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In this paper, we focus on the managerial characteristics of micro and small-sized firms. Using linked employer-employee data on the Portuguese economy for the 2010-2018 period, we estimate the impact of management teams’ human capital on the probability of firms becoming financially distressed and their subsequent recovery. Our estimates show that the relevance of management teams’ formal education on the probability of firms becoming financially distressed depends on firms’ size and the type of education. We show that management teams’ formal education and tenure reduce the probability of micro and small-sized firms becoming financially distressed and increases the probability of their subsequent recovery. The estimates also suggest that those impacts are stronger for micro and small-sized firms. Additionally, our results show that functional experience previously acquired in other firms, namely in foreign-owned and in exporting firms and in the area of finance, may reduce the probability of micro firms becoming financially distressed. On the other hand, previous functional experience in other firms seems to have a strong and highly significant impact on increasing the odds of recovery of financially distressed firms. We conclude that policies that induce an improvement in the managerial human capital of micro and small-sized firms have significant scope to improve their financial condition, enhancing the economy’s resilience against shocks.

Skills mismatch - the sub-optimal use of an individual's skills in their occupation - can be a source of dissatisfaction for workers and a brake for productivity growth. In our view, a difference in the level of skills within an occupation is not sufficient to infer that a skills mismatch exists. Since skills-mismatch is the result of a disparity between the supply and demand of labour, the quantifying of skills-mismatch must therefore be based on the mechanisms involved in this disparity. We propose to include in our measurement the level of education and field of study, which are key markers of an individual's skill level in the labour market. This makes it possible to identify, among individuals whose skill level differs from others within an occupation, those whose training profile can (or cannot) explain this situation. Through using the OECD PIAAC 2012 survey, this paper first identifies with data for France, individuals who present an apparent skills mismatch according to the framework proposed. Following an international comparison of 'apparent skills mismatch rates', we conclude this study by observing how the different groups identified differ in terms of how they perceive their employment situation as well as their individual characteristics.

This paper presents new evidence on the impact of intangible capital on productivity dispersion within industries. It first shows that rise in productivity dispersion after 2000 is more pronounced in intangible-intensive industries; then analyses the link between intangible capital intensity and productivity dispersion both at the top and at the bottom of the productivity distribution, and in different industries. The findings suggest that industries that have experienced a stronger increase in intangible investment have also seen a steeper rise in productivity dispersion both at the top and at the bottom of the productivity distribution. While the results at the top seem to be associated with the scalability of intangible capital – which is likely to disproportionally benefit high-productivity firms and incumbents – dispersion at the bottom appears to be linked to complementarities between intangible investment and factors like digital intensity, trade openness and venture capital.

Whilst overall productivity growth is stalling, firms at the frontier are still able to capture the benefits of the newest technologies and business practices. This paper uses linked employer-employee data covering all Belgian firms over a period of almost 20 years and investigates the differences in human capital between highly productive firms and less productive firms. We find a clear positive correlation between the share of high-skilled and STEM workers in a firm's workforce and its productivity. We obtain elasticities of 0.20 to 0.70 for a firm's productivity as a function of the share of high-skilled workers. For STEM (science, technology, engineering, mathematics) workers, of all skill levels, we find elasticities of 0.20 to 0.45. More importantly, the elasticity of STEM workers is increasing over time, whereas the elasticity of high-skilled workers is decreasing. This is possibly linked with the increasing number of tertiary education graduates and at the same time increased difficulties in filling STEM-related vacancies. Specifically, for high-skilled STEM workers in the manufacturing sector, the productivity gain can be as much as 4 times higher than the gain from hiring additional high-skilled non-STEM workers. To ensure that government efforts to increase the adoption of the latest technologies and business practices within firms lead to sustainable productivity gains, such actions should be accompanied by measures to increase the supply and mobility of human (STEM) capital. Without a proper supply of skills, firms will not be able to reap the full benefits of the digital revolution.

This report presents the results of a proof of concept for a new analytical infrastructure ('Fundstat') for analysing government funding of R&D at the project level, exploiting the wealth of text-based information about funded projects. Reflecting the growth in popularity of artificial intelligence (AI) and the OECD Council Recommendation on AI’s emphasis on R&D investment, the report focuses on analysing government investments into AI-related R&D. Using text mining tools, it documents the creation of a list of key terms used to identify AI-related R&D projects contained in 13 funding databases from eight OECD countries and the EU, provides estimates for the total number and volume of government R&D funding, and characterises their AI funding portfolio. The methods and findings developed in this study also serve as a prototype for a new distributed mechanism capable of measuring and analysing government R&D support across key OECD priority areas and topics.

As artificial intelligence (AI) advances across economies and societies, stakeholder communities are actively exploring how best to encourage the design, development, deployment and use of AI that is human-centred and trustworthy. This report presents a framework for comparing tools and practices to implement trustworthy AI systems as set out in the OECD AI Principles. The framework aims to help collect, structure and share information, knowledge and lessons learned to date on tools, practices and approaches for implementing trustworthy AI. As such, it provides a way to compare tools in different use contexts. The framework will serve as the basis for the development of an interactive, publicly available database on the OECD.AI Policy Observatory. This report informs ongoing OECD work towards helping policy makers and other stakeholders implement the OECD AI Principles in practice.

Finland has been setting research and development (R&D) intensity targets for almost 50 years. This paper explores the Finnish national policy experience in fostering public and private investments in R&D. Three key insights are the following: a) a systemic and integrated policy approach needs an impactful co-ordination and governance mechanism; b) a balanced innovation system with well-working joint public-private partnership efforts and mechanisms will do better in absorbing shocks; c) a key strategy to absorb shocks to the economy and society is to invest in long-term capabilities. This study also provides an overview of the factors influencing the level of R&D intensity. The current 4% target to be reached by 2030 was set in 2019 but thus far relatively few policy actions have been introduced to operationalise it. With these dynamics and uncertainty, it remains to be seen if the target will be reached by 2030.

This is the first report on the state of implementation of the policy recommendations to governments contained in the OECD Principles on Artificial Intelligence adopted in May 2019. This report presents a conceptual framework, provides findings, identifies good practices, and examines emerging trends in AI policy, particularly on how countries are implementing the five recommendations to policy makers contained in the OECD AI Principles. The report builds both on the expert input provided at meetings of the OECD.AI Network of Experts working group on national AI policies that took place online from February 2020 to April 2021 and on the EC-OECD database of national AI strategies and policies. As policy makers and AI actors around the world move from principles to implementation, this report aims to inform the implementation of the OECD AI Principles. This report is also a contribution to the OECD AI Policy Observatory.

The importance of knowledge co-creation – the joint production of innovation between industry, research and possibly other stakeholders, such as civil society – has been increasingly acknowledged. This paper builds on 13 cross-country case studies and co-creation experiences during the COVID-19 pandemic to characterise the diversity of knowledge co-creation initiatives and identify lessons for policy. The paper identifies a strong rationale for policy to support knowledge co-creation because the benefits of successful co-creation initiatives outweigh the initial co-ordination costs. Moreover, knowledge co-creation initiatives can contribute to democratising innovation. Successful initiatives engage all stakeholders and have effective governance and management structures. They also have clearly defined ownership and use rights of the collaborations’ outcomes and benefit from favourable conditions to operate, including temporary staff mobility and institutional set-ups that facilitate collaboration and effective communication among participants.

Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued.