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How Important Is Innovation? A Bayesian Factor-Augmented Productivity Model Based On Panel Data

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  • Bresson, Georges
  • Etienne, Jean-Michel
  • Mohnen, Pierre

Abstract

This paper proposes a Bayesian approach to estimating a factor-augmented GDP per capita equation. We exploit the panel dimension of our data and distinguish between individual-specific and time-specific factors. On the basis of 21 technology, infrastructure, and institutional indicators from 82 countries over a 19-year period (1990 to 2008), we construct summary indicators of each of these three components in the cross-sectional dimension and an overall indicator of all 21 indicators in the time-series dimension and estimate their effects on growth and international differences in GDP per capita. For most countries, more than 50% of GDP per capita is explained by the four common factors we have introduced. Infrastructure is the greatest contributor to total factor productivity, followed by technology and institutions.

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  • Bresson, Georges & Etienne, Jean-Michel & Mohnen, Pierre, 2016. "How Important Is Innovation? A Bayesian Factor-Augmented Productivity Model Based On Panel Data," Macroeconomic Dynamics, Cambridge University Press, vol. 20(8), pages 1987-2009, December.
    • Handle: RePEc:cup:macdyn:v:20:y:2016:i:08:p:1987-2009_00
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    Cited by:

    1. Badi H. Baltagi & Georges Bresson & Jean-Michel Etienne, 2020. "Growth Empirics: a Bayesian Semiparametric Model With Random Coefficients for a Panel of OECD Countries," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 217-253, Emerald Group Publishing Limited.
    2. Lingyan Xu & Dandan Wang & Jianguo Du, 2021. "The Heterogeneous Influence of Infrastructure Construction on China’s Urban Green and Smart Development—The Threshold Effect of Urban Scale," Land, MDPI, vol. 10(10), pages 1-17, September.

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