My avatar suggests I should be uplifting.
But I did want to know how our membership would suffer as a consequence of:
· University of Adelaide prediction of the loss of 24000 jobs as result of GMH closure – presumably within the Adelaide metro area, at the completion of 2017
Here are the assumptions I have made
· Those members who lose their jobs will not be financially able to buy membership
· All jobs will be lost in the metro area and referenced to a 2015 membership of 60000
· 85% of our 60000 live in the Adelaide metro area (population 1.3million) – A GUESS
· The % of PAFC members in Adelaide metro equally applies to the 24000 who will lose their jobs = [85% x 60000]/1300000
· Using the fact that closure of the Mitsubishi plant resulted in only 1/3 of those who lost their jobs being ever employed full time again. The rest (66.6%) remain unemployed or under-employed
· So the number affected will be MINIMUM: 66.6% x ([85% x 60000]/1300000) x 24000
· I have also calculated a MAXIMUM number of people affected by assuming that every 3 members is responsible for a 4th membership (husband, wife son, daughter etc). So every member is on average responsible for 1.33 memberships - A GUESS. In this case the number of memberships affected is: 1.33 x 66.6% x ([85% x 60000]/1300000) x 24000
On this basis
MINIMUM NUMBER OF PAFC MEMBERS AFFECTED BY GMH CLOSURE: 627
MAXIMUM NUMBER OF PAFC MEMBERS AFFECTED BY GMH CLOSURE: 833
MIDDLE NUMBER OF PEOPLE AFFECTED: 730
If I knew the weighted average of membership fees I could attempt to monetise this number of people, but for now I will monetise in terms of “Platinum Membership Equivalents” I have set Platinum membership at $500 for the 2018 season (the 2016 Platinum Membership compounded at an inflation rate of 2.75% over 2 years)
MINIMUM $ EFFECT: $314,000
MAXIMUM $ EFFECT: $417,000
Remember also that this may be reflected in merchandise sales, with an add-on effect of something like
[merchandise profit/60000] x (627 or 833)
Of course you can view this data any way you want – even dismissing it on the basis that lost memberships will be taken up by our waiting list. But we know that waiting lists can be surprisingly fickle, disappearing in poor economic circumstances or even if the team performs at less than the expected outcome.
I certainly write some dry stuff on this blog site. Beware, I am analysing what the SA government's predicted 9.3% unemployment might do to our membership at completion of 2017. Much more difficult because the 9.3% value quoted is garbage (including people who work 1 hour a week as employed is nonsense).