How Can Workers Comp Statistics Madness Be Cured?
One of the supposedly important Workers Comp Statistics based articles appeared last week. The study and article compared (Affordable Care Act) Medicaid expansion states with states which decided not to allow the expansion. The conclusion drawn was:
States that allowed Medicaid expansion under the Affordable Care Act experienced a lower Workers Compensation Loss Ratio than the states that did not allow Medicaid expansion.
I coined the madness term back in 2013. The madness originated from other articles and studies, not insurance or WC.
The main thesis consisted of comparing the Workers Comp Loss Ratios <<<not one of my favorite workers comp statistics. The reason why consists of better statistics (hundreds) provided by actuaries and rating bureaus. If you disagree, please use our contact us or comment on the article. Many proponents think it is one of the best workers comp statistics. I think otherwise.
The researchers often miss the intervening statistic. The old study example of ice cream sales compared to assaults comes to mind. The more ice cream sold equates to higher assault rates. However, the Summer is the intervening statistic or :
- Higher Ice Cream Sales = Summer
- Summer = Higher Assault Rates<<<due to more people outside in public
- So, higher ice cream sales results in higher assault rate
Studies of this type litter the Internet. That concerns me.
The E-Mod is a delayed system. You cannot tell actually what happened fully in any state with WC until 4 years after a policy commences. The aforementioned loss ratios are not designed to be a rating factor of any type. My actuarial background says no.
If your company is self insured, then the Loss Development Factor (LDF) reaches further into the past – up to 10 years. To say the Loss Ratio produces a premature measurement is a large understatement.
Therefore, as with ice cream and assaults, one should examine other intervening variables – LDF or E-Mod. Workers comp statistics can be great – with the right ones.
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