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The Great Recession’s regional effects

How have different industrial compositions affected unemployment for Census regions?

Every corner of the U.S. was hard-hit by the Great Recession, but the varying makeup of local economies resulted in different effects on individual cities, states, and regions. Areas that are reliant on economic necessity—say, healthcare and waste management—may fare better during economic downturns than areas that are reliant on economic prosperity—say, tourism and construction. In 2008, the recession particularly disrupted the construction industry. According to the Bureau of Labor Statistics, the percentage of jobs lost in that industry, 19.8%, exceeded those of all other nonfarm industry supersectors.

FRED can help us see the recession’s effects on the four U.S. Census regions: Midwest, Northeast, West, and South. The annual percent change in unemployment varied most in 2008, where unemployment increased nearly 40% in the West but increased only 19.6% in the Midwest. The map below shows that some of this variation may have been the result of job losses in construction. Western states experienced declines of over 8% in the number of construction employees from 2007 to 2008. The rest of the U.S. fared better, with some Midwestern and Southern states even seeing increases in the number of construction employees.

The relatively small increase in unemployment in the Midwest may be a result of its more agriculturally based economy, which wasn’t initially hit as drastically as the shock-prone industries of tourism and construction, which are more prominent in the West. The years 2009 and 2010 saw less-significant differences in unemployment changes among regions. But data from 2011 show the continued comparative resilience of the Midwest, where the decrease in unemployment was over 11%, compared with the other regions’ 4% to 6% range. This variation in rates of recovery may also be attributable to industrial makeup: Research from the St. Louis Fed has analyzed the construction industry’s contribution to the slow overall recovery and compared it with the manufacturing industry. Research from the Minneapolis Fed notes that the construction industry took longer to recover from the recession than other industries in part because of workers’ reluctance to return to construction jobs.

How this graph was created: In FRED, search “unemployment rate census region.” Check the boxes next to the seasonally adjusted series for each of the four regions and click “Add to Graph.” Next, click “Edit Graph” and change the units to “Percent Change.” Click “Copy to all.” Modify the frequency of each line to “Annual.” Next, click the “Format” tab and change the graph type to “Bar.” Finally, adjust the dates on the graph to show from 2007-01-01 to 2011-01-01.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Maria Hyrc.

View on FRED, series used in this post: CMWRUR, CNERUR, CSOUUR, CWSTUR

Cost of living and per capita incomes in U.S. cities

A recent post introduced regional price parities (RPPs) and their applications at the state level. These RPP data are also available for metropolitan statistical areas (MSAs), which include a principal city and its surrounding area. So we can conduct a similar analysis of price levels and adjusted incomes at the metro level. Data are indexed such that the population-weighted national average is equal to 100. Of the 349 MSAs in the data, 94 fall within 5% of the national average for cost of living.

Consistent with the state-level data, the most expensive cities are heavily concentrated in the Northeast and on the West Coast. Of the 34 MSAs with a cost of living more than 5% higher than the national average, 32 are in either of those two regions. Honolulu, Hawaii (not pictured above), is the priciest MSA, with a cost of living over 24% above the national average. Inland cities are substantially cheaper, especially in the Midwest and the South. In Rome, Georgia, the least costly MSA, the cost of living is about 20 percent lower than the national average.

One implication of these regional cost of living differences is that a dollar in one city isn’t necessarily the same as a dollar in another: Average per capita personal income nationwide is about $43,996. In terms of purchasing power, the equivalent income in St. Louis, Missouri, is below $40,000 due to the relatively low cost of living. Meanwhile, in comparatively expensive New York, New York, the equivalent income is almost $54,000. In other words, as the cost of living goes up, it takes more dollars to buy the same basket of goods and services. Hence, using the RPPs to adjust per capita personal income for cost of living yields a more accurate measure of how much the average person in a given city can consume.

As seen on the next map, real (cost-of-living-adjusted) per capita personal income is more broadly dispersed geographically than the RPPs: 40 MSAs across 32 different states have a real per capita personal income more than 10% greater than the national average. Midland, Texas, has the highest income, at almost $96,000 (adjusted dollars) per person.

Given that we derive real per capita personal income by dividing nominal income by cost of living, one might expect that the most costly cities have the lowest adjusted incomes. While that is sometimes true, it’s not always the case. For example, San Jose and San Francisco, California, and Bridgeport, Connecticut, are each in the top ten for both most expensive cities and highest-earning cities. Similarly, McAllen, Texas, has the lowest real per capita personal income of any MSA, about $27,000, despite being one of the 20 cheapest cities in the country.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Andrew Spewak and Charles Gascon.

Healthy inflation?

Inflation in the healthcare industry vs. general CPI

Some components of the consumer price index have consistently, over several decades, risen faster than the rest. This blog recently discussed education as one such component. The components of the CPI devoted to medical care have also seen faster price increases than the rest of the basket. Going back as far as the series are available, since 1948, the price of medical care has grown at an average annual rate of 5.3% while the entire basket, headline CPI, has grown at an average annual rate of 3.5%. In the past 20 years, in the regime of stable inflation, headline CPI has grown at an average annual rate of 2.2%, whereas the price level of medical care has grown at an average annual rate of 3.6%—about 70% faster.

The graph above shows the two time series. Besides the difference in their levels, it’s also notable how much less cyclical medical care inflation is. Although overall CPI inflation dips during recessions, medical care inflation stays steady.

The implication of these two features is far reaching: It’s symptomatic of the increasing share of income the U.S. spends on medical care. Beyond macro trends, the features of these two series themselves have policy implications. Indeed, indexing government healthcare budgets to overall CPI rather than medical care prices has implications for spending in real terms. This gap could also widen during recessions, when government help may be most in demand.

The CPI is intended to measure the price of goods consumers purchase directly, and therefore the medical care subset is actually measuring only the prices of out-of-pocket expenses. For healthcare, however, there’s a great deal of other spending going on. And the inflation rate of that spending is something a policymaker might need to know. Luckily, the BEA puts together a more holistic price index for healthcare spending—the health expenditures price index—which we add in the graph below. Although the history of this series is shorter, this measure of healthcare prices is still rising considerably faster than headline CPI: In 2001-2013, this measure of healthcare inflation rose almost 4% per year, whereas headline CPI rose 2.3% in this period and the other healthcare CPI rose 3.9%.

How these graphs were created: For the first graph, search for “Consumer Price Index Medical.” In the “Edit Graph” tab, convert the units to “Percent Change from a Year Ago.” Then use the “Add Line” feature to search for “Consumer Price Index All Items.” Add this line and again check that its units are the same. (FRED does this automatically, but it doesn’t hurt to check.) These series are both also available as chained indices, but for a shorter period. For the second graph, add to the first another line by searching for “Health Expenditures Price Blended Account.” Then restrict the period to show the entirety of the new line.

Suggested by David Wiczer.

View on FRED, series used in this post: CPIAUCSL, CPIMEDSL, HLTHSCPIBLEND

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