Every State Has Its Price, 2015 Edition
Real Income Growth in America
UPDATE: Here’s this same data, but shown for metro areas instead.
I was reminded today by a tweet from the Tax Foundation that the BEA has updated their regional price parities, showing which states have the highest cost of living. The Tax Foundation’s map is here:
Red means your money goes further (i.e. the state is cheaper), yellow means your money goes less far (i.e. the state is more expensive). Unsurprisingly, the west and northeast are pricier, while the “heartland” of the Ohio, Mississippi, and Missouri river valleys are cheaper. No shockers.
But we can also look at where price levels have changed the most since 2008. Here’s a map of state-level implicit regional price deflators, which basically show how much the purchasing power of $1 has declined since 2008:
Purchasing power in the center of the country has fallen rather more sharply than in the southeast. But while this map is neat enough, I’m not sure it really tells us lots. The chart below takes these price parities and applies them to personal income, showing household income per capita in each state in real terms (purchasing power) as well as nominal terms (raw dollars) in 2015.
The richest regions in the nation are Connecticut, DC, North Dakota, Massachusetts, Wyoming, South Dakota, Nebraska, New Hampshire, Alaska, and New Jersey. Is that the top-10 list you had in your head? Meanwhile, the poorest regions are New Mexico, Mississippi, Utah, Arizona, Hawaii, Idaho, West Virginia, South Carolina, Nevada, and Kentucky. Those poorest-10 may be more in keeping with your mental model, but I would guess many people were surprised by Utah, Arizona, Idaho, Nevada, or especially Hawaii.
Controlling for prices, many supposedly high-income states like California or New York end up middle-income. Mississippi moves up from dead-last-by-a-mile to being tightly clustered alongside several other states. Controlling for local purchasing power both substantially changes our perception of which states are rich or poor, but also results in a greater clustering of income levels. The standard deviation of per capita incomes shrinks from $8,300 to $5,300; or from about 17% of mean incomes to 12% of mean incomes.
We can represent this another way for people who prefer X-Y plots:
Here we can really see what an outlier Hawaii is. Its nominal income is extremely high compared to its real income. This is almost certainly due to the Jones Act driving prices in Hawaii sky-high. This graph makes clear that nominal and real incomes are pretty closely correlated, but the main sequence of the series is fairly wide. So, for example, in nominal terms, South Dakota is not particularly well off at all… but in real terms, it’s #5!
We can also look at how incomes have changed since 2008. Here’s percentage change in nominal incomes 2008–2015:
North Dakota rocks! California and New York make very strong performances as well. North Carolina, Arizona, Nevada, Florida, and Connecticut are less stellar.
Here’s growth in real incomes 2008–2015:
More-or-less the same pattern, though all growth rates are lower. One fascinating tidbit though: North Carolina has had negative real per capita income growth since 2008. That surprised me quite a bit. I would not have thought that the Rust Belt was a cluster of strong real income growth since 2008 or that North Carolina was a major underperformer.
There’s a running debate about how to interpret these figures. Are price levels high because high incomes bid them up, especially given land use rules? Or are incomes high because of price levels? Personally, I think there’s some reason to think both factors are at work.
Consider Hawaii. Hawaii cannot receive goods from the U.S. unless they are shipped on U.S. shipping, which greatly boosts prices. Plus, it’s just very far away. On its own, as a sovereign nation, this would result in Hawaii having lower income, rather like the other Pacific island countries.
But as part of the U.S., Hawaii benefits from visa-free travel from more economically efficient areas; i.e. places where search and transport costs are lower so economic exchange and development happens more rapidly. Because Hawaii is beautiful and there are no legal barriers to movement, U.S. money spills over into Hawaii. But for this money to have any use, it will be spent on the same expensive goods brought from elsewhere! Prices must rise! Add in the role of Federal transfers, military personal, etc, and you get a situation where isolated places have that isolation priced into local costs as much or more than income levels.
Or consider the existence of a place with strict land use policy, like California. If you have a high-value business with high profitability, you can afford to pay the cost of California. But if you have a less high-value business, you can’t. We know that firm productivity is associated with individual wages, ergo we can see that higher land prices will push lower-wage-paying firms out of a region. So high local prices inflate local incomes by sorting out lower-income firms and people. The effect on national productivity is zero, however, as that lower-productivity-firm produces the same goods for the same prices that it produced in California. The only difference is its revenues become profits or salaries instead of land rents or interest.
Or consider a place like New York City or Washington, DC. A few exceptional sectors have extremely high productivity and concentration of their leaderships in those regions. They command high salaries (though no higher than they might command were they located elsewhere). They bid up rents; here high salaries drives prices up. But hold on: those rents accrue as income for landlords! To the extent that landlords reside locally, high rents re-appear as high incomes for real estate owners in the same region. When you measure incomes for the region, then, high housing prices actually create extra measured income.
Of course, this isn’t to deny the incomes-driving-prices story. Just as one reason California has high incomes is that it has high prices is true, so too one reason California has high prices is that it has high incomes. When productivity is high locally, the price of less productive sectors (especially nontraded sectors) will rise as well.
This all matters for a very simple reason: should we think of high prices in NYC or California as pure economic loss that, if we could manage it better with land use policies, could be extra disposable income for residents? To some extent, yes. Better land use policy would help reduce the degree to which high local incomes/productivity is translated into higher local prices. Some degree of transmission is inevitable, of course. You cannot have a place where productivity in one or two large sectors is anomolously high and not have it spill over as higher prices in less-anomolously-high sectors. But we could mitigate this to some degree.
But on the other hand, to the extent that high incomes are caused by high local prices, and there is some of this happening, better land use policy would not increase real incomes. Incomes would fall with prices. Real estate owners would lose income or asset value, and both directly and through wealth effects would consume less, resulting in less income for everyone. Lowering prices would reduce the degree to which transfers compensate for high prices; the canonical case of course here is that lower price levels in Hawaii would reduce the COLA received by the Federal workers who make up a large amount of the income base. Lower prices would also enable more lower-margin and lower-productivity firms to locate in the area, pulling incomes down further.
I don’t think we know right now how large each of these effects is. Maybe Prices → Income is just 1/10 the scale of Income → Prices, so not much worth considering. But maybe they’re equal.
The point is, when you look at cost of living maps, don’t jump to the conclusion that you could, by land use or some other policy, lower the cost of living in a region and keep on enjoying the same nominal income at a lower price level. It ain’t that easy.
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I’m a native of Wilmore, Kentucky, a graduate of Transylvania University, and also the George Washington University’s Elliott School. My real job is as an economist at USDA’s Foreign Agricultural Service, where I analyze and forecast cotton market conditions. 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.
