Every State Has Its Price

A Look at BEA’s Regional Price Parities

BEA recently released the 2014 data for their Regional Price Parity series. This data series tracks cost of living for “parity” goods and services across states and metro areas. Much of its conclusions are driven by housing, but not all.

Prices vary widely across the US. But here, let me just share the map the Tax Foundation made, because they do a good job:

As you can see, there are big price differences around the country, from expensive New York and California, to much less costly Arkansas and Mississippi. These cost differences partially offset often-observed income differences between some of these states, such that real income varies less between states than nominal income, as much of the higher income earned in richer states and cities is just poured into paying higher prices. Ultimately, the only people who reliably enjoy higher real incomes in the richer states are people who own lots of rental property.

But the Tax Foundation covers that stuff pretty well. I want to look at something else.

The BEA publishes this data annually because, no surprise here, it changes. Because there’s inflation, prices change, and they don’t change at the same rate everywhere. Now, there’s a fun argument that can bubble up from the wells of economic history about the geographic distribution of price changes due to monetary supply shocks *coughCantillonEffectscough*, I really just want to illustrate how prices have changed at different rates in different states.

So, let’s illustrate!

The above map shows in darker colors states with a larger increase in cost of living, and in lighter states with a smaller increase. Of course, the fastest increase of all wasn’t in any state, but in the District of Columbia, too small to see here. DC’s price deflator rose 13.3 points. It went from being the second highest-priced area in 2008 (behind only Hawaii), to being the highest priced place as of 2014. Congrats, DC, you’re not just expensive, but you’re getting moreso, and fast! Prices also rose by a large amount in New Jersey, North Dakota, New York, Colorado, Maryland, West Virginia, Vermont, and Washington.

Meanwhile, the slowest price increase was in Arizona, followed by Nevada. Both had middling-level prices in 2008, and now have just a bit-below-middling prices. Major declines in housing prices are probably at work here, as Nevada and Arizona have both seen fairly fast price rises in the last 2 years. Other lower-price-increase states were Idaho, Georgia, Michigan, Ohio, Montana, Florida, Rhode Island, South Carolina, and New Hampshire.

But it should be noted, every state saw rising prices over the period. Arizona rose from a deflator index of 100.4 to 105. DC rose from 115.4 to 128.7. The cheapest state in the union, Mississippi, rose from 86.2 to 94.5. There have been no inflation-proof states.

The headline IRPD doesn’t let us break out subcomponents. But if we look at just the regional price parity core data, we can, so let’s do that. Let’s see what’s driving these trends.

First of all, we need to look at the headline regional price parity. The RPP differs from the IRPD because, each year, the RPPs are calculated fresh, showing each state’s price level as a ratio of the national average price level. So for example, West Virginia’s RPP in 2008 was 87, meaning its cost of living was about 87% of the national average. In 2014, its RPP was 88.9, meaning it had risen to about 88.9% of the national average; i.e. West Virginia got more expensive compared to the rest of the nation.

States colored in red saw their price relative to the national average rise, while states colored in blue saw their relative prices fall. In other words, even though there was inflation around the country, meaning that prices were higher everywhere, prices were a lot higher in, say, North Dakota, but only a little higher in Nevada, compared to the national average.

RPPs are pretty stable. As you can see from the scale, most states had less than 1 percentage point change in relation to the national average. By and large, local area price levels are not exceptionally volatile, but reflect enduring differences in local economies. That said, which states are cheap and which are not can and probably will change over time.

But what drives that change? Well, unlike the IRPD, the RPP is broken out by some categories.

As you can see at left, the map of price differences for goods is pretty similar to price differences overall. By the way, “goods” does not include “housing.” We’re talking about gas, food, cars, baseball bats, guns, stationery paper, bug spray, polyurethane sheets, coloring books, hacksaws, your basic run-of-the-mill consumption.

