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This brief discusses the policy implications of the findings of a recent large scale study of UK egg and sperm donors. Since 2005, UK sperm and egg donors must consent to being identifiable to people conceived from their donation(s), should they request that information at age 18. There has been much speculation, but limited empirical research, on how donors might feel about this change. The Curious Connections study, which included interviews with donors, their relatives and professionals who work with them, examined how donors and their families experience donation in the context of increased openness. The study found that contemporary sperm and egg donors were overwhelmingly supportive of the move to “identity-release” donation. However, current UK policies do not fully reflect the range of ways in which donors and their families experience and understand what it means to donate egg or sperm. The authors recommend a range of policy and practice changes, including increased flexibility regarding information sharing between donors and recipient parents, the provision of long-term support for donors and their families, enabling children of donors to register with the Donor Sibling Link, creating a voluntary register of donor conceptions arranged outside of licensed clinics, and increased support for known donor conception.

This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services.

The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs).

This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years.

To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.