Slightly related to previous questions by others, but more theoretical/hypothetical.
Is there an accepted phrase, or perhaps a decent argument to which you can kindly refer me, for including two independent variables that are somewhat collinear "because of the way the world is", when you still want to include the effects of both because you feel (for theoretical reasons), that both are important?
Let's say you are regressing to predict suicide rates (among 100 towns), and want to include both income poverty (town means) and percentage of households with piped water (per town), among other variables. Now these two variables run together, perhaps correlated at ".8". We understand why they are positively correlated, and we understand that they will present a pretty high VIF score. Let's say a VIF above 10. So, my reading suggests I should drop one of the two variables. But, surely, there is an argument to say they also measure subtly different things?
So - is there a standard way of explaining that one "knows that two variables will be collinear and correlated, because that's just how things are in real life", but that you retain both nonetheless because they are not, in reality cognates, and introduce important nuance into a regression?