Federal Reserve Economic Data

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How food and fuel prices fluctuate

Detailed prices from the CPI

The consumer price index (CPI) follows the price of a basket of goods. The goods in the basket are determined by the purchases of an “average” U.S. household. Each item is tracked at multiple locations and for numerous varieties. The data are then aggregated to form the CPI.

The CPI has been a part of FRED for quite some time (since the early days if not the very beginning). FRED also offers some finer slices of consumer price data. The graph includes three examples: unleaded gasoline, peppers, and tomatoes. These are still aggregates, as the tracked prices come from many locations and, for tomatoes at least, across the various brands, varieties, and other ways of differentiating products.

What immediately gets our attention is how dynamic these lines are. The prices for these items change a lot and with little notice, which is why monetary policymakers in general prefer to look at price indices that exclude food and energy: Volatility can hide the bigger picture of inflation.

To reveal the extent of this volatility, we constructed the graph below, which compares the general CPI and the CPI without food and energy. For the latter, we even included the series without seasonal adjustment to demonstrate that seasonal adjustment does not remove the noise that policymakers are worried about.

How these graphs were created: For the first graph, start from the Average Price Data release table, check the items you want displayed, and click “Add to Graph.” For the second graph, start from the CPI graph and go to the “Edit Graph” panel. From there, open the “Add Line” tab and search for “CPI less food and energy”; add the monthly seasonally adjusted series. Repeat for the not seasonally adjusted series. Finally, adjust the units to “Percent Change from Year Ago” and click “Copy to All.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: APU0000712311, APU0000712406, APU000074714, CPIAUCSL, CPILFENS, CPILFESL

What’s the story behind who’s working?

Disaggregating EPOP by race and gender

Back in 2016, a FRED Blog post discussed the volatility of the labor market for people of different races based on the employment-to-population ratio (EPOP). The Bureau of Labor Statistics defines this measure as “the number of employed people as a percentage of the civilian noninstitutional population.” So EPOP is basically the percentage of adults who are employed.

The EPOPs for Hispanic and Black Americans have increased at roughly the same rates since the Great Recession of 2007-09, while the growth rate for White Americans has leveled off. This change occurred in spite of the decrease in employment from the Great Recession, which hit Hispanics and Blacks harder than Whites, judging by the steepness and level of decreases between 2008 and 2010. In fact, the EPOP for Hispanics has again risen above the ratio for Whites, which first happened in January 2000.

The next graph shows EPOPs according to both race and gender: It appears that the gap between Black and White overall is mostly due to the gap between Black men and White men. The EPOP for Black women has been higher than the EPOP for White women since the fourth quarter of 2014. A recent working paper from the Levy Institute at Bard College indicates that the changes in EPOP are due to increases in labor force participation for Blacks and the aging/retiring of White Baby Boomers.

The EPOP is by no means a comprehensive measure of well-being or fairness in the labor market. For example, St. Louis Fed Review articles discuss the significant gaps in wages and homeownership rates between Black and White Americans, and a stratification economics approach explores the enduring racial wealth gap. And there’s also no EPOP data for smaller racial and ethnic groups, such as Asians and Native Americans. But the EPOP does present interesting trend data about employment and demographic changes that can be useful for research.

How this graph was created: From the employment situation release table, select the series you want according to race and gender and click “Add to Graph.” For the second graph, the women’s employment-population ratio line is a different shade of the color for the men’s employment-to-population ratio line. This can be adjusted with the “Edit Graph” panel’s “Format” tools. The data range selected is 1972-01-01 to present.

Suggested by Darren Chang and Christian Zimmermann.

View on FRED, series used in this post: LNS12300003, LNS12300006, LNS12300009, LNS12300028, LNS12300029, LNS12300032, LNU02300031

Sugar spikes

Fluctuations in the price of sugar

We watch oil prices fluctuate all the time. Of course, oil gets a lot of attention because it has visible and sometimes significant consequences for the rest of the economy. Other commodities may not enjoy the same status, but they often suffer the same fate of volatile prices. The FRED graph above tells the recent story of sugar. It’s remarkable that the price of a commodity produced and used across the globe can almost double for a while and then return to its original level. In fact, as the graph below shows for an earlier period, this volatility can be even more extreme.

What does it take to generate price spikes like these?

  • Supply issues, such as a world war
  • Poor harvests in the major producing regions
  • Political issues (For example, Cuba is a major producer of sugar cane.)
  • New uses, such as ethanol produced from sugar
  • Attempts at manipulating markets

In a way, all these factors combined to create the extraordinary sugar spike in 1920: World War I essentially shut down the sugar beat harvests in France, the U.S. Congress considered buying the entire Cuban harvest of sugar, and a speculative frenzy ensued.

Read this article for more about the history of sugar.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: M0430AUS000NYM267NNBR, PSUGAUSAUSDQ


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