New IRS SOI Data is Out — And Even More Improved!
The Data Just Keeps Getting Better
I covered 2013–2014 IRS SOI data a while back. But then, IRS took down their data. This was odd. I contacted the folks at the IRS to get the story on why. It turns out, they’d made a mistake all of us who use this kind of data have made at some point: used the wrong vintage of county FIPS codes. So they were taking down the data to fix the issue. Okay, that’s reasonable.
But in the conversation, I also communicated to the IRS that there was one simple way they could vastly improve their data: report the pre-migration and post-migration incomes in the gross migration file. This would emphasis a point I’ve made many times: that AGI associated with migration does not necessarily actually move. Meanwhile, it would give researchers a super-simple cursory “first look” at when and where migration is, on average, associated with income increases or decreases.
This post won’t cover the usual migration data, winners and losers, etc. That stuff will come in another post, hopefully tomorrow. Instead, I’m just gonna run a victory-lap here and talk about the fun new thing IRS did.
Migration Isn’t Money Moving
Labor Mobility is Not Capital Mobility
I want to start by offering some context for why the data change made matters. There’s a propensity among some migration commentators *cough* to portray the AGI associated with migration as moving “with” migration.
This is totally bogus. Here I’m gonna quote verbatim from a report I recently wrote for a consulting firm in Illinois. Go read the report for more detail:
IRS SOI Data Doesn’t Measure Migration of Income
It is common for migration commentators to treat the AGI of IRS SOI migrants as “migration of money.” This is an egregiously wrong use of the data. The IRS SOI user guide makes clear that this is not a viable interpretation of the data, and thus those who read the data this way have either failed to perform the most basic due diligence by looking at the manual, or else actively mislead their readers.
There are four reasons why AGI associated with migration is not equivalent to “migration of money.” First, the AGI associated with a given migrant return is the total AGI for the filer after migration. It is not how much money they earned prior to departure, it is how much money they earned after arrival (although of course, AGI is not quite equivalent to earnings, a point only worth belaboring for tax experts). Thus the AGI reported has no necessary connection to the departure state; it cannot be considered an “outflow.”
Second, many migrants experience job transitions, moving out of one job to another. This is one of the most common reasons for migration. When a migrant moves from one state to another as a result of a job transition, it is likely that they move from a job that will still exist after their departure, to a job that existed before their arrival. In other words, the AGI they had before departure is likely to more-or-less remain in the previous state (minus transition costs as the employer fills the vacancy), while their new AGI in the arrival state likely existed prior to their arrival (again, minus transition costs related to employment vacancy).
Third, insofar as “migration of money” is a real thing, any economist will tell you that it refers to capital mobility. Capital mobility is a radically different phenomenon than labor mobility. Unfortunately, figures on capital mobility within the United States are less available than figures on labor or population mobility, that is, migration. Conflating labor mobility (migration) with capital mobility is entirely incorrect, as anyone who’s taken even basic macroeconomics can explain. Labor migrants may shift their investments alongside migration, especially residential investment if they build a new home, and investment generally may respond to population changes, but the phenomena remain distinct and should not be confused by sloppy terminology.
Finally, some income, such as rental, ownership, or investment income may “migrate” with migrants. But that income type is not separated out of AGI in the IRS SOI migration files, and the largest share of investment income, namely capital gains, is realized in very lumpy and tax-sensitive patterns that may be correlated with migration across taxing jurisdictions. This money may “move,” but, depending on the exact nuances of the economic question being asked, it may be more appropriate to count capital gains on an accrual rather than a realized basis, in which case even separating investment income from AGI would not give an accurate depiction of money migration.
In sum, labeling the AGI associated with IRS SOI migration as “migration of money” is unjustifiable.
In the report, I cover three other major erroneous uses of IRS SOI migration data. So check it out.
But Migration Does Impact Income!
We Want to Know How Geographic Mobility Relates to Income Mobility
The gold-standard for migration data would be if the IRS strung together, say, 30 years of tax returns for a representative sample of filers, and let us see how migration correlated with a few key kinds of deduction and credit claims, as well as total AGI and filing status. Sadly, as far as I know, that database doesn’t exist.
