ST. LOUIS – Federal Reserve Bank of St. Louis President James Bullard discussed “Living Standards across U.S. Metropolitan Statistical Areas” at Bi-State Development’s 2017 annual meeting on Friday.
In comparing living standards across metropolitan statistical areas (MSAs), he drew on recent research at the St. Louis Fed as well as additional research from outside the St. Louis Fed. He said that the motivation for his talk is the literature on international standards of living, which tries to determine which countries are performing relatively well and which are performing less well for their citizens, as well as what makes some countries relatively well-off in terms of material well-being and what makes others relatively poor.
He explained that this literature typically uses real per capita income (i.e., total real income produced in the economy divided by the population) as the measure of “standard of living.” Based on this measure, he noted that the U.S. standard of living is relatively high, while the standard of living in many other countries is lower. He also pointed out that countries with a high standard of living are not necessarily the fastest-growing countries.
For this talk, Bullard applied this international framework to MSAs in the U.S. However, he noted that prices can vary greatly across the country, in part due to differences in housing costs. “Adjusting for price differences across MSAs is essential for generating meaningful comparisons of living standards across MSAs,” he said.
“We all have an intuitive sense that some places are expensive locations to live and other places are inexpensive,” Bullard said. “Recently, more systematic data have been developed that account for these differences in prices across the country.”
Bullard explained that these regional price level data—called Regional Price Parities (RPPs)—measure the differences across MSAs for a given year. RPPs, which are published by the Bureau of Economic Analysis, are expressed as a percentage of the national price level.
Among the 381 MSAs that Bullard examined, 59 MSAs were more expensive than the nation at large (meaning their RPPs were larger than 100 percent) in 2015. The median and average (population weighted) RPPs were 93 percent and 101.7 percent, respectively. St. Louis had an RPP of 90.6 percent.
Next, Bullard discussed adjusting real income per capita for each MSA based on the regional level of prices for that MSA. “This type of calculation gives the standard of living as the average level of real income per person for a particular location, analogous to the cross-country literature,” he said.
Specifically, he used per capita personal income in 2009 chained dollars for each MSA, divided by the MSA’s RPP. Based on this measure, he noted that in 2015 St. Louis ranked No. 20 out of the 381 MSAs, which is within the top 6 percent. “Said differently, 94 percent of all MSAs have a lower standard of living than St. Louis,” he said.
He also examined the 53 MSAs with a population of at least 1 million, which he referred to as the large MSAs. St. Louis ranked No. 7 among this group in 2015.
Of the top 10 large MSAs in terms of RPP-adjusted real per capita personal income, Bullard pointed out that some have a high cost of living while others have a low cost of living. “Just two cities in the top 10, St. Louis and Nashville, have a cost of living less than the national average,” he said.
Bullard noted that when considering living standards across MSAs, there is also the question of income inequality within an MSA. Although the per capita concept he used doesn’t account for income distribution within an MSA, he drew on other research that has studied income inequality across MSAs. According to this research, he said, the St. Louis MSA’s income inequality is near the average, but some other top 10 MSAs in real per capita income have higher-than-average income inequality.1
Bullard concluded by reiterating the importance of adjusting for price differences in comparing living standards across MSAs. In addition, he said, “The facts uncovered through this analysis may provide the basis for future research on why some cities are more successful than others.”
1 Inequality is measured by the ratio of the average household income of the top 1 percent to the average household income of the bottom 99 percent.