While means-tested benefits such as minimum income benefits (MIB) and unemployment assistance (UA) are an essential safety net for low-income people and the unemployed, incomplete take-up is the rule rather than the exception. Building on desk research, open-ended surveys and semi-structured interviews, this paper investigates the opportunities and risks of using artificial intelligence (AI) for managing these means-tested benefits. This ranges from providing information to individuals, through determining eligibility based on pre-determined statutory criteria and identifying undue payments, to notifying individuals about their eligibility status. One of the key opportunities of using AI for these purposes is that this may improve the timeliness and take-up of MIB and UA. However, it may also lead to systematically biased eligibility assessments or increase inequalities, amongst others. Finally, the paper explores potential policy directions to help countries seize AI’s opportunities while addressing its risks, when using it for MIB or UA management.
Being able to swim empowers individuals to make choices, have agency, and be free to choose core aspects of their life, such as working safely on or near water. It is also associated with lifelong health benefits and reduces the risk of drowning. Using data from the Lloyd’s Register Foundation World Risk Poll 2019, this paper provides the first global estimates of adults’ ability to swim without assistance. Individuals in high-income countries are considerably more likely to report being able to swim without assistance than individuals in low-income countries. Disparities also exist within countries. In particular, women are less likely to be able to swim without assistance than men in virtually all countries, birth cohorts, and levels of education. Investing in reducing inequalities in life skills, such as swimming, can foster economic development and empowerment, especially in light of threats, such as climate change.
Social protection systems use a range of entitlement criteria. First-tier support typically requires contributions or past employment in many countries, while safety net benefits are granted on the basis of need. In a context of volatile and uncertain labour markets, careful and continuous monitoring of the effectiveness of income support is a key input into an evidence-based policy process. This paper proposes a novel empirical method for monitoring the accessibility and levels of safety net benefits. It focusses on minimum-income benefits (MIB) and other non-contributory transfers and relies on data on the amounts of cash support that individuals in need receive in practice. Results show that accessibility and benefit levels differ enormously across countries – for instance, in 2015/16, more than four out of five low-income workless one-person households received MIB in Australia, France and the United Kingdom, compared to only one in five in Greece, Italy and Korea, three countries that have since sought to strengthen aspects of safety-net provisions.
Trends towards increasing non-standard employment may pose challenges for the Czech pension system, as some of these workers are at risk of low pension entitlements and the financial sustainability of public pensions is undermined.