About aneconomicsense

Economist

Measures of GDP; How Recessions Are Determined and Dated; the Economy in the First Half of 2022; and the Prospects for 2023

A.  Introduction

The Bureau of Economic Analysis (BEA) of the US Department of Commerce released on August 25 its second estimate of the GDP accounts for the second quarter of 2022.  The figures indicate that GDP fell by 0.6% in the quarter, a bit less than the fall of 0.9% in its initial estimate released in late July (what it calls its “advance estimate”).  But it was still a fall, and following the reduction in GDP in the first quarter of 2022 (by 1.6% in the most recent estimate), there have now been two consecutive quarters where estimated GDP has gone down.

Many mistakenly believe that an economic recession is defined as two consecutive quarters of falling real GDP.  This is not correct – there is no such definition for a recession.  But it is easy to see that such confusion can arise, as a commonly used “rule of thumb” is that if real GDP fell for two consecutive quarters, then this is a sign that the economy is in a recession.

The reality is more complex.  Much more enters into a designation that the US economy was in a recession in some period.  Indeed, while the quarterly GDP figures are certainly important, they actually play a secondary role as the designation of a recession is based more on a number of indicators that are available on a monthly basis (such as the monthly employment figures, wholesale and retail sales, and more).  Indeed, the dates assigned to a recession (when it began and when it ended) are of specific months, not calendar quarters.

Usually this does not matter much.  Such economic indicators normally move together.  But not always, and they certainly have not in 2022 thus far.  While real GDP as currently estimated fell in the first half of this year, the employment market has been extremely strong.  Employment has grown by an average of over 440,000 per month in the first half of 2022, and the unemployment rate fell from an already low 4.0% in January to just 3.6% in June and an even lower 3.5% in July.  This is the lowest the unemployment rate has been since 1969 – matching the 3.5% rate hit in early 2020 just before the pandemic crisis.  While a formal determination has not been made on whether the economy is in a recession or not – and as discussed below will not be made until more of the data are in and the trends are clear – it is highly doubtful that the first half of 2022 will be so designated.

This blog post will cover how that designation process works.  But it is of interest first to look at the current estimates of what has happened to real GDP in the first half of 2022.  The period illustrates well the pitfalls of exclusively focussing on whether real GDP fell for two consecutive quarters as an indicator of whether the economy is in a recession.

There is indeed a question of whether GDP in fact fell in the first two quarters of 2022 – even setting aside the issue that there will be further revisions in the current estimates.  Specifically, the BEA issues figures for GDP based on two different ways of estimating it:  One is based on expenditures (for consumption, investment, etc.) which it labels the expenditure-based GDP (or just GDP for short), and another is based on incomes earned (which it labels Gross Domestic Income, or GDI for short).  They should in principle be identical, as whatever is spent is someone’s income.  But the two estimates will differ in practice, as they are based on different approaches and different sources of data.

As seen in the chart at the top of this post, these two measures of GDP, while generally moving together over time, have diverged significantly from each other since late 2020.  And in the first half of 2022, GDI continued to grow while GDP fell.  The reasons for this divergence are not clear, but I am sure economists at the BEA are ardently trying to figure this out now.

At this point we do not know what the answer is.  It might well simply be a consequence of the estimates still being recent, and might go away as further data become available to yield better estimates.  But that difference between the two estimates illustrates well why one should not simplistically assert that two quarters of real GDP decline signals a recession underway.

This post will thus first look at the recent data, focusing on what the GDP and GDI concepts mean, why they should be identical (and indeed, for this reason serve as a useful check on each other in the estimates), and what might have caused the recent divergence.  The post will then look at the process followed in the US for designating periods of economic recession and expansion, where for historical reasons the process is overseen not by the government, but rather by a nonpartisan organization called the National Bureau of Economic Research (NBER).  It will conclude with a brief discussion of the prospects for 2023.  While it is doubtful that the economy in the first half of 2022 will ever be designated as being in a recession, the prospects of a recession in 2023, or even later in 2022, are substantial.

