How important is innovation? A Bayesian factor-augmented productivity model on panel data

G. Bresson, J.-M. Etienne & Pierre Mohnen

#2014-052

This paper proposes a Bayesian approach to estimate a factor augmented productivity equation. We exploit the panel dimension of our data and distinguish individual-specific and time-specific factors. On the basis of 21 technology, infrastructure and institution indicators from 82 countries over a 19-year period (1990 to 2008), we construct summary indicators of these three components and estimate their effect on the growth and the international differences in GDP per capita.

Keywords: Bayesian factor-augmented model, innovation, MCMC, panel data, productivity.

JEL classification: C23, C38, O47

  


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