Data for: Dynamic models of R & D, innovation and productivity: Panel data evidence for Dutch and French manufacturing

Abstract of associated article: 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.

关联论文摘要：本文将动态性纳入此前大多基于横截面数据开展估算的研发-创新与创新-生产率关系研究框架中。本文构建四类包含个体效应与跨方程相关异质性误差的非线性动态联立方程模型，各类模型的差异在于创新进入劳动生产率条件均值的形式：可通过观测二元指示变量、观测强度变量，或是对应观测到的创新发生情况与强度的连续潜变量实现。研究采用取自三轮社区创新调查（Community Innovation Survey）的两套荷兰与法国制造业企业非平衡面板数据，通过全信息极大似然估计（full information maximum likelihood）方法对上述模型进行估算。实证结果证实，创新对生产率存在稳健的单向因果关系，且生产率的持续性显著强于创新。