Posted on: 7 March, 2019
Application deadline: April 7, 2019

PhD Studentship: Using data analytics to understand pension accumulation, University of Manchester and NEST Corporation, UK

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.

This project therefore presents a unique opportunity to be among the first social scientists to analyse data from the NEST Corporation to understand pension saving behaviour among low and middle-income employees, using a range of data analytic techniques.

Key questions that might be explored during this project include:

  • Understanding take-up, opt-out and cessation behaviour, when people enrol independently of an employer, who makes additional contributions beyond the default, and who continues in NEST when employment has ceased or changed;
  • Understanding patterns of participation and non-participation over time;
  • Understanding how earnings volatility is linked to pension behaviour;
  • Understanding who engages with active management of their pension fund, and how and when they do so;
  • Understanding contact with NEST: who contacts NEST, when and why? Integrating and interrogating customer query data online and via telephone with actions taken or not taken to understand the impact of communication on behaviour;
  • Developing predictive analytics at employer and employee level to understand key factors driving pension behaviour;
  • Investigating the possibilities for matching NEST data to other data sources including commercial (e.g. market segmentation data, area codes, credit referencing data) and government sources (e.g. National Pupil Database, Census) to inform key questions of interest regarding people’s financial behaviours and needs – both through actual linkage and model-based comparisons.

The findings of this project will have an impact for: social science, especially in social policy, sociology, and gerontology; industry, helping NEST and similar providers to engage effectively with their members and build a system that is optimal; and other key stakeholders and policymakers through providing novel insights into pension saving behaviour in this group.  The findings will also serve as the foundation and inspiration for future data analytics social science studies in these sectors.

Deadline 7th April 2019

Reference number MN32

Apply online here

Get in touch with the supervisor

Original listing