RSS Oxford talk on Intergenerational fairness of pensions: Statistical analysis, assumptions and actuarial models
Prof Jane Hutton
Tuesday, 19 November 2019, 4.30pm to 6pm
Large Lecture Theatre, Department of Statistics, 24-29 St Giles', Oxford OX1 3LB
Hosted by RSS Oxford local banch
Join the Oxford local branch of the Royal Statistical Society for this talk aimed at academics and non-academics with an interest in statistics. Professor Jane Hutton will speak about Intergenerational fairness of pensions: Statistical analysis, assumptions and actuarial models.
Refreshments will be served from 16:30. The RSS Oxford local group's AGM will be held at 16:45. The talk will begin at 17:00.
Professor Jane Hutton of the University of Warwick works in medical statistics, with special interests in survival analysis, meta-analysis and non-random data. Her methodological research largely focuses on developing models to answer questions raised by healthcare professionals and patients. She has written extensively on the ethics and philosophy of statistics, in response to challenges arising from medical research and discussions with international statistical colleagues and philosophers, and has contributed to Research Council ethics guidelines.
Intergenerational fairness is an important and ancient concept in society and government. With respect to pensions, intergenerational fairness as well as fairness to women of a certain age have been debated when considering changes in state pension age.
Funded defined benefit schemes which are estimated to have deficits are required to impose deficit recovery payments or change benefits. Current members pay not only for their own future pensions, but also for their predecessors' (and their own) accrued entitlements. It is often assumed that this means younger people paying for older people. However, older generations' pensions contributions have provided productive capital investment and infrastructure used by all ages. Strict intergenerational 'fairness' within a scheme might neglect wider social balance.
Actuarial models require assumptions in order to estimate assets, liabilities, life expectancy and other demographic factors. Multiple assumptions biased away from statistically valid estimates can substantially increase an estimated deficit. Consequences of these assumptions affect not only the particular scheme's stakeholders, but wider society. Money used for deficit recovery payments is diverted away from business investment and dividends. A large estimated deficit can bankrupt a company, and put many people out of work. If pension contributions are tax-exempt, the government's income is reduced.
Assumptions underlying actuarial models are not merely economic or statistical.