And now for "the next post". If you read the previous post, you'll be familiar with these two maps:
|Change in Aggregate Gross Income (AGI)||Change in Returns Filed|
Left map is net aggregate gross income that has entered (blue through purple) or left (orange through red) a state in 2011. That means that, if a state is blue through purple it gained more aggregate gross income, according to the IRS, than it lost. Right map is same concept, except in number of returns filed. So, the left map presents money and the right map presents people. As you can see, they're very similar. The movement of income is a close (but not perfect) match to the movement of people. (Holding your mouse over the map will pop up the specific state's change in income or returns.) Data for these maps and all other data for this post still comes from the IRS migration data.
How close is this match? For those of you who aren't nerds, there is a number called a correlation coefficient. It can be calculated for any two linked sets of numbers. It's a rough measure of how close one set follows along with the other. If you square it, you get what's called a coefficient of determination. For no good reason, this is abbreviated as R2. This final number is a measure of how much one set of numbers "explains" the other. You don't need to worry about the details. All you need to know is that if the R2 is zero, then the two sets of numbers are unrelated. If the R2 is one, then when one set of numbers goes up, the other set will also always go up, and that this will happen at the same rate. Another way of looking at it is if that the R2 is one, you could draw a perfectly straight line on the data if they were plotted against each other.
What does this particular R2 turn out to be, anyway? For net returns vs. net annual gross income, the R2 is 0.948, which means that nearly 95% of the gain or loss in annual gross income in a state goes along with gain or loss in number of households in a state. This is important, since we presume that income follows people. In this case, the presumption holds. Basically, when we talk about income moving, we're still talking (almost completely) about people moving.
However, there's a lot more to this situation than just income and people. After all, the two maps are only very similar, they're not identical. The rate of people movement isn't the same as the rate of income movement. Fortunately, there is a way to lay this out plainly. We can look at change of annual gross income per return. Now, to make this following map, I had to weight the calculations by state.
|Change in Aggregate Gross Income per Return|
What this map shows is the net change in terms of dollars per return for each state. A state that is blue through purple has probably attracted people with higher incomes (overall) than those it lost. A state that is orange through red has ended up (overall) getting people with lower incomes than moved out. This map should have some surprises for you unless you are amazing at doing math in your head buy guessing at numbers that correspond to colors. California is a big loser when it comes to aggregate income and people, but when we look at the change in income per return, it's not such a heavy loss. New York and Illinois are a big losers, either way. Alaska really leaps out. They had a slight loss of income, an overall gain of people, and a very visible drop in income per return. I'll leave it to the Alaska legislature to work out the implications of that one.
When we look at winners, there are more surprises. First, the Rocky Mountain states really stand out as a group. States that had net gains in population or income have huge net gains in income per household, and even states with net losses in population (Utah, but a mild gain in aggregate income) actually gain in terms of income per return. On the other hand, Texas, which is so often touted as some economic New Eden because of these income migration maps, only sustains moderate gain in terms of overall income per household. Florida, though, just blows the doors off in all measures, and South Carolina's weaker population gain is so heavily offset by its gain in income per return that it visibly alters the aggregate income gain. But there is another interesting little detail from comparing these two maps, one that might not leap out at you. What is it? Forgive me, but I'm going make that wait until the next post.