Dynamic Models of R&D, Innovation and Productivity: Panel Data Evidence for Dutch and French Manufacturing
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.
Year of publication: |
2013
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Authors: | Raymond, Wladimir ; Mairesse, Jacques ; Mohnen, Pierre ; Palm, Franz |
Publisher: |
Munich : Center for Economic Studies and ifo Institute (CESifo) |
Subject: | R&D | innovation | productivity | panel data | dynamics | simultaneous equations |
Saved in:
freely available
Series: | CESifo Working Paper ; 4290 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 751737364 [GVK] hdl:10419/77668 [Handle] RePec:ces:ceswps:_4290 [RePEc] |
Classification: | C33 - Models with Panel Data ; C34 - Truncated and Censored Models ; C35 - Discrete Regression and Qualitative Choice Models ; L60 - Industry Studies: Manufacturing. General ; O31 - Innovation and Invention: Processes and Incentives ; O32 - Management of Technological Innovation and R&D |
Source: |
Persistent link: https://www.econbiz.de/10010317010