In the face of long-term megatrends like population ageing, labour market transformations and climate change, OECD countries’ social protection systems are well-prepared in some ways but unprepared in others. The welfare state that developed post-World War II in most OECD countries has matured and offers the core foundation for challenges ahead. Yet the coverage of social protection remains incomplete in many countries, and disadvantaged groups often struggle to receive the support they need.
Recent crises, including the COVID‑19 pandemic and rising costs of living, have highlighted the crucial role of social programmes that are responsive to evolving needs. Social protection systems must make more efficient use of constrained public finances and ensure that the right benefits and services reach those who need them.
This report – Modernising access to social protection: Strategies, technologies and data advances – forms part of the OECD’s Future of Social Protection programme of work, overseen by the OECD Employment, Labour and Social Affairs Committee. The report assesses how OECD countries use new technologies, as well as new data sources, to identify people in need and to improve the delivery of social benefits and services. This and other projects under the Future of Social Protection programme of work will serve as inputs to the 2025 OECD Social Policy Ministerial.
Among other key findings, this report illustrates how OECD countries are working to improve the take‑up of benefits and services by linking data across agencies and using this information to simplify enrolment processes. Automatic enrolment enabled by improved individual- and household-level data, in particular, has the potential to expand the reach of social protection, as well as make it more responsive to evolving needs. As the digitalisation of social protection progresses, governments must ensure that this transformation is inclusive, for example by maintaining in-person service provision, and by being cautious, fair, human-centred and transparent when using automated decision-making technologies and artificial intelligence.