I've calculated income inequality metrics (Gini/Hoover/Theil coefficients) for several populations. I know I can make claims like "this population has a higher Gini coefficient, so its distribution in this variable is more unequal than this other population."
But is it possible to make more quantifiable claims? For example, to say that "since the Gini coefficient for this population is twice as large, the variable is distributed eight times more unequally?" Or something like that?
The data isn't economic data - this has to do with relative numbers of contributions to a volutneer project. There are 5 datasets, with ~10,000 rows each. Each row corresponds to a single Internet user who did one or more tasks to contribute to that project. Some did just one, some did thousands.
The question is: of the five projects, which ones are such that a small core group has done a lot of tasks each, and which ones are such that many people have done a few tasks each to add up to the total?
Thinking about this more, maybe the Gini coefficient doesn't have enough information to make this kind of comparison. Maybe I need to compare the actual distributions of the variable among population members, using something like a K-S test?