In the meantime, we have IRS SOI migration data. As I’ve said before, IRS’ recent revamping of the data has been a big improvement. The analysts who work on this for the IRS are also, as it happens, very friendly and knowledgeable.
And, apparently, extremely open to taking suggestions. As I said above, I mentioned to the IRS migration analysts that including pre-migration and post-migration income would be useful, since they already collect that information anyways. I didn’t expect them to incorporate my suggestions immediately. But they did!
So we can now see, for one year at least, the average impact of migration for each state, broken down by age and income. Want to know the average income change associated with rich retiree moving away from Illinois? I can answer that! Want to know the average income change associated with a poor Millennial moving from to California? I can answer that! We can’t yet do specific state-pairs, i.e. I can’t limit to just people moving between Illinois and California. But this is still a huge improvement,
Let’s have some fun and answer some example questions!
Can you tell how giddy I am? This is fun stuff. The only more fun thing would be if you subscribed to my new migration history podcast.
Income of Migrants Rises Really Fast
There’s a Selection Bias, Obviously. But How Much of One?
Previous research has shown that migration boosts lifetime incomes. Separately, I’ve shown that migration tends to relocate Americans towards better neighborhoods. But is migration immediately associated with meaningful income changes? Let’s see!
Holy cow. AGI for migrants grew by over 4%. AGI for non-migrants grew by about 0.4%. In other words, income for migrants grew 10x as fast as income for non-migrants.
Income for migrants grew 10x as fast as income for non-migrants.
Normally I make the big quotes different than the things I write in the paragraphs. Not this time. That’s a fairly huge difference. Now, for all we know, this may be entirely selection bias. Maybe if we encouraged more people to migrate, they wouldn’t be as successful as current migrants, and so we’d find the growth rates converge. That’s not just possible, it’s eminently plausible.
Nonetheless, when we find something associated with a huge gap like this, it’s usually a good sign that somebody should be researching this question and conducting experiments. Maybe a 10x acceleration in income growth is implausible. But look, if we could even boost income growth rates by 25%, rather than 1,000% like I observe here, that would be a huge improvement. So maybe it’s just selection bias. But if it isn’t… what a massive income-boosting tool we’ve left on the table! What a heinous oversight it would be if the real impact of migration is even 1/40th the size we observe in the data! Can we afford to say, “Well, migration might increase the growth rate of income 10-fold, but there might be selection bias, so probably not worth spending money to experiment with relocation vouchers.” No! We can’t afford to say that!
Yes, IRS Data Has a Selection Bias
Yes, That Means the Effect is WAY Smaller Than Face Value Suggests
Cool story: we can explore some of that selection bias using the IRS data, because they break it down by age and income. Let’s start with income.
Oh crap. Look at that. Migration reduced the income of poor people.
Here we get the core selection bias problem. The IRS is categorizing people based on their 2014 post-migration income. People with low income are likely to have lower income than the year before; it’s unlikely you fall in a low bracket after having experienced an income boost. And vice versa for income increases. The result is a major statistical bias, wherein every year’s data will necessarily show that poor people got poorer, and rich got richer, even if they didn’t.
Now think about what could give someone low income and make them migrate. How about… losing a job. Or quitting a job. These events could artificially lower income while also triggering migration, even if migration itself boosts income relative to staying. This effect is especially large in the very lowest income group, because it also includes many filers with “unusual” filing circumstances and volatile incomes; people who may have very low income due to “losses,” for example, despite not being poor.
That’s why, when I see a 10-fold gap between migrant income growth and non-migrant income growth, I don’t assume that’s all the “real effect.” I don’t assume half of it is the effect of migration. I don’t assume a quarter, or even a 10th. I genuinely believe what a critic would say here: that selection bias is huge.
But look again at that data. Among every middle-class or high-income group, we find that migration boosts income growth. It’s true, for people making under $25k post-migration, that’ s usually an indicator of failed migration. But these failed migrations are much less numerous than successful migrations, and their aggregate impact is much smaller, economically speaking.
Migration and Income Have a Life Cycle
Surprise!
So what do those successful migrations look like? Well, we can cut the data another way, by age.
