Suppose I am interested in the follows: I have county-level data. For each county, I know the share of the population that was born from one parent and the share of the population that was born from two. Suppose I am interested in the impact of the black population on tax revenues. In particular, suppose I am also interested in the impact of the share of the population born from two black parents separately from the share born from one black parent. I am using OLS.
The problem is that the share of the population born from one and two black parents is highly correlated, resulting in a near multicollinearity problem.
Of course I can proceed by just testing the total share of the black population, and that is a result in which I am inherently interested. However, if I include the 1 and 2 parents as together as two regressors, the results seem meaningless relative to the total share because the 1 and 2 parent variables produce a near multicollinearity.
Should I run two separate regression? A regression on the share with 1 parent and a regression on the share with 2 parents? Or is there nothing that can be done because of the near multicollinearity?
Thank you very much for any help you can provide.