How Important is Innovation? A Bayesian Factor-Augmented Productivity Model Based on Panel Data

G. Bresson, J.-M. Etienne, P. Mohnen

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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. © Cambridge University Press 2016.
Original languageEnglish
Pages (from-to)1987-2009
Number of pages23
JournalMacroeconomic Dynamics
Volume20
Issue number8
DOIs
Publication statusPublished - Dec 2016

Keywords

  • Bayesian Factor-Augmented Model
  • Innovation
  • Markov Chain Monte Carlo
  • Panel Data
  • Productivity
  • HETEROGENEITY
  • NUMBER
  • PRICES
  • INFERENCE
  • TRENDS
  • REGRESSIONS
  • FEATURES

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