Cheng, Liwei, 2016. "Market distortions and aggregate productivity: Evidence from Chinese energy enterprises," Energy Policy, Elsevier, vol. 95(C), pages 304-313.
    4. Vernon Topp & Tony Kulys, 2014. "On Productivity: The Influence of Natural Resource Inputs," International Productivity Monitor, Centre for the Study of Living Standards, vol. 27, pages 64-78, Fall.
    5. John E. Tilton, 2013. "Cyclical and Secular Determinants of Productivity in the Copper, Aluminum, Iron Ore, and Coal Industries," Working Papers 2013-11, Colorado School of Mines, Division of Economics and Business.
    6. Klayme, Tania & Gokmenoglu, Korhan K. & Rustamov, Bezhan, 2023. "Economic policy uncertainty, COVID-19 and corporate investment: Evidence from the gold mining industry," Resources Policy, Elsevier, vol. 85(PA).
    7. Claudia De La Huerta & Emiliano Luttini, 2017. "The Implications of Exhaustible Resources and Sectoral Composition for Growth Accounting: An Application to Chile," Working Papers Central Bank of Chile 807, Central Bank of Chile.
    8. C. Lovell & J. Lovell, 2013. "Productivity decline in Australian coal mining," Journal of Productivity Analysis, Springer, vol. 40(3), pages 443-455, December.
    9. Villena, Marcelo & Greve, Fernando, 2018. "On resource depletion and productivity: The case of the Chilean copper industry," Resources Policy, Elsevier, vol. 59(C), pages 553-562.
    10. Rodríguez, Xosé A. & Loureiro, Maria L. & Arias, Carlos, 2021. "Measuring productivity in the extractive industries. Evidence from Spanish fluorite mining," Resources Policy, Elsevier, vol. 73(C).

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    More about this item

    Keywords

    Productivity; Mining; Measurement; C32; L72; O40;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L72 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Other Nonrenewable Resources
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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