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Convergence in Productivity Across Industries: Some Results for New Zealand and Australia

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  • Troy Matheson
  • Les Oxley

Abstract

New Zealand shares a wealth of common interests and experiences with Australia. This has tempted some to assume that these economies form an 'Economic Club', in which one would expect to identify common aggregate trends and growth experiences. In this paper we present results that test, and generally reject, convergence in labour productivity across Australia and New Zealand, using both aggregate and disaggregate, industry-level data. We find that only two industries satisfy our definition of Conditional Convergence (Agriculture, Forestry and Fishing and Cultural and Recreational Services), and that the Mining and Wholesale Trade industries have particularly important roles to play in explaining the measured divergence. Cointegration-based tests reveal more stochastic trends governing Australian productivity than in New Zealand. The evidence suggests, therefore, that the underlying growth processes of the two economies are fundamentally different, thereby questioning the relevance of aggregate comparisons between them. New evidence using industry-level data does not, therefore, resolve the aggregate-level 'non-convergence puzzle' identified here, and elsewhere.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal International Review of Applied Economics.

Volume (Year): 21 (2007)
Issue (Month): 1 ()
Pages: 55-73

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Handle: RePEc:taf:irapec:v:21:y:2007:i:1:p:55-73

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Keywords: Productivity; convergence; Australia; New Zealand; industry;

References

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  1. Bernard, A.B. & Durlauf, S.N., 1994. "Interpreting Tests of the Convergence Hypothesis," Working papers 9401r, Wisconsin Madison - Social Systems.
  2. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  3. Bernard, Andrew B & Durlauf, Steven N, 1995. "Convergence in International Output," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 97-108, April-Jun.
  4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  5. Bernard, Andrew B & Jones, Charles I, 1996. "Productivity across Industries and Countries: Time Series Theory and Evidence," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 135-46, February.
  6. Eric Zivot & Donald W.K. Andrews, 1990. "Further Evidence on the Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Cowles Foundation Discussion Papers 944, Cowles Foundation for Research in Economics, Yale University.
  7. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-18, Nov.-Dec..
  8. Greasley, David & Oxley, Les, 1997. "Time-series based tests of the convergence hypothesis: Some positive results," Economics Letters, Elsevier, vol. 56(2), pages 143-147, October.
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Cited by:
  1. Lavopa, Alejandro, 2011. "The impact of sectoral heterogeneities in economic growth and catching up: Empirical evidence for Latin American manufacturing industries," MERIT Working Papers 075, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  2. Weshah Razzak, 2007. "Explaining the gaps in labour productivity in some developed countries," Money Macro and Finance (MMF) Research Group Conference 2006 30, Money Macro and Finance Research Group.
  3. Razzak, Weshah, 2006. "Explaining the gaps in labour productivity for some developed countries," MPRA Paper 53, University Library of Munich, Germany.

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