Federal Reserve Economic Data

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Gaslighting gas prices

What's behind the recent surge of prices at the pump?

The Russian invasion of Ukraine has amplified concerns about the price of gasoline. And for good reason: In March, prices at the pump surged over $4 per gallon for the first time since July 2008. This increase is more than 50% above prices in March 2021, which were about $2.80 per gallon. Gas at $4 per gallon sounds scary, but are real gas prices really that high?

By real prices, we mean prices that take overall inflation into account. To investigate, we compare nominal gas prices (the price you pay at the pump) to real gas prices, which we compute by dividing the nominal price by the consumer price index (CPI) and multiplying by 127.5, the value of the CPI in January 1990. By doing this, we normalize the gas price to the value of the dollar in January 1990, allowing us to compare gas prices across time and account for overall inflation.

The FRED graph above shows the result: Real gas prices have indeed increased in the past few months; however, they have only recently reached their pre-pandemic level. While nominal gas prices have increased rapidly over the past few months, real gas prices were still lower than they were for most of the 2006-2014 period. In fact, today’s real prices are within one standard deviation of the mean over the past 15 years.

These normalized, real gas prices have remained relatively flat over the past 5 years, which tells us that the high nominal prices are substantially driven by high inflation. Now we understand that an essential driver of the long-run increase in the price at the pump is inflationary pressures.

How this graph was created: Search FRED for “gas price,” then from its graph click on “edit graph”, under “customize data,” and apply formula (127.5*a)/(b). Then open the “add line” tab and search for the gas price again.

Suggested by Julian Kozlowski and Sam Jordan-Wood.

Comprehensive updates to real GDP

Periodic improvements to economic statistics

The FRED Blog sometimes taps into ALFRED, the archive of historical versions (or vintages) of FRED data. Today’s post does just that to discuss how the Bureau of Economic Analysis (BEA) periodically updates its quarterly and annual gross domestic product (GDP) figures to produce more accurate and complete figures of overall economic activity.

The ALFRED graph above shows seven different vintages of real GDP for the second quarter* and third quarter of 1991. The vintage dates included in the series names are the dates when the BEA released a comprehensive update to the data series. While a data revision incorporates newly arrived source data to paint a more complete picture of current or very recent economic conditions, a comprehensive data update reflects changes in economic and statistical standards applied to the whole economic record.

Comprehensive updates typically take place every five years, give or take a year or two, depending on business cycle conditions. On those dates, the BEA also updates its GDP statistics by moving forward the reference year for its inflation adjustment and price measures. The legend on the left axis of the ALFRED graph lists the reference year for the seven comprehensive updates to the real GDP series available in our database: 1987, 1992, 1996, 2000, 2005, 2009, and 2012. As the general price level rises over time, each successive bar is taller than the previous one. However, absent substantial changes to the types of economic activity being measured, the compounded annual rates of growth of real GDP record very small differences (see example here).

At the time of this writing, the latest comprehensive update was released in July 2018. Although the date for the next hasn’t been announced yet, it could be completed as early as 2023. Whenever the BEA releases those updated data, FRED will make them available through its website and ALFRED will add another vintage to its series repository.

* Why did we include the second quarter of 1991 in the graph? FRED went live online on April 18, 1991, the second quarter of that year.

How this graph was created: Search ALFRED for “Real Gross Domestic Product.” By default, ALFRED shows a graph with two sets of bars: the most recent vintage and the prior vintage. Add additional vintages by using the “Add Line” tab and select the date of the desired vintage from the “or select a vintage” dropdown menu. Change the start date and the end date above the graph to customize the number of data points shown.

Suggested by Diego Mendez-Carbajo.

How important are fuel excise taxes?

As gas prices have soared, various polities have proposed the reduction or temporary elimination of excise taxes on fuel, to provide relief to households. There are various issues attached to this proposal, and we touch on a few in this post.

How much relief would this provide? To answer this question, we propose the FRED graph above, where we express the fuel excise taxes at the state and federal levels and express them as a percentage of disposable income. (That is, the income that household have for expenses after paying taxes.)

The first thing we see is that this percentage has been declining almost constantly, more than halving in about three decades of data. The second thing is that, as of 2020, about a quarter of a percent of disposable income is dedicated to fuel excise taxes. Is that a lot or not? We’ll let the reader make that judgement.

That said, prices do act as a signal of scarcity. If the recent surge in gas prices is a reflection of a mismatch between supply and demand, lowering the price to consumers is only going to aggravate that mismatch. Conversely, if the goal is to reduce fuel consumption for whatever reason, increasing its price is going to work better that reducing it.

How was this graph created: Search FRED for “federal diesel tax.” From the “Edit Graph” panel, use the “Customize data” field to search for and add the “federal gasoline tax,” “state fuel tax,” and disposable income series. (For the last, use the nominal not real measure.) Apply formula (a+b+c/1000)*100. (Use 100 here to get percentages and 1000 here to get the same units.)

Suggested by Christian Zimmermann.



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