1-Net Return on Assets VS. Increase in Consumer Price Index. 1990-2009
2-SBCERS Pension History of Economic Assumptions 1981-2009
3- Santa Barbara County Employer-Employee History of Cost 12/31/86-06/30/2009
4- Net Return on Assets VS. Increase in Consumer Price Index. 1988-2007
Hi Cathy & Gina,
I thought I would start by sharing with you both why I have always felt that mathematically it was impossible for Santa Barbara County’s pension to be in such dire straights. Honestly once I got a handle on our situation I knew I could apply the same logic to many of the other County pensions in California.
Now I write a blog that stems from my divorce and the ton of bad things that happen to me during that whole process. I started writing about my situation but honestly nobody cared about another bad divorce. At this time 2008/09 the media was filled with stories about how our public pensions suffered huge losses in 2008 which of course were later exceeded in 2009 in some cases by nearly 150%. In Santa Barbara County the pensions two-year losses exceeded 25% of its total 2007 value, thus creating the alleged future underfunded obligation crisis’s we currently face.
To me it seemed like everything was being blamed on Wall Street yet no specific venue was ever actually given. So I looked into the numbers here in Santa Barbara and I knew things just didn’t add up. After reviewing just these 4 attachments I was positive there was something a-rye here, but I admit wondering if I had missed something.
Now the first attachment shows that the 20 year average net return (actual performance) on assets ending June 2009 was 7.6% with a CPI average of only 2.8%. This of course reflects the pensions losses in 2008 of 7.2% and the 19.2% lose of 09. But was there any compensating factors and would they help or hurt the situation? How come no one else was talking about compensating factors and how they might affect the alleged pension deficit like me?
The phrase compensating factors for me has a common sense meaning so why people try to make it more complicated than that is beyond me. For example even though the data in attachment 1 supports that Santa Barbara County should have a pension deficit in 2009. That all changes when we input the positive compensating factors found in attachments 2&3? In fact after you apply these positive factors to the whole situation it actually becomes mathematically impossible for the pension to have any kind of deficit.
The two biggest compensating factors to consider are the actual CPI index factor average of 3.0% in attachment 1 vs. the assumption average of 4.75% used in attachment 2. If you look closer at the SBCERS Pension History of Economic Assumptions you can see the relationship that the CPI index has to the pensions real net return. If the CPI assumption was to low then the real rate of return is reduced by the difference. However if the CPI assumption was higher than the actual number the real rate of return should increase by the difference.
So in our case the 20 year CPI assumption averaged 4.75% and the actual 20 year average index came in at 3.0%. In other words we have a positive compensating factor of 1.75% which more than makes up for the under performing 20 net return on assets of 7.6% by 1.15%.
But wait a minute attachment 3 column 4 UAAL rate shows that since 1988 Santa Barbara County has also been over contributing to the pension because of the alleged unfunded future obligation. So we now have another positive compensating factor that eliminates the alleged damage caused by the under performing (7.6%) pension fund.
Finally we have to look at attachment 4 and what it represents. This document shows that the pension up till June 2007 had actually earned a 10.1% for 20 years easily exceeding the desired 8.15%. Yet attachment 3 columns 4 UAAL still shows that the county was over contributing during the entire 20 year period represented in this document. So the math should look something like this: 20 years x 10.1% (not 7.6% or even 8.15%) add the positive impact from the lower than assumed CPI index (1.75%) plus the additional contributions found under the UAAL column and I think you to will agree with me that it is mathematically impossible for the SBCERS pension to be anything but fully funded with a rather healthy reserve.
Now what happens if I do the math, well in these 2 examples there is over 600 million dollars difference between the 2. That does not factor in the 20 years of additional contributions made by Santa Barbara County because of the alleged deficit represented in attachment 3 column 4 under UAAL.
Example 1 will use the assumption factors;
Let’s use a starting amount of $270,540.000.dollars. After 20 years of earning an average net return of 8.15% compounded semiannually the pension funds final balance on June 2007 should be 1,336,873,000.
Example 2 actual fund performance factors;
Again we start with a fund balance of $270,540,000, and after 20 years of earning 10.10% return on assets compounded semiannually. The pension’s final balance June 2007 would be an amazing 600 million more than what would have been earned at the desired rate of 8.15%, coming in at 1,941,223,000.dollars.
Ladies I hope I did not take the long road with my explanation; truth is this is not even the tip of the ice berg. But let me leave you with this, if the difference between the assumption and actual CPI index was as high as 1.75% in Santa Barbara, it makes sense the other pensions over shot their CPI assumption as well. In fact since 2010 most pensions have tried to convince the public they have actually lowered their assumptions for asset returns. But actually all they have really done is lower their CPI assumption from the 4% area down to the 3% area, where is all the money?
FYI CPI data for your review.
|The table below shows the average inflation over various periods, ending June 30, 2011: Periods Ending June 2011||U. S. City Average Annual Increase in CPI-U|
|Last five (5) years||2.15%|
|Last ten (10) years||2.40%|
|Last fifteen (15) years||2.46%|
|Last twenty (20) years||2.57%|
|Last twenty-five (25) years||2.94%|
|Last thirty (30) years||3.09%|
|Since 1913 (first available year)||3.23%|