I really like the idea of having the readers draw their assumption out. A great approach to creating conscious engagement with a complex topic.
The scales in this particular example are problematic, though. By using a logarithmic scale for the x dimension, the non-linear relationship between income and college attendance is hidden to the majority of readers. Logarithmic scales are hard to grasp for most people not working with numbers all day. Having percentages on both axes but only one of them being linear further obfuscates the variable relationship.
It's income rank, rather than actual income. In my explanatory text they mention that the difference between two points on the far left is a few hundred dollars and the difference between two points on the right is a million or so (I forget the actual numbers).
As jacalata described, the actual income differences in dollars are not distributed linearly across the y axis. Each percentile interval represents a different dollar interval.
The y axis uses percentages, too. But it displays an absolute number of people, so the number of people in the 10-20% bracket equals those in the 80-90% bracket and so on.
The actual number of children won't be the same for each income bracket, but the are evenly distributed on the y axis. I think :)
The scales in this particular example are problematic, though. By using a logarithmic scale for the x dimension, the non-linear relationship between income and college attendance is hidden to the majority of readers. Logarithmic scales are hard to grasp for most people not working with numbers all day. Having percentages on both axes but only one of them being linear further obfuscates the variable relationship.