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Consumer spending on milk and cookies

Enjoy some comforting FRED expenditures data

The FRED Blog has looked at consumer comforts before: the seasonal increases in electricity use for cozy heating and cooling and the prices of homemade foods. Today we devote our post to, arguably, the most comforting childhood tradition: milk and cookies.

The FRED graph above shows consumer expenditures on milk and cream (in white) and on bakery products (in chocolate chip cookie brown). We’ve adjusted the nominal value of those dollar figures by their corresponding consumer price item index to compare them over time. As it happens, households spend, on average, about twice as much on baked goods as they do on milk and cream.

We’ll also be looking for suitable data alternatives for our readers who avoid gluten and lactose. For now, try dunking your favorite baked good in your favorite rich, savory beverage while reading the FRED Blog. We hope both experiences bring you similar levels of comfort.

How this graph was created: Search for and select “Expenditures: Fresh Milk and Cream: All Consumer Units.” From the “Edit Graph” panel, use the “Edit Line 1” tab to customize the data by searching for and selecting “Consumer Price Index for All Urban Consumers: Dairy and Related Products in U.S. City Average.” Next, create a custom formula to combine the series by typing in a/b*100 and clicking “Apply.” For the second line, repeat the same steps with the series “Expenditures: Bakery Products: All Consumer Units” and “Consumer Price Index for All Urban Consumers: Cereals and Bakery Products in U.S. City Average.” To change the line colors, use the choices in the “Format” tab.

Suggested by Diego Mendez-Carbajo.

From PPI to CPI

The consumer price index (CPI) measures the cost of a fixed bundle of consumer goods relative to the cost of those same goods in a chosen reference year. Inflation is the percent change in the index from one year to the next and reflects how prices are changing for consumers.

The producer price index (PPI) is a similar construct that measures the price that producers get for their wares. It was formerly called the wholesale price index (WPI). Because many of these goods are intermediate goods and thus inputs to the production of final consumer goods, one might hypothesize that changes in the PPI could forecast future changes in the CPI.

The FRED graph above shows recent movements in these two series (January 2015 to present). Both series have grown at a fairly constant rate over the medium term. Moreover, after an initial dip at the start of the COVID recession, the PPI has risen sharply. Does this mean that future CPI inflation is imminent?

While it’s certainty possible that changes in the PPI are passed through to the CPI, economists have found that the former generally does not forecast the latter (see Clark, 1995). What does the sharp increase in the PPI mean for consumer prices? Only time will tell.

How this graph was created: From the FRED main page, search for and select the data series “Consumer Price Index for All urban Consumers: All Items in U.S. City Average”. From the “Edit Graph” panel (orange button), use the “Add Line” tab to search for and select the data series “Producer Price Index by Commodity: All Commodities.” Using the sliding blue bar at the bottom of the graph (or the date entry boxes in the top right hand corner), adjust the timespan to your desired date range.

Suggested by Michael Owyang.

ALFRED at 15: Archiving FRED data since 2006

You know FRED, but do you know ALFRED? ALFRED is ArchivaL FRED, which is pretty much what it sounds like: an archive of historical versions (or vintages) of FRED data. ALFRED is turning 15 years old, which is a nice opportunity to describe why recording data history is important.

Economic data are often revised over time as more and/or more-accurate information becomes available. Accuracy is important, and that’s what FRED provides. But the original, less-accurate vintages of the observations are important, too, as they tell the story of what information was known at the time. That’s what ALFRED provides.

Despite being 15 years younger than FRED, ALFRED is an old soul with a great memory that contains all the historical vintages of the series in FRED. Each time a data series is updated in FRED, the prior version of the series is stored and made available in ALFRED, with all of its prior vintages of data observations at specific points in time. ALFRED also records the metadata, such as series title and units, which may also have been revised.

How about an example? The ALFRED graph above shows the change in total employment reported by the Bureau of Labor Statistics (BLS). For now, let’s focus only on the observations for January 2008, right in the middle of the graph.

  • The blue bars show the first observation for January 2008, released on February 1, 2008.
  • The red bars show the next vintage, released on March 7, 2008.
  • The green bars show the most-recent vintage available at the time of this post, released March 5, 2021.

In its initial release on Februrary 1, 2008, the BLS estimated a loss of 17,000 jobs (in blue). Over time, the BLS is able to gather more information about the number of people employed in the economy. So, their estimate was revised on March 7, 2008, to a loss of 22,000 jobs (in red). And with even more time to gather information, the BLS adjusted their estimate from a loss to a small gain of 11,000 jobs (in green) as of March 5, 2021.*

Now let’s fast forward to 2020 and another ALFRED graph for our second example. If we look at March 2020, we see that the change in number of employees was revised from -701,000 to -870,000 between the initial release (April 3, 2020) and the subsequent release (May 8, 2020). As of March 5, 2021, the change in number of employees had been revised again, to -1,683,000. Between April 2020 and March 2021, the BLS gathered information that led to a revision in the number of job losses that was almost twice what was initially estimated.

Data always tell a story, and it’s important to be aware that data change over time. ALFRED provides several advantages here. Not all the originating data sources or statistical agencies make prior vintages of data available on their website. Having access to real-time data allows researchers and analysts to model the economy at different points in time and evaluate policies using the data that were available when those policies were initially enacted. Real-time data also enable replication of prior research.

To learn more about ALFRED and its history, see the resources available in its help page.

*By the way, the monthly change in total employment as of January 2008 first became positive with the vintage released on February 4, 2011. Note also that the earlier vintages of some series have been pre-populated in ALFRED. However, the general case is that vintages start accumulating in ALFRED only after these data are initially added to FRED.

How these graphs were created: For the first graph, search ALFRED for “PAYEMS” and select the monthly, seasonally adjusted series. By default, ALFRED shows a graph with two sets of bars: the most recent vintage and the prior vintage. From the “Edit Graph” panel, use the Edit “Bar 1” option to select the vintage “2008-02-01” in the vintage selection dropdown menu or type the date in the “As-of date” box. From “Edit Bars,” use the “Edit Bar 2” option to select the vintage “2008-03-07.” With the “Add Bar” tab, search for “PAYEMS” in the search box and again select the monthly, seasonally adjusted series (units in Thousands of Persons). Click on the “Add data series” button. Under “Edit Bar 3,” select the vintage “2021-03-05.” Then change the units to “Change, Thousands of Persons.” Click on the “Copy to all” button to apply the unit change to all the series on the graph. Finally, adjust the start date of the graph to 2007-10-01 and the end date to 2008-04-01. For the second graph, use the “Edit Graph” panel’s “Edit Bars” tab to edit Bar 1 and Bar 2 by selecting the “2020-04-03” and “2020-05-11” vintages as before. Then, set the graph’s start date to 2019-12-28 and the end date to 2020-07-01.

Suggested by Maria Arias.



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