If what you have are truly populations and subpopulations, the word "significance" in the sense of "significance test" is meaningless. The purpose of signifcance testing is to make probability-based inference about unobservable population quantities on the basis that (say) the population was randomly sampled, or randomly allocated to treatments (or whatever else the basis for the probability model is) - to use probabilistic reasoning to try to answer a question like "do the population proportions differ?".
If you actually have the population (as you state), the population quantities are not unobserved, but known, without error. You can determine if they differ by simple examination - if they are not identical, then they differ (e.g. 0.430 is greater than 0.429, even though that may not be an important difference). That is, you can immediately see the answer to the question that statistical inference is attempting to infer.
In order for probabilistic reasoning to take meaning, you'd have to make some kind of sampling assumption about drawing from some even larger population ... but then you don't have the population about which you wish to make inferences and the claim that you did is false.
So either drop the claim that you have a population (in which case you're stuck trying to explain how what you do have could be seen as a sample about which you could make a probabilistic argument), or drop any attempt at probabilistic inference and simply compare the proportions directly, focusing instead on the practical importance of any differences.