Federal Reserve Economic Data

The FRED Blog has tapped into US Bureau of Economic Analysis data before to discuss the small regional differences in the price of goods and the much larger regional differences in the price of housing. Today, we tap into US Bureau of Labor Statistics data to compare differences in medical care consumer prices across urban areas.

The FRED graph above shows the annual inflation rate, calculated as the percent growth rate from the previous year, in medical care service prices recorded in eight core-based statistical areas (CBSAs). These geographies are urban clusters with high degrees of social and economic integration.*

Medical care prices vary quite a bit from year to year and across regions. Consider, for example, the year 2018 and the urban areas of Denver-Aurora-Lakewood, CO, and Tampa-St. Petersburg-Clearwater, FL. The former recorded 6% annual inflation and the latter 4% annual deflation.

Does this price variability indicate there are different constraints to the demand and supply of medical care across regions? Maybe. Perhaps medical services aren’t highly mobile and local changes to the quantity and use of those services result in large price changes. On the other hand, research by James Choy at the BEA reports estimates of regional price levels for health-related goods and services that are stable across years and that vary less across regions than existing estimates obtained using CPI data. Answering this question more precisely requires more research.

* To look for data on other CBSAs, navigate FRED to Consumer Price Index by Expenditure Category > CPI for Metropolitan Areas and search the alphabetical list of geographies.

How this graph was created: Search FRED for and select “Consumer Price Index for All Urban Consumers: Medical care in Boston-Cambridge-Newton, MA-NH (CBSA).” Click “Edit Graph” and select the “Add Line” tab to search for “Consumer Price Index for All Urban Consumers: Medical care in Dallas-Fort Worth-Arlington, TX.” Don’t forget to click “Add data series.” Repeat this step to add the other six data series shown in the graph. Use the “Edit Lines” tab to select any of the lines shown in the graph. Use the “Units” dropdown menu to select “Percent Change from Year Ago” and click on “Copy to all.” Last, use the “Format” tab to change the “Graph type” to “Bar.”

Suggested by Diego Mendez-Carbajo.

FRED includes consumer price index data from the US Bureau of Labor Statistics. FRED data tools make it easy to use these price data to calculate the dollar value of figures measured at constant prices—a process known as adjusting for inflation.

The FRED Blog frequently adjusts for inflation when describing prices, whether it’s gasoline prices, stock prices, or foreign exchange (just to name a few data series).

The FRED graph above shows two versions of monthly advance retail sales of retail trade and food services, reported by the US Census. The blue line shows the dollar figures unadjusted for inflation, and the red line shows the dollar figures adjusted for inflation with the aforementioned consumer price index (CPI). The CPI currently uses the years 1982-1984 as the reference period, so you can think of the data shown by the red line as retail sale figures measured in 1982-1984 prices.

FRED makes it easy to customize the reference period to adjust dollar figures for inflation. So, read on!

The second FRED graph above shows the same advance retail sales series as the first graph, but adds two alternatives using customized reference periods: the start of the 2001 recession (the dashed-dotted red line) and the start of the 2020 recession (the dashed red line). The steps for creating that graph are listed below.

A benefit of customizing the reference period, by choosing a more-recent date, is to facilitate the visual comparison between the inflation-adjusted and unadjusted data. And, given that the choice of reference period does not impact the calculation of the rate of growth of inflation-adjusted data, you can be confident using those units to tell the real story behind the numbers.

How the graphs were created: First graph: Search FRED for and select “Advance Real Retail and Food Services Sales.” Use the “Edit Graph” panel to select the “Add Line” tab and search for and add “Advance Retail Sales: Retail Trade and Food Services.” Second graph: Add another line for “Advance Retail Sales: Retail Trade and Food Services” and customize the data by searching for “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” Don’t forget to click “Add.” Next, type the formula (a/b)*100 and click “Apply.” Edit the “(b)” series in Line 2 by changing the units to “Index (Scale value to 100 for chosen date)” and selecting a date of your choice. Finally, repeat these last steps with a new line, searching for “Advance Retail Sales: Retail Trade and Food Services” and selecting a different date. You  can also use the “Format” tab to play with the color and style of the lines.

Suggested by Diego Mendez-Carbajo.

As part of its overall accounting of expenditures, the Bureau of Economic Analysis (BEA) collects data specifically on government spending, which can be broken down further into finer categories. FRED has time series of these data to help users explore the evolution of government spending. That evolution can be hard to interpret, so today we take a closer look at one of these series.

The FRED graph above tracks real federal energy expenditures from 1960 to 2022 using an index, which provides annual values relative to the level in 1976. Most of these expenditures come from activities by the Department of Energy (DOE).

During the 1960s and early 1970s, energy expenditures were much lower, ranging from around 10% to 40% of their 1976 level. Starting around 1974, energy expenditures climbed sharply, which coincided with the 1973 oil crisis and subsequent founding of the DOE in 1977.

Since the 1980s, energy expenditures have roughly kept pace with inflation but have varied widely year to year: Expenditures peaked at around twice their 1976 level in the early 1980s and again in 2010. By 2021,  real energy expenditures were back to about the same level as they were in 1976.

In 2022, energy expenditures actually become negative for the first time since reporting began in 1959. The FRED graph below zooms in on that time period and shows the year-over-year percent change in real energy expenditures since 2012. In a typical year, energy expenditures change by as much as 20%. But in 2022, energy expenditures declined by 115%. Why?

Just like the jump in spending during the 1970s, the drop in 2022 can be explained by policy responses related to events in global oil and gas markets.

The US stores a constant back-up supply of crude oil called the Strategic Petroleum Reserve (SPR). Within a typical year, very little of this store is released, but the US president can authorize drawdowns from the SPR during emergencies or shortages. For example, then-President Obama authorized a large release of oil from the SPR in response to the war in Libya in 2011. After Russia’s 2022 invasion of Ukraine disrupted oil markets, President Biden authorized a large “emergency drawdown” of the SPR in coordination with the International Energy Agency’s International Energy Program.

Over the course of 2022, this drawdown added around 180 million barrels. For context, the last two emergency drawdowns in 2011 and 2005 were around 30 and 20 million barrels, respectively. (For more discussion of petroleum reserves, check this May 2024 FRED Blog post.)

When the government releases oil from the SPR, it does so through competitive sales, which are accounted for in current expenditures as net outlays and are negative values. In 2022, SPR-related outlays were large enough to dwarf other energy-related spending, resulting in negative total expenditures.

How these graphs were created: First graph: Search FRED for series “G160551A027NBEA.” Using the date selector above the graph, set the date range to 1960-01-01 to 2022-01-01. From the “Edit Graph” panel under the “Edit Line 1” tab, use the “Customize Data” section to search for and select the series “CPIAUCSL” to add to the graph. Apply the formula a/b. Under “units” at the bottom, convert your new combined series to an index and set 1976-01-01 as the custom date. Second graph: Switch the final units of the first graph from an index to percent change from a year ago. Use the date selector to change the date range to 2012-01-01 to 2022-01-01.

Suggested by Bill Dupor and Marie Hogan.