Mark Johnstone writes:
I’ve recently been investigating a new European Court of Justice ruling on insurance calculations (on behalf of MoneySuperMarket) and I found something related to statistics that caught my attention. . . . The ruling (which comes into effect in December 2012) states that insurers in Europe can no longer provide different premiums based on gender. Despite the fact that women are statistically safer drivers, unless it’s biologically proven there is a causal relationship between being female and being a safer driver, this is now seen as an act of discrimination (more on this from the Wall Street Journal).
However, where do you stop with this? What about age? What about other factors? And what does this mean for the application of statistics in general? Is it inherently unjust in this context?
One proposal has been to fit ‘black boxes’ into cars so more individual data can be collected, as opposed to relying heavily on aggregates.
For fans of data and statistics, the law poses some interesting challenges. And I’d love to see somebody digging into this further from a statistical point-of-view.
I don’t have much to add here, beyond the usual Bayesian point that, if we have enough data on individuals, this will be more important than average rates, and also the usual political point that good information might not get used if the rulemakers have particular sympathy for unsafe drivers.