Recent government reforms have made provision of workplace pensions compulsory for almost all employees, including by micro-employers. Although a new market has grown in this sector, a national scheme – NEST – is available to any employer wishing to use it. NEST is a trust-based scheme which now has over 7 million members, and it is anticipated that more than 15 million UK employees will be members of NEST at some point. Median earnings for NEST employees are just £18,500 per annum – well below the national average. This means that NEST and other private pension providers in these new markets now need to engage with those whose work patterns are much more precarious, and who are on much lower incomes, than under earlier systems when pensions for these socio-economic groups were broadly seen as the responsibility of the State. If the new pension system fails, this will leave millions in poverty in later life, and so designing systems that will work optimally for all social groups has become a matter of social justice and social cohesion, as well as being commercially important for providers. Conversely, understanding where and how systems might fail has become crucial for holding policy-makers to account and arguing for policy change where appropriate. This collaborative studentship between the University of Manchester and NEST will use cutting edge data analytic techniques to address these issues, by analysing NEST data accumulated since NEST’s inception in 2012 including administrative data, contact data and web data. The aim will be to better understand the pension accumulation over time of members of the national NEST pension scheme, providing new and urgently required social scientific insights into how this growing segment of the population is managing an increasingly privatised and individualised pension system.
|Effective start/end date||1/10/19 → 30/09/23|
Research Beacons, Institutes and Platforms
- Manchester Institute for Collaborative Research on Ageing
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.