The IRS does a clever thing with the tax returns it receives. Each tax return has a tax payer ID and an address; the IRS uses this to figure out year-to-year migration patterns. The assumption being, when a tax payer reports a different address from one year to the next, this indicates that they moved. The IRS aggregates this data at the state and county level; so for example, they can determine how many people moved from California to Florida in a given year. For privacy reasons, there are limitations: counts below a certain cutoff (typically 10 or 20 returns) are suppressed.
The good thing about this data is that it tracks the migration status of every taxpayer in the county. There are other migration data that use surveys (for example, the American Community Survey), but these represent a subset of the population; the results are extrapolated and can have very large margins of error. The IRS data have no margins of error – it counts every taxpayer.
The bad thing about this data is that it only counts people who file federal tax returns (and their dependents). This excludes people who don’t earn enough to file a return, those who get paid “under the table”, and an unknown percentage of undocumented workers (some do file federal tax returns). So with the proviso that this data excludes a particular slice of the population, let’s dig in.
I’ll look first at general types of migration. For a given year, someone can either stay put, move somewhere else in the state, or move out of the state (either to another state, or a foreign country). Let’s look at California. For 2021 to 2022 (latest data):
| California Migration, 2021 – 2022 | |
| Stayed | 95.0% |
| Moved within State | 2.8% |
| Moved out of State | 2.3% |
| Total | 100% |
| Moved in from another state | 1.3% |
So 95% stayed in the same county, 2.8% moved to another county within the state, and 2.3% left the state, which totals to 100%. The last row represents those moving to California from other states – note that it is significantly less than the percentage who moved out. Numbers like this don’t necessarily mean that a state is losing population – there are other factors (birth and death rates, and immigration not captured by the IRS data). But in this case, it’s true: California’s population dropped from 2021 to 2022, and the 1% difference in migration played a big part.
We have data for all the states, over a 10 year period. I’ll list the migration averages, per year, over that decade. The column headers are as follows (the columns are all sortable):
- Stay In State – percentage of residents who stayed put, either not moving, or moving within the state
- Move out – percentage of residents who left the state. These first two columns will add to 100%.
- Move in – new residents who moved into the state, as a percentage of the state’s population
- Turnover – How much migration is happening in this state, in either direction. The sum of Move Out and Move In.
Note: 1The IRS data makes the distinction between people who moved to another county in the state, and those who staying within the same county. This distinction turns out to be problematical when comparing states. Some states have most of their population concentrated in a single county; because one county dominates the population, intra-state county-to-county moves are rare. Other states have several large-population counties, so county-to-county moves are more common. It has nothing to do with migration patterns, and everything to do with how the state happened to have designed its county lines. So for the purposes of state comparisons it’s best to just use two categories: people who stayed within the state (whether or not they changed addresses) and people who left the state.
Average yearly migration, 2012-2022
| State | Stay in state | Move out | Move in | Turnover |
|---|---|---|---|---|
| Michigan | 98.34% | 1.66% | 1.52% | 3.18% |
| Ohio | 98.19% | 1.81% | 1.69% | 3.50% |
| Wisconsin | 98.14% | 1.86% | 1.80% | 3.66% |
| California | 98.08% | 1.92% | 1.36% | 3.28% |
| Minnesota | 98.07% | 1.93% | 1.82% | 3.74% |
| Texas | 98.04% | 1.96% | 2.53% | 4.49% |
| Pennsylvania | 97.98% | 2.02% | 1.85% | 3.87% |
| Indiana | 97.87% | 2.13% | 2.18% | 4.32% |
| Maine | 97.74% | 2.26% | 2.85% | 5.11% |
| Iowa | 97.65% | 2.35% | 2.22% | 4.57% |
| Alabama | 97.58% | 2.42% | 2.69% | 5.11% |
| Illinois | 97.55% | 2.45% | 1.60% | 4.04% |
| Kentucky | 97.47% | 2.53% | 2.55% | 5.08% |
| Massachusetts | 97.46% | 2.54% | 1.98% | 4.51% |
| Missouri | 97.43% | 2.57% | 2.59% | 5.15% |
| Louisiana | 97.42% | 2.58% | 2.05% | 4.63% |
| Nebraska | 97.36% | 2.64% | 2.43% | 5.07% |
| New York | 97.33% | 2.67% | 1.50% | 4.17% |
| Arkansas | 97.33% | 2.67% | 2.89% | 5.57% |
