Councils might have overpaid into staff pension funds by millions of pounds a year because of lifespan predictions based on inadequate data, an information-sharing group has claimed.
Initial findings from the first batch of local authority pension funds that have signed up to Club Vita, an offshoot of consultancy Hymans Robertson, show that funds made unnecessarily large contributions based on the most recent three-yearly fund valuations, which took place in 2007.
A total of 26 local authority funds are now members of the scheme, which matches existing fund data with geo-demographic profiles that are accurate down to postcode level.
Results from the first 18 councils that have had their mortality profiles calculated show potential average savings of £2.8m a year, with the majority of those authorities achieving contribution reductions of between 0.5% and 2.5%.
So far, Norfolk Pension Fund has notched up the highest potential reduction of £9m, or 2.4%, in its annual contributions. Alex Younger, fund accountant, said he did not expect the council would reduce its contributions by that amount.
However, he did expect the Club Vita data to inform the levels required from 2011, as these would take into account next year’s valuations.
Mr Younger said Norfolk had approached Club Vita as a governance exercise to demonstrate that it was properly managing the risks associated with accurately predicting the life expectancies of its scheme’s members.
“We have some positive numbers, but there are a whole range of other pressures out there, which we’ll find out in 2010,” he said.
“This information will allow us to come out of that with a balanced view, and it is nice to know from the much more detailed information that there aren’t any nasty surprises out there that our earlier assumptions hadn’t picked up.”
According tow Douglas Anderson, a partner at Hymans Robertson, which set up Club Vita, none of the local authority funds has so far been found to be under-contributing - unlike some private sector member funds.
But he said that marrying existing fund data with consumption and health profiles from such information sources as store loyalty card databases - highlighting, for example, a population’s tendency to drink and smoke - resulted in invaluable profiling potential.
“There’s a perception that people living in nice, leafy rural areas of England have longer