Template-type: ReDIF-Paper 1.0
Author-Name: Schubert, Torben
Author-Email: Torben.Schubert@isi.fraunhofer.de 
Author-Workplace-Name: Fraunhofer ISI, and Circle, Lund University
Author-Name: Jäger, Angela
Author-Email: Angela.Jaeger@isi.fraunhofer.de
Author-Workplace-Name: Fraunhofer ISI
Author-Name: Türkeli, Serdar 
Author-Email: turkeli@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Author-Name: Visentin, Fabiana
Author-Email: visentin@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Title: Addressing the productivity paradox with big data: A literature review and 
	 adaptation of the CDM econometric model
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.
Classification-JEL: C55, C80, D24, E22, L80, O31, O32, O34, O36, O40, O47
Keywords: Productivity, Intangibles, Servitisation, Innovation, R&D, Open Innovation,
	 IPR, Knowledge diffusion, Economic growth, Productivity Paradox, Big data,
	 Large data sets, data collection
Series: UNU-MERIT Working Papers
Creation-Date: 20201111
Number: 2020-050
File-URL: https://unu-merit.nl/publications/wppdf/2020/wp2020-050.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2020050