| Oklahoma | 97.32% | 2.68% | 2.88% | 5.56% |
| New Jersey | 97.32% | 2.68% | 2.20% | 4.88% |
| West Virginia | 97.31% | 2.69% | 2.47% | 5.17% |
| Florida | 97.26% | 2.74% | 3.81% | 6.55% |
| Utah | 97.20% | 2.80% | 3.07% | 5.87% |
| Mississippi | 97.20% | 2.80% | 2.56% | 5.36% |
| Tennessee | 97.17% | 2.83% | 3.54% | 6.37% |
| Connecticut | 97.10% | 2.90% | 2.50% | 5.40% |
| North Carolina | 97.08% | 2.92% | 3.62% | 6.54% |
| Georgia | 97.07% | 2.93% | 3.39% | 6.31% |
| South Carolina | 97.00% | 3.00% | 4.26% | 7.26% |
| Oregon | 96.94% | 3.06% | 3.52% | 6.58% |
| Washington | 96.92% | 3.08% | 3.46% | 6.54% |
| Vermont | 96.86% | 3.14% | 3.17% | 6.31% |
| Arizona | 96.84% | 3.16% | 4.14% | 7.30% |
| South Dakota | 96.80% | 3.20% | 3.50% | 6.70% |
| New Hampshire | 96.75% | 3.25% | 3.72% | 6.97% |
| Rhode Island | 96.72% | 3.28% | 3.00% | 6.28% |
| Montana | 96.68% | 3.32% | 4.19% | 7.50% |
| Maryland | 96.65% | 3.35% | 2.98% | 6.34% |
| Idaho | 96.59% | 3.41% | 4.90% | 8.32% |
| Kansas | 96.51% | 3.49% | 3.09% | 6.58% |
| Delaware | 96.37% | 3.63% | 4.46% | 8.09% |
| Virginia | 96.30% | 3.70% | 3.54% | 7.24% |
| New Mexico | 96.25% | 3.75% | 3.51% | 7.27% |
| Colorado | 96.11% | 3.89% | 4.32% | 8.21% |
| Nevada | 95.93% | 4.07% | 5.16% | 9.23% |
| North Dakota | 95.26% | 4.74% | 4.26% | 9.00% |
| Wyoming | 94.96% | 5.04% | 4.82% | 9.86% |
| Hawaii | 94.77% | 5.23% | 4.48% | 9.71% |
| Alaska | 93.96% | 6.04% | 4.85% | 10.89% |
There’s a lot to parse here. Let’s start with the “stay in state” numbers. Four of the top five “stay” states are in the Midwest: Michigan, Ohio, Wisconsin, and Minnesota. Why would citizens of these states be homebodies? It could be cultural – roots, family ties, however you want to word it. Additionally, these states all have medium-large populations, and a mix of urban/rural living. This implies a wide range of industries and occupations. So if your current career isn’t working, there is less need to move out of state to start a new one.
At the other end of the list are Alaska, Hawaii, Wyoming, North Dakota, Nevada. States with small populations, and a lack of variety in industries and occupations. Fewer locations and jobs to “start again”. So if things aren’t working out, you’re more likely to move to another state, not just to another region in the same state. That’s my theory, at least.
This is bolstered by another survey, the US Census Annual Social and Economic Supplements (ASES). In this survey, people who moved during the year are asked for the reason, from a list of twenty. It turns out there’s a distinction between moving within a state and moving to another state. If a person moves to another state, it’s five times more likely that the reason is job related, compared to people who move within a state.
Summarizing. Top of the list: largish states with varied economies, and perhaps a regional inclination for sticking around. Bottom of the list: small states without much economic variety – if things don’t work, you bail on the whole state. Sounds plausible, but I can’t prove it.
As for California, what about the idea that everyone is abandoning the Golden State? California has the forth-lowest Move Out rate, so that seems to put the lie to that narrative. But it also has the lowest Move In rate. So really, the problem isn’t that so many people are fleeing California, it’s that so few are coming in. As for why people are leaving, from the ASES survey the answer that stands out above all others is that housing is too expensive. Pretty sure we knew that already.
You might remember, the year (1974) I moved to San Diego there was a strong anti development and anti-urban Sprawl vibe going on. I remember the radio stations broadcast traffic news provided by “Construction Industry Hard Hat Patrol” to promote positive relations. SD county certainly was very attractive and different fifty years ago compared to now.
All these years, I’ve thought there must be a concept in the field of urban planning that calculates the ideal balance of population to available resources. And for SoCal I would say people / water might be the first balance to factor. Air pollution / acre / person, maybe? Waste water / acre / person, open space / urban , etc.
Also, I subscribe to a couple of California farm industry news feeds, that industry has strong water reduction pressure, and growing overhead expense. Small wineries are in very serious shape now. Mostly due to over supply and lower demand however.
Clearly Cal has much of the best food growing land in North America. Is there analysis that weighs, should an water acre foot go to crops, or to condos?
Hope this might be some inspiration. Always enjoy your data dives.
Brad