Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/326752 
Year of Publication: 
2020
Series/Report no.: 
UNU-MERIT Working Papers No. 2020-050
Publisher: 
United Nations University (UNU), Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht
Abstract: 
This paper develops the plan for the econometric estimations concerning the relationship between firm productivity and the specifics of the innovation process. The paper consists of three main parts. In the first, we review the relevant literature related to the productivity paradox and its causes. Specific attention will be paid to broad economic trends, in particular the higher importance of intangibles, the increasing importance of knowledge spillovers and servitisation as drivers of the slowdown in productivity growth. In the second part, we introduce a plan for the econometric estimation strategy. Here we propose an extended Crépon-Duguet-Mairesse type of model (CDM), which enriches the original specification by the three influence factors of intangibles, spillovers, and servitisation. This will allow testing the influence of these three factors on productivity at the level of the firm within a unified framework. In the third part, we build on the literature review in order to provide a detailed plan for the data collection procedure including a description of the variables to be collected and the source from which the variables are coming. It should be noted that we will rely partly on structured data (e.g. ORBIS), while many others variables will need to be generated from unstructured sources, in particular the webpages of firms. The use of unstructured data is a particular strength of our proposed data collection procedure because the use of such data is expected to offer novel insights. However, it implies additional risks in terms of data quality or missing data. Our data collection plan explores the maximum potential of variables that will ideally be made available for later econometric treatment. Whether indeed all variables will have sufficient quality to be used in the econometric estimations will be subject to the outcomes of the actual collection efforts.
Subjects: 
Productivity
Intangibles
Servitisation
Innovation
R&D
Open Innovation
IPR
Knowledge diffusion
Economic growth
Productivity Paradox
Big data
Large data sets
data collection
JEL: 
C55
C80
D24
E22
L80
O31
O32
O34
O36
O40
O47
Creative Commons License: 
cc-by-nc-sa Logo
Document Type: 
Working Paper

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