This paper tries to calculate some facts for the "knowledge economy". Building on the work of Corrado, Hulten and Sichel (CHS, 2005,9), using new data sets and a new micro survey, we (1) document UK intangible investment and (2) see how it contributes to economic growth. Regarding investment in knowledge/intangibles, we find (a) this is now greater than tangible investment at, in 2008, £141bn and £104bn respectively; (b) that R&D is about 11% of total intangible investment, software 15%, design 17%, and training and organizational capital 22%; (d) the most intangible-intensive industry is manufacturing (intangible investment is 20% of value added) and (e) treating intangible expenditure as investment raises market sector value added growth in the 1990s due to the ICT investment boom, but slightly reduces it in the 2000s. Regarding the contribution to growth, for 2000-08, (a) intangible capital deepening accounts for 23% of labour productivity growth, against computer hardware (12%) and TFP (40%); (b) adding intangibles to growth accounting lowers TFP growth by about 15% (c) capitalising R&D adds 0.03% to input growth and reduces ΔlnTFP by 0.03% and (d) manufacturing accounts for just over 40% of intangible capital deepening plus TFP.

In this paper we develop a framework for analysing the impact of Artificial Intelligence (AI) on occupations. This framework maps 59 generic tasks from worker surveys and an occupational database to 14 cognitive abilities (that we extract from the cognitive science literature) and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity across a broad range of different AI areas. The use of cognitive abilities as an intermediate layer, instead of mapping work tasks to AI benchmarks directly, allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly created. An application of our framework to occupational databases gives insights into the abilities through which AI is most likely to affect jobs and allows for a ranking of occupations with respect to AI exposure. Moreover, we show that some jobs that were not known to be affected by previous waves of automation may now be subject to higher AI exposure. Finally, we find that some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies (e.g., visual and auditory processing using deep learning, and sensorimotor interaction through (deep) reinforcement learning). This article appears in the special track on AI and Society.

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Public funding of research improves the systemic conditions of entrepreneurial ecosystems. It provides early-stage financing to technologies that form the basis for new products and services. In addition to financial support, instruments as the EC Framework Programmes (FP) facilitate the creation of research networks. By bringing together organisations of various types and geographic origins and increasing the diversity of their interactions, the instrument seeks to accelerate a discovery process in which organisations attempt to bring desired innovations to the market and society. In this paper, we examine the impact of organisational and geographic diversity of partnerships in EU-funded research networks on the commercial potential of their innovations. We explore a sample of 603 collaborative research projects supported by European FPs. We use data from the Innovation Radar, a unique survey database developed by DG CONNECT to assess the innovation outcomes of FP projects in ICT. We show that innovations developed by research networks with a higher organisational diversity have more commercial potential. This finding supports the idea that policies improving systemic conditions of entrepreneurship ecosystems through the creation of institutionally diverse research networks can have beneficial effects on the commercialisation potential of innovations developed in FP projects. In contrast, research networks with a wider range of internationally dispersed research partners are likely to have less innovation potential. This may suggest the existence of coordination and communication difficulties in FP projects where geographic diversity is greater.