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Addressing the productivity paradox with big data: A literature review and adaptation of the CDM econometric model

Torben Schubert (), Angela Jäger (), Serdar Türkeli () and Fabiana Visentin
Additional contact information
Torben Schubert: Fraunhofer ISI, and Circle, Lund University
Angela Jäger: Fraunhofer ISI
Serdar Türkeli: UNU-MERIT, Maastricht University

No 2020-050, MERIT Working Papers from United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT)

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.

Keywords: Productivity; Intangibles; Servitisation; Innovation; R&D; Open Innovation; IPR; Knowledge diffusion; Economic growth; Productivity Paradox; Big data; Large data sets; data collection (search for similar items in EconPapers)
JEL-codes: C55 C80 D24 E22 L80 O31 O32 O34 O36 O40 O47 (search for similar items in EconPapers)
Date: 2020-11-11
New Economics Papers: this item is included in nep-big, nep-cse, nep-eff, nep-ino, nep-mac, nep-ore and nep-tid
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