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Bertschek, Irene
Kaiser, Ulrich
Year of Publication: 
Series/Report no.: 
ZEW Discussion Papers No. 01-32
This paper analyzes the relationship between investment in information and communication technologies (ICT), non-ICT-investment, labor productivity and workplace reorganization. Firms are assumed to reorganize workplaces if the productivity gains arising from workplace reorganization exceed the associated reorganization costs. Two different types of organizational change are considered : introduction of group-work and flattening of hierarchies. Empirical evidence is provided for a sample of 411 firms from the German business-related services sector. We develop and estimate a model for labor productivity and firms' decision to re-organize workplaces that allows workplace reorganization to affect any parameter of the labor productivity equation. Our general and flexible methodology allows to properly take account of strategic complementarities between the input factors and workplace reorganization. The estimation results show that changes in human resources practices do not significantly affect firms' output elasticities with respect to information and communication technologies (ICT), non-ICT-capital and labor although most of the point estimates of the individual output elasticities and of the control variables for observable firm heterogeneity are larger if workplace reorganization is realized. We therefore apply Kernel density estimation technique and demonstrate that for firms with organizational change the entire labor productivity distribution shifts significantly out to the right if workplace reorganization takes place, indicating that workplace reorganization induces an increase in labor productivity that is attributable to complementarities between the various input factors and workplace reorganization. By contrast, firms without organizational change would not have realized significant productivity gains if they had reorganized workplaces.
workplace reorganization
labor productivity
endogenous switching regression model
Kernel density estimation
Document Type: 
Working Paper

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