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Conditional convergence and the dynamics of the capital-output ratio


  • Kieran McQuinn
  • Karl Whelan


Output per worker can be expressed as a function of technological efficiency and of the capital-output ratio. Because technology is exogenous in the Solow model, all of the endogenous convergence dynamics take place through the adjustment of the capital-output ratio. This paper uses the empirical behavior of the capital-output ratio to estimate the speed of conditional convergence of economies towards their steady-state paths. We find that the conditional convergence speed is about seven percent per year. This is somewhat faster than predicted by the Solow model and is significantly higher than reported in most previous studies based on output per worker regressions. We show that, once there are stochastic shocks to technology, standard panel econometric techniques produce downward-biased estimates of convergence speeds, while our approach does not. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Kieran McQuinn & Karl Whelan, 2007. "Conditional convergence and the dynamics of the capital-output ratio," Journal of Economic Growth, Springer, vol. 12(2), pages 159-184, June.
  • Handle: RePEc:kap:jecgro:v:12:y:2007:i:2:p:159-184 DOI: 10.1007/s10887-007-9013-3

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    References listed on IDEAS

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    Cited by:

    1. David E. Bloom & Elizabeth T. Cafiero & Mark E. McGovern & Klaus Prettner & Anderson Stanciole & Jonathan Weiss & Samuel Bakkila & Larry Rosenberg, 2013. "The Economic Impact of Non-communicable Disease in China and India: Estimates, Projections, and Comparisons," PGDA Working Papers 10713, Program on the Global Demography of Aging.
    2. Michael Gerfin & Boris Kaiser, 2010. "The Effects of Immigration on Wages: An Application of the Structural Skill-Cell Approach," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 146(IV), pages 709-739, December.
    3. Krasnopjorovs, Olegs, 2013. "Latvijas ekonomikas izaugsmi noteicošie faktori
      [Factors of Economic Growth in Latvia]
      ," MPRA Paper 47550, University Library of Munich, Germany.
    4. Christian Groth & Ronald Wendner, 2011. "Learning by Investing, Embodiment, and Speed of Convergence," EPRU Working Paper Series 2011-01, Economic Policy Research Unit (EPRU), University of Copenhagen. Department of Economics.
    5. Iulia Andreea BUCUR & Oana Ancuta STANGACIU, 2015. "The European Union Convergence In Terms Of Economic And Human Development," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 7(2), pages 256-275, August.
    6. Michael Paffermayr, 2009. "Spatial Convergence of Regions Revisited: A Spatial Maximum Likelihood Systems Approach," Working Papers 2009-07, Faculty of Economics and Statistics, University of Innsbruck.
    7. Bayraktar-Sağlam, Bahar & Yetkiner, Hakan, 2014. "A Romerian contribution to the empirics of economic growth," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 257-272.
    8. Groth, Christian & Wendner, Ronald, 2014. "Embodied learning by investing and speed of convergence," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 245-269.
    9. Lorenz B. Fischer & Michael Pfaffermayr, 2015. "The more the merrier? Migration and Convergence among European Regions," Working Papers 2015-08, Faculty of Economics and Statistics, University of Innsbruck.
    10. McGuinness, Anne, 2007. "Institutions and Total Factor Productivity Convergence," Research Technical Papers 9/RT/07, Central Bank of Ireland.
    11. repec:eee:joecag:v:4:y:2014:i:c:p:100-111 is not listed on IDEAS
    12. Spruk, Rok, 2011. "Productivity and income convergence in transition: theory and evidence from Central Europe," MPRA Paper 33389, University Library of Munich, Germany.

    More about this item


    Convergence; Solow Model; Panel Data; O41; O30; C23;

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models


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