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on Knowledge Management and Knowledge Economy |
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Issue of 2024–12–30
four papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Antoinette Baujard (Université Jean Monnet Saint-Étienne, CNRS, Université Lyon 2, emlyon, GATE Lyon Saint-Étienne UMR 5824, F-42023, Saint-Etienne, France) |
| Abstract: | This paper shows how the debate on information in Welfare Economics is enriched from the perspective of Lisa Herzog’s thesis on citizen knowledge, and conversely. First, the two sources of information for welfare enhancing public decisions, individual utilities and knowledge, need articulated justification, insofar as knowledge may be used to revise individual utilities. The process of preference revisions implicitly assumes the coincidence between knowledge and truth, but there are compelling arguments why this assumption should be debated. Second, public decisions are ultimately based on an additional third component of information: collective ethical norms. They are decisive, but their legitimacy is conditional to their transparency in the debate between experts and citizens. Transparency on which knowledge is judged relevant hence constitutes a minimal condition for the design of democratic infrastructures involving public decision making. |
| Keywords: | Knowledge, Information, Welfarism, Demarcation, True-False demarcation, positive normative demarcation, Experts, Citizens, Democratic infrastructures of knowledge, Public decision-making |
| JEL: | A10 B41 D60 D70 D80 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:gat:wpaper:2421 |
| By: | Roth, Felix; Mitra, Alessio |
| Abstract: | The European Union (EU) faces challenges such as an ageing population, migratory pressures, geopolitical vulnerabilities, and climate change, highlighting the need to enhance its ability to do more with less. This paper examines the drivers of EU labour productivity before and after the 2007 financial crisis, across goods and services sectors, tangible and intangible assets, and Information and Communication Technologies (ICT) and non-ICT tangibles. Using the EUKLEMS 2022 dataset for 14 EU countries and the UK from 1995-2019 and growth regression analysis, we find that Research & Innovation (R&I) is crucial for productivity growth. Labour productivity in the goods sector benefits most from non-ICT tangible assets, while in the service sector, it benefits more from the non-R&D intangibles software, training, and organisational capital. On the other hand, training and ICT tangibles became more important drivers of labor productivity growth after the economic crisis. We argue that the productivity gap between the EU and the United States is largely due to insufficient investment in non-R&D intangibles like software, training, and organizational capital. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:uhhhdp:19 |
| By: | Margaret Galt (The Treasury) |
| Abstract: | Human capability is one of the four aspects of wealth in the Treasury’s Living Standards Framework. Combined with the other three aspects – the natural environment, physical and financial capital, and social cohesion, it underpins both current and future wellbeing. Human capability is defined as “people’s knowledge, physical and mental health, including cultural capability”. It would be ideal if we could measure the total contribution to wellbeing of all of these aspects but, at the moment, there is no generally accepted methodology to do this, so it is not covered in this note. Instead, the methodology used widely in the literature measures only the labour market value of the knowledge portion, by looking at lifetime earnings associated with different levels of education. We have called this component “human capital” in this note. Human capital is an important resource for the economy, enabling businesses to operate in a way that provides higher incomes. But it is equally important for an individual as not only do high levels of knowledge and skill increase their incomes, but they are also associated with many other positive outcomes in life. Treasury has previously written on the impact of human capability in lifting living standards in its Start of a Conversation series and this note is a continuation of this work (Morrissey, 2018). The Treasury, as part of the first wellbeing report Te Tai Waiora Wellbeing Report 2022, commissioned Trinh Le, a leading New Zealand expert in this area, to update the valuation of New Zealand’s human capital previously published with co-authors John Gibson and Les Oxley in 2006 (Le et al, 2006). This short note is an introduction to the methodology, and it highlights some aspects of these numbers that were interesting. This work also provides, for the first time, disaggregated numbers for Māori and non-Māori human capital. An accompanying spreadsheet is provided with the detailed tables. |
| Date: | 2023–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:nzt:nztaps:ap23/02 |
| By: | Mirko Draca; Max Nathan; Viet Nguyen-Tien; Juliana Oliveira-Cunha; Anna Rosso; Anna Valero |
| Abstract: | Which types of human capital influence the adoption of advanced technologies? We study the skill-biased adoption of information and communication technologies (ICT) across two waves in the UK. Specifically, we compare the 'new wave' of cloud and machine learning / AI technologies during the 2010s-pre-LLM-with the previous wave of personal computer adoption in the 1990s and early 2000s. At the area-level we see the emergence of a distinct STEM-biased adoption effect for the second wave of cloud and machine learning / AI technologies (ML/AI), alongside a general skill-biased effect. A one-standard deviation increase in the baseline share of STEM workers in areas is associated with around 0.3 of a standard deviation higher adoption of cloud and ML/AI. We find similar effects at the firm level where we are able to test for the influence of a wide range of skills. In turn, this STEM-biased adoption pattern has encouraged the concentration of these technologies, leading to more acute differences between high-tech and low-tech areas and firms. In contrast with classical technology diffusion, recent cloud and ML/AI adoption in the UK seems more likely to widen inequalities than reduce them. |
| Keywords: | Technology Diffusion, ICT, Human Capital, STEM |
| JEL: | D22 J24 O33 R11 |
| Date: | 2024–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:csl:devewp:495 |