Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/77668
Authors: 
Raymond, Wladimir
Mairesse, Jacques
Mohnen, Pierre
Palm, Franz
Year of Publication: 
2013
Series/Report no.: 
CESifo Working Paper 4290
Abstract: 
This paper introduces dynamics in the R&D to innovation and innovation to productivity relationships, which have mostly been estimated on cross-sectional data. It considers four nonlinear dynamic simultaneous equations models that include individual effects and idiosyncratic errors correlated across equations and that differ in the way innovation enters the conditional mean of labor productivity: through an observed binary indicator, an observed intensity variable or through the continuous latent variables that correspond to the observed occurrence or intensity. It estimates these models by full information maximum likelihood using two unbalanced panels of Dutch and French manufacturing firms from three waves of the Community Innovation Survey. The results provide evidence of robust unidirectional causality from innovation to productivity and of stronger persistence in productivity than in innovation.
Subjects: 
R&D
innovation
productivity
panel data
dynamics
simultaneous equations
JEL: 
C33
C34
C35
L60
O31
O32
Document Type: 
Working Paper

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