The main aim of the BIGPROD project is to arrive at a better explanation of the inconsistent and conflicting interactions of technological and market processes currently observed in the global economy. By utilizing new “Big” data sources and processes, which will result in new metrics and economic indicators, BIGPROD aims to provide input to improve on the existing econometric models used for productivity analysis. These models try to explain the changes in economic productivity, however there is a consensus among economists that the current models do not include enough information on intangible assets (inputs other than capital or fixed assets), the changing nature of innovation towards more open modes, and servitization trends to come to a sufficiently thorough understanding of the productivity slowdown which we know as the “productivity paradox”. BIGPROD will focus primarily on the total or multi factor productivity (TFP/MFP) part that accounts for the contributions of firm R&D, innovation and technology.
A number of authors have hinted at the technology and diffusion-related explanations for the productivity paradox (Pilat, 2019):
- Transition costs of the adoption and diffusion of technologies (Griliches, 1957; David, 1991;
Jovanovic and Rousseau, 2005) - Transitional productivity growth dynamics due to rising resource misallocation (Gopinath et
al., 2015) - Measurement issues (Byrne et al., 2016)
Research by the OECD seems to point to a break-down of the “diffusion engine” (Andrews et al., 2015) where the normal trickle-down of knowledge (technological but also management) from hightech firms seems to stall while also the introduction of new technologies (such as AI), which are hard to implement for smaller firms, and the lack of resource reallocation linked to a slowdown in business dynamics and structural change are possible compounding effects.
To build an understanding of the current state of the work done in the area of productivity analysis a literature review will be undertaken to establish the definitions of each of the base variables that we will work with in this study. Specifically, we will review the literature explaining how the changing patterns of innovation, the increasing importance of intangibles, and servitization have contributed to the slowdown in productivity growth. We will furthermore undertake a media review to chart the current state of policy implementation in the area. This enables the project to build a bridge between the more academic approach of the economists and econometricians, and the practical needs of policy makers. Such a bridge will aid the policy relevance of research addressing the productivity slowdown and will help improve policy implementation efforts in this area.
In addition, previous work on big data, data science and indicator design needs to be studied in order to understand the possibilities that big data offers and the problems with translating this data into metrics and indicators. Specific attention needs to be given to previous, and current, work done by the EC on this topic to establish synergies and prevent any doubling of efforts. Moreover, the work by the OECD is important to keep an eye on, since the OECD has working groups dedicated to the topic of big data, indicator design and measurement, and productivity analysis.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870822
