August 12, 2014

Tax Migration--Get to the Point, Already!

For the three people who have managed to get this far, I really do have a point to these entries. This is a lot of stuff for people to get through, so I broke it up in the hope that it would be more digestible. The first entry of this series of three pointed out that so-called "income migration" really mostly reflects people migration. The money isn't magically moving around all on its own. It follows people. My followup, which was longer and more boring, pointed out how closely money and people move together (almost 95% of the change in aggregate income went along with change in households, as estimated by tax returns). Then I went on to show a harder to see set of changes, the way that migration altered the average household income of a state. If you look at the states by changes per households that migrated, some of the "big winners" and "big losers" weren't so big. I also pointed out a few states that "lost while winning" or "won while losing". Their total income change went in the opposite direction to the income change per household. Some states got more money overall but gained poorer people while other states lost overall income but gained richer people. Is that good? Is it bad? I got no clue, but it is something to consider if you play the "let's compare the states" game.

And so what? I'm getting to so what, but to do that, I need to introduce a new idea, with new calculations and maps. Have I expressed my sheer joy that people cannot throw rotten vegetables at me through the internet? I do apologize, but this needs to be done to make the point. While it might be useful to look at absolute changes in things like aggregate income, population, or income per household, the real impact of such changes depends upon how big (in number of people) the state that undergoes these changes already is. It's one thing for a state to have a net loss of $5,000 income per household of people who moved into or out of the state if the number of people who moved in or out makes up 1% of the whole state's population. It's a much bigger deal if those people end up being 10% (ten times the other state) of the state's population.

This very important difference is hidden when you report absolute changes. The actual impact on the state will disappear because you simply subtract out the people who stayed put! If you do the math to measure changes as a percent of the state's total population, income, etc., you will factor in a sometimes very large number of people and their income. The picture can change, and it can explain how states act much better than can absolute difference maps. Even absolute changes can be different if what you are comparing is an absolute change in ratios (like income per return) but don't take into account what changed for the people who stayed put.

What I mean is that my last two posts were entirely restricted to the differences of migration and ignored the people who never moved at all. If you think about it, it doesn't make sense to stop there. I'll just cut to the chase on this: If you look at states, proper, the biggest change in number of households amounted to less than one percent of the total population. That means over 99% of the population stayed put. The overall effect on a state due to migration could really amount to no more than a teaspoon in an ocean. So what happens when you do take into account the whole population of a state? We'll first compare aggregate gross income:

Change in Aggregate Gross Income from Migrants Only Percent Change in Aggregate Gross Income Including non-Migrants

As always, you can roll your mouse over a state to get a little more detail. I use percent on the right because that better reflects the idea of "relative change". If a jump of one million is a hundredth of one state's economy but is only one tenth of that proportion of another state's, then the "felt effect" of the jump would be bigger in the second state, even though the same amount of income might have moved. So, what does this say? In terms of relative change in aggregate income in a state, Florida still gets to give the finger to everyone else. In absolute and relative terms, Florida is bringing it in. Texas is still doing pretty darn well in terms of aggregate income change, but it's not all that special. It's really in a pack with the Rocky Mountain states and some of the southwest. The Carolinas and Tennessee, and New England, also turn out better when looked at this way. When it comes to losers, California is not doing so bad, overall. It helps to have a giant population that can absorb losses, but who knows how long that will work? New York also doesn't look quite as bad, but it still looks pretty bad. The Midwest, on the other hand, is hurting more in terms of aggregate impact than in absolute losses. It loses and has less reservoir to spend off. The big contrast, though, is Alaska. In terms of crude aggregate income, it barely lost anything. When the state as a whole is looked at, Alaska lost pretty badly.

Of course, this only looks at one element, overall change in income. Since I already presented change per household, what does that look like when we consider the state as a whole and include people who didn't move?

Change in Income per Household from Migrants Only Percent Change in Income per Household Including non-Migrants

This is where things get interesting, really interesting. Regardless of "reality", the map on the right is probably the best visual summary of how the people of a state are likely to feel about their economic well-being in 2011 vs. 2010. It doesn't matter how much a state might lose or gain overall if the difference per household doesn't match. Likewise, it doesn't matter how much each household coming in or leaving might have if the state as a whole completely swamps their effect. Another way of putting it is that if over 99% of the state's households have a net gain in income, the state as a policy unit simply will not care if that less than 1% who moved in has a much lower income--or vice-versa.

What does the new map (on the right) say, then? First, guess what, Florida, in terms of overall personal prosperity, 2011 may have been a good year for you, but you're not the king. The big winner is Colorado. The Rocky Mountain states and some of the Southwest just blow the rest of the country away in terms of household income change--if you take the people who stayed put into account. Sorry, Texas, but however much total money and people you may have pulled in, per household improvement is really weak if you take the Texans who stayed in Texas into account. New England is even better than it previously looked, particularly since Massachusetts's crappy situation is balanced by a dense population that overall didn't do so badly. New York can't brag, but they aren't nearly as stinky when looked at per household, including New Yorkers who stayed put. California can pretty much just shrug. It's got such a large population that the per household effect of 2011 almost gets lost. The big loser on a per household basis is Alaska. Not only did migration hurt, but the state as a whole lost a big percent of household income between 2010 and 2011. Some interesting tidbits pop up in the Midwest. Indiana, for example, is a loser in aggregate income and a (very) mild loser in income per household, unless you take the Hoosiers who stayed put into account. At that point, while its aggregate income loss didn't change, it actually noticeably gained in terms of income per household. There are some other states that flipped like this, in either direction, depending on whether or not you took total population into account.

So, the point? That Californians are great and Texans suck? That Florida isn't all that? That Colorado is the bee's knees? None of the above. The point of all these maps and all these words is to try to convince somebody to not trust these maps and their accompanying words. We have become (mostly) savvy to spin. We understand that nearly anything can be explained away. We don't trust the "why". But we still get so easily suckered in by a flashy "what". Produce a spiffy colored map with popups and a citation to data, and it will be touted by one "side" and ignored by the other of some political debate, you can count on it. Most people just don't have the background to ask "Is this really the best way to make the conclusion that's being pushed?" That's why I presented the exact same source data just turned around slightly differently. Each little turn produced a different "result". I didn't alter the data. I just presented different bits of it at different times. In some cases, whether or not a state "gained" or "lost" depended entirely on how I chose to slice the data.

That's my agenda. I just wanted to give vivid, colorful examples of how you can use the same data set to say very different things. I expect to be ignored, not passed along, never linked to, and simply forgotten. After all, these three posts would be difficult to use by either of the two pseudo-sides of the American political non-debate. But it's something I wanted to get out there, hoping that somebody might see it, make the effort to read it, and get this point.

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