It turns out, when young people move, it’s usually associated with higher incomes. The effects are a little more ambiguous for people 35–55. For those over 55, migration tends to be associated with lower incomes. This is all what you’d expect, and suggests that the negative sign for low-incomes may need another caveat.
Retiree migration is accompanied by income reduction: but that’s not necessarily a loss in well-being.
When someone retires and their income falls, we don’t say, “Man, that poor guy, he’s not earning the big bucks anymore.” He’s retired! He’s “earning” (i.e. realizing investments and taking pensions and Social Security) when he needs to earn to live. And yes his income falls, he’s not working anymore, and he probably is quite happy about that. In other words, some of the falling incomes for migrants are associated with natural and desirable labor-force exit. Meanwhile, we can see for younger people entering the labor force, those who migrate see income growth 6 percentage points higher, or about a third. In other words, migrating Millennials get a 35% bigger raise, on average, than non-migrating Millennials.
We can also combine age and income. Let’s do it. It’ll be fun.
The above chart shows what’s really going on. Migration is a valuable strategy for the people who do the most migrating: young people. And those young people most likely to experience “successful” migration are those who are already not too poor, although, again, we should be aware that the lowest-tier bracket is quite skewed.
For people over 45, migration tends to be associated with income losses for everyone except the very rich.
This confirms previous research done by Scott Winship, which finds that migration during a crucial window of the life cycle around 18–35 is associated with with substantially higher income later in life. The same association does not hold as much for late-in-life migration.
So offering migration vouchers to unemployed 55-year-olds might not do very much after all. Again, this data can help us figure out exactly how to best structure policies that might actually help people. And from all we can see here, the best argument to be made is that any movement relocation voucher provided should probably have a gradual phase-out with age, beginning around 30 and ending around 50. There’s not really any evidence I can see that migration would substantively boost the incomes of a 50-something who’s lived most of their life in the same place.
But for people in their 20s and 30s, relocation assistance very well might help. We see the negative bars for poor people: but recall these aren’t necessarily people who were poor before migration, but people who became poor after migration. That tells us that, yes, migratory failure is possible. Post-relocation assistance will probably be necessary, even if cheaper than a purely “stationary” program. Migration does not solve every problem. But if your goal is to boost wage growth, especially for younger people, then migration can be part of a bigger toolkit.
Migration and Income Have a Life Cycle
Surprise!
Because I’m spiteful and like to stick my thumb in the eyes of people who are wrong, I’ve also got a fun state map. This is a map of states where migrants, on average, experience the biggest and smallest income gains vs. non-movers in the state. Here’s how it works: I take the percentage change in income for movers into a state, then subtract the change for non-movers in the state. Greenish states are those where in-migrants see income gains much bigger, relative to locals, than the national average. Brownish states are those non-movers make the biggest relative gains.
The next map looks at out-migrants. So instead of asking, “In which states did recent arrivals get the biggest income boosts?” it asks, “In which state did residents leaving get bigger income boosts than people staying behind?”
Which of these maps is most interesting to you will depend on what question you’re trying to answer. I won’t elaborate on their policy implications here.
But I will not that neither of them correlates at all with headline net migration. It turns out, most people leaving most states do better than if they stayed. Because migrants are heterogenous with respect to capabilities. Likewise, it turns out that most recent arrivals in a state do better than residents. Because they tend to have arrived because they have in-demand skills. In other words, migration has a role in income-equalizing around the nation, not lopsided money-migration. Such interpretations are an egregious misreading of the data.
And with that, I’ll stop. Tomorrow I’ll do the net flows stuff. But for today, just remember: improving data lets us answer new questions. And today, we got data that lets us take a first stab at some huge questions.
See my previous post, on changing geographic trends in fertility.
Check out my new Podcast about the history of American migration.
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I’m a graduate of the George Washington University’s Elliott School with an MA in International Trade and Investment Policy, and an economist at USDA’s Foreign Agricultural Service. I like to learn about migration, the cotton industry, airplanes, trade policy, space, Africa, and faith. I’m married to a kickass Kentucky woman named Ruth.
My posts are not endorsed by and do not in any way represent the opinions of the United States government or any branch, department, agency, or division of it. My writing represents exclusively my own opinions. I did not receive any financial support or remuneration from any party for this research. More’s the pity.
