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HOW IMPORTANT IS INNOVATION? A BAYESIAN FACTOR-AUGMENTED PRODUCTIVITY MODEL BASED ON PANEL DATA

Published online by Cambridge University Press:  01 August 2016

Georges Bresson ,
Jean-Michel Etienne  and
Pierre Mohnen
Georges Bresson
Affiliation:
Université Paris II and Sorbonne Universités
Jean-Michel Etienne
Affiliation:
Université Paris-Sud 11
Pierre Mohnen*
Affiliation:
UNU-MERIT, Maastricht University
*
Address correspondence to: Pierre Mohnen, UNU-MERIT Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; e-mail : [email protected].
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Abstract

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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.

Keywords

Bayesian Factor-Augmented ModelInnovationMarkov Chain Monte CarloPanel DataProductivity
Type
Articles
Information
Macroeconomic Dynamics , Volume 20 , Special Issue 8: Technology Aspects in the Process of Development , December 2016 , pp. 1987 - 2009
Copyright
Copyright © Cambridge University Press 2016 

References

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