HOW IMPORTANT IS INNOVATION? A BAYESIAN FACTOR-AUGMENTED PRODUCTIVITY MODEL BASED ON PANEL DATA. (1st August 2016)

• Record Type: Journal Article
• Authors: Bresson, Georges; Etienne, Jean-Michel; Mohnen, Pierre
• Editors: Azomahou, Théophile T.; Boucekkine, Raouf; Mohnen, Pierre; Verspagen, Bart
• Is Part Of: Macroeconomic dynamics. Volume 20:Number 8(2016)
• Page Start: 1987
• Page End: 2009
• Publication Date: 2016-08-01
• Subjects: Bayesian Factor-Augmented Model, -- Innovation, -- Markov Chain Monte Carlo, -- Panel Data, -- Productivity; Macroeconomics -- Periodicals; 339.05
• DOI: 10.1017/S1365100515000371
• Languages: English
• ISSNs: 1365-1005

Abstract

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.