Towards a typology of pension contributors in the Nest pension: A topological and system approach to modelling Nest's members' pension accumulation

Join Maria Fernanda Ibarra Gutierrez, University of Manchester for our November CHASM seminar as she discusses research into the National Employment Saving Trust (Nest) to understand patterns of savings of workers who have traditionally been excluded from pension schemes. 

The National Employment Saving Trust (NEST) is a pension scheme with auto-enrolment with the main goal of serving millions of UK workers, mainly from small industries and low incomes, who did not previously have workplace pension schemes.  It is important to understand patterns of savings of these workers who have traditionally been excluded from pension schemes. 

In this work, we seek to understand whether the NEST population clusters into different patterns of pension accumulation, and the potential paths across clusters for NEST members.  We therefore represent NEST members as a network. 

By studying the structure of this network, we aim to (1) understand the characteristics of the emerging groups; and (2) estimate how groups are potentially connected, which we can think of as a proxy for potential pathways to pension accumulation. 

Our analysis revealed patterns of accumulation in 25 clusters which were further grouped as: Uninvolved members, Low-involved members, Partially involved members, Involved members and Highest-pot members. We use this analysis to identify groups that seem to be at the highest risk of inadequate pension accumulation within Nest and also to identify members that have non-traditional accumulation. 

Bio

Maria Fernanda Ibarra Gutierrez is a PhD candidate in Data Analytics and Society at the University of Manchester, where her research centres on studying the pension accumulation of low-income workers using data analytics. She holds a bachelor’s degree in actuarial science from the National Autonomous University of Mexico (UNAM). Her research is driven by an interest in using statistics and complex data analysis to explore and address social problems, with a particular focus on pensions, inequalities and gender issues.