So if someone says to you, “Those prices differences are just housing!” aha, no. No they’re not. Housing costs, as it happen, do vary more widely than other costs, but they vary in correlation with other costs.

What’s interesting to me, however, is that second map. Basically all of the southern states saw relative weakness in goods prices, while northern states, and especially the plains states, saw relative increases in goods prices. I don’t know goods in the south got cheaper relative to goods in the upper midwest or the northeast, but that does seem to be what happened from 2008 to 2014.

Above we see the same charts, but for services other than housing. Again, the northeast and California are costly, the rest of the country, not so much.

But the pattern of change is not the same. This time, change in the southern states looks mild and without a strong pattern, while we have some kind of bleeding swathe of price-hikes for service-providers from Wisconsin to Nevada. I don’t know why doctors and repairmen and lifestyle coaches and yoga instructors and concert pianists got so much more expensive in this part of the US than the rest of it from 2008–2014 but, hey, it is what it is. Meanwhile, seems like services got relatively less expensive in New England and the upper western states.

Now again, recall we’re talking relative change. It’s still costlier to buy the same service in New York than in Colorado. But that gap closed a bit between 2008 and 2014.

Finally, we come to the main event: rents.

I’ve tweaked the colors a bit to emphasize that we’re looking at a different game here. Note the scales. For rents, state RPPS range from about 62% of the national average to 162%, while the change in those RPPs ranges from a loss of 24% to a gain of 22%. Rents have a level of variation and volatility that does really overwhelm the other factors.

Now, keep in mind that, overall, high rental costs tend to track high goods costs and high service costs. It’s not like if we ignored rents that the map would look totally different. It just might not look quite so extreme. Once again, for rents, the northeast and California are costly. Notably though, DC takes the cake for costliest place, at 162.5% of the national average. It passed up Hawaii and California during the last 6 years, as both had formerly been costlier than the capital.

On the cheaper side, Alabama, Arkansas, Mississippi, and West Virginia are all sitting around 62–65% of the national average for rental costs.

Now to be clear, these are quality-controlled rents, as best as can be done. There are basic controls for the type of house here, so it’s not just that the WV housing supply is worse than New York’s or something.

But the biggest relative declines in rental costs have been in states hard-hit by the recession: Florida, Nevada, Arizona, and, er, wait a second, California?

Uhhh… okay… so… California’s rents since 2008 have gotten cheaper compared to the national average. Now to be clear, they haven’t actually seen rents fall, just California’s housing costs have not risen as fast as housing costs nationally since 2008. That one really surprised me.

Economists often talk about “convergence,” whereby connected economies gradually see their wages and prices equalize. Many US economists have suggested that convergence in incomes has ended in the US, while indeed a new divergence may have begun. Maybe. I won’t litigate that issue here.

But in the RPP data since 2008 we can see something interesting. The above chart takes the standard deviation for state goods, rents, and services RPPs, and indexes them at 100 for 2008. Goods and rents shuffle around a bit, but are basically stable: the amount of variation and regional difference in housing and goods costs is about the same in 2014 as in 2008, at least at the state level of aggregation.

But that is not true for other services. Service costs have equalized substantially across states. I don’t know if cheap areas got more expensive or if costly areas got cheaper or what, but something made the variation in service costs drop by about 10–12% between 2009 and 2012.

“But Lyman, that’s just states. Really, you should look at metro areas. The fact that you didn’t shows this problem that also happens to be my pet issue.”

Here’s the chart for metro areas:

Quoth Admiral Akbar about rascally economists who do the MSA level work then don’t show it to you until the end of the blog post: “It’s a trap!”

Lookee there! Metro area convergence is even more impressive than state-level convergence! And goods actually converged the most! Rents do seem to have converged the least, and show some rising divergence right now, but we still have less variation in MSA-level-rents than we had in 2008.

So next time you want to talk about “a few exceptional cities,” just remember: as far as prices are concerned, there’s less variation now than 6 years ago.

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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.