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Conditional convergence and the dynamics of the capital-output ratio

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  • Kieran McQuinn
  • Karl Whelan

Abstract

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

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Bibliographic Info

Article provided by Springer in its journal Journal of Economic Growth.

Volume (Year): 12 (2007)
Issue (Month): 2 (June)
Pages: 159-184

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Handle: RePEc:kap:jecgro:v:12:y:2007:i:2:p:159-184

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Web page: http://www.springerlink.com/link.asp?id=102931

Related research

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

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  8. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
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  10. Caselli, Francesco & Esquivel, Gerardo & Lefort, Fernando, 1996. " Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics," Journal of Economic Growth, Springer, vol. 1(3), pages 363-89, September.
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Citations

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Cited by:
  1. Krasnopjorovs, Olegs, 2013. "Latvijas ekonomikas izaugsmi noteicošie faktori
    [Factors of Economic Growth in Latvia]
    ," MPRA Paper 47550, University Library of Munich, Germany.
  2. Groth, Christian & Wendner, Ronald, 2011. "Learning by investing, embodiment, and speed of convergence," MPRA Paper 29008, University Library of Munich, Germany.
  3. McGuinness, Anne, 2007. "Institutions and Total Factor Productivity Convergence," Research Technical Papers 9/RT/07, Central Bank of Ireland.
  4. Groth, Christian & Wendner, Ronald, 2014. "Embodied learning by investing and speed of convergence," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 245-269.
  5. 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.
  6. Spruk, Rok, 2011. "Productivity and income convergence in transition: theory and evidence from Central Europe," MPRA Paper 33389, University Library of Munich, Germany.
  7. 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.

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