For an expanded version with figures and a table, see the On the Economy blog post.
About 90 percent of U.S. gross domestic product (GDP) is produced in metropolitan statistical areas (MSAs). Furthermore, about 86 percent of the U.S. population lived in the 381 MSAs in 2015,1 and about 56 percent of the population lived in the 53 largest MSAs (those with at least 1 million people).
I am keenly interested in how incomes and prices differ throughout the U.S. To gain a better understanding of how the individual MSAs are performing, I recently examined which ones have relatively high and low standards of living, based on a measure of per capita income.2 From a macroeconomic point of view, we would want all MSAs to be performing at a high level so that overall GDP and standard of living can be as high as possible.
The approach I used to compare MSAs is similar to the methodology used to compare standards of living across countries. I focused on 2015, the most recent year for which we have complete data.
Comparisons across MSAs are usually based on real per capita variables that are adjusted by a nationwide price index. However, the usual comparisons can be misleading because they do not factor in the large differences in cost of living across the country. Differences in housing costs, in particular, can be substantial.
Therefore, adjusting for price differences across MSAs is essential for generating meaningful comparisons of living standards. My colleagues at the St. Louis Fed have done just that.3 My analysis draws on their research.
The Bureau of Economic Analysis recently released data that measure the differences in price levels across MSAs for a given year. These Regional Price Parities (RPPs) are expressed as a percentage of the national price level. For 2015, these ranged from 79.7 percent for Beckley, W.Va., to 124.5 percent for Honolulu (with the national level being 100 percent). The most expensive MSAs tend to be relatively larger and located on either coast, and the least expensive MSAs tend to be relatively smaller and located in the interior.
To compare MSAs, the measure of income that I used is real (i.e., inflation-adjusted) per capita personal income, which I adjusted by the appropriate RPP. The results suggest that some MSAs have a much higher standard of living while others have a much lower standard of living than the nation as a whole. St. Louis, for instance, did extremely well. Its RPP-adjusted real per capita personal income was about 13 percent higher than the national average.
Among all MSAs, St. Louis ranked No. 20, putting it in the top 6 percent. Said another way, St. Louis’s standard of living was higher than about 94 percent of MSAs in the country.
It is also helpful to look at living standards across the 53 largest MSAs. The top 10 large MSAs with the highest standard of living include three on the West Coast (San Jose, San Francisco and Seattle), three on the East Coast (Boston, Hartford and Washington) and four in the middle of the country (St. Louis, Nashville, Minneapolis and Houston). St. Louis ranked No. 7 in this group.
Some MSAs among the top 10 had a high cost of living and others had a low cost of living. Only St. Louis and Nashville had a lower cost of living than the national average. This suggests that these two MSAs have a cost advantage over their other competitors in the top 10. In some cases, the cost differential was 30 percentage points or more.
While this analysis gives an idea of how MSAs are performing on average, the per capita concept does not account for the income distribution within an MSA. For this, I used data from other research.4 The main finding is that income inequality tended to be higher in larger MSAs. In addition, among the top 10 large MSAs in terms of living standards, some had very high income inequality (such as San Jose, San Francisco and Boston). Others had income inequality that was closer to the average (such as St. Louis, Nashville and Minneapolis).
The bottom line is that, among the top 10 large MSAs, St. Louis and Nashville were the only ones that could simultaneously claim a higher-than-average standard of living, a lower-than-average cost of living and moderate income inequality.
The results of this analysis demonstrate the importance of adjusting for price differences across regions when comparing living standards. The facts uncovered here may provide the basis for future research on why some MSAs are more successful than others.