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

Torben Schubert, Angela Jäger, Serdar Turkeli, Fabiana Visentin

Research output: Working paper / PreprintDiscussion paper

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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 servitization 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-DuguetMairesse type of model (CDM), which enriches the original specification by the three influence factors of
intangibles, spillovers, and servitization. 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.
Original languageEnglish
PublisherUNU-MERIT working papers
Number of pages55
Publication statusPublished - 2020

Publication series

SeriesUNU-MERIT Working Papers

JEL classifications

  • c80 - "Data Collection and Data Estimation Methodology; Computer Programs: General"
  • d24 - "Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity"
  • e22 - "Capital; Investment; Capacity"
  • l80 - Industry Studies: Services: General
  • o31 - Innovation and Invention: Processes and Incentives
  • o32 - Management of Technological Innovation and R&D
  • o34 - Intellectual Property Rights
  • o40 - Economic Growth and Aggregate Productivity: General
  • o47 - "Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence"


  • productivity
  • intangibles
  • Servitization
  • innovation
  • R&D
  • open innovation
  • IPR
  • knowledge diffusion
  • economic growth
  • productivity paradox
  • big data
  • large data sets
  • data collection

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