B.  Gross Domestic Product and Gross Domestic Income

Gross Domestic Product (GDP) is a measure of production – how much the economy is producing.  But while it is a measure of production, the primary way estimates are made of how much was produced, as well as the way most people think of GDP, is not by how much is produced but by how much is used.  That is, everyone who has taken an Econ 101 macro course will know that GDP will equal the sum of Private Consumption, Private Investment, Government Consumption and Investment Spending (often combined as simply Government Spending – but excluding spending on transfers to households such as for Social Security), and Net Foreign Trade (Exports less Imports).

Why should that sum of expenditures equal production?  The trick (as discussed in this earlier post on this blog) is that investment includes investment in any net buildup of inventories.  That is, changes in net inventories in a period will balance out any difference between what was produced and what was sold.

This is then a convenient way to estimate GDP.  But one should keep in mind that GDP is a measure of production, and that there are other ways to measure that which should yield the same result.  One is to approach it via incomes, as whatever is produced and sold is then someone’s income (when one includes the value of any net inventory accumulation).  Those incomes accrue as someone’s wages (including all forms of labor compensation) or as profits (net operating surplus more formally).  The BEA can assemble available data on wages and profits in the economy, and the sum should in principle be the same as GDP (with adjustments for indirect taxes such as sales taxes and including whatever was set aside in depreciation allowances).  (For those interested in the detailed breakdown, see Table 1.10 in the BEA NIPA Interactive Tables.)  For clarity, the BEA labels this income-based estimate of what should sum also to GDP as Gross Domestic Income, or GDI.

A third approach to estimating GDP is to estimate directly what production was in each sector of the economy.  The BEA does this as well, but one needs to take into account that the net contribution to production in the economy as a whole is not the gross output of any given sector, but that gross output less the value of whatever inputs it purchased from other sectors of the economy.  This is so that one does not double-count what is being produced.  That is, in each sector one estimates what economists call “value-added” – the value of what was produced less the value of the material inputs purchased to make that product.  The sum of this value-added across all sectors should once again be GDP.  The BEA refers to these estimates of value-added by sector as “GDP by Industry”.

The three measures should in principle yield the same figures for overall GDP.  But while in practice generally close, they don’t exactly match as they are all estimates based on data, and the data come from different sources.  Furthermore, that data is subject to revision as more complete information becomes available, so even though initial estimates may differ by some amount, the degree of those differences generally falls over time as better estimates become possible.

Why then does the public discussion generally focus on the expenditure-based estimate of GDP?  One simple reason is that it is always the first one that is published.  The BEA issues this initial estimate of GDP (its “advance estimate”) just one month after the end of the calendar quarter.  This estimate is eagerly awaited both by policymakers and the general public, and receives a good deal of attention in the news media.

The BEA only releases its first estimate of the income-based estimate of GDP (i.e. GDI) a month later, along with its second estimate of the expenditure-based approach to estimating GDP.  Since it comes later, and possibly also because it is less well known, less attention is given by the public (and consequently in the news media) to this income-based estimate of GDP.  But the quarter-to-quarter changes in GDI can differ significantly from the quarter-to-quarter changes in the expenditure-based estimate of GDP.  For example, in the estimates released on August 25, the revised (“second estimate”) for expenditure-based GDP was of a fall of 0.6% in real terms (at an annual rate and seasonally adjusted).  However, the initial estimate of the income-based estimate of GDP (i.e. of GDI) was that GDP grew by 1.4%.  This will be discussed further below.

The initial estimates using the third approach to estimating GDP (i.e. value-added by sector) are then only made available a month after that, i.e. along with the third estimate of the expenditure-based estimate of GDP and the second estimate of the income-based estimate of GDP (i.e. GDI).  These estimates receive even less attention.  The BEA has also been publishing them along with the monthly GDP reports only recently – starting in September 2020 for the second quarter of 2020 GDP figures.  They released them separately before with some further lag, and the underlying data series themselves are only available (in a consistent series based on the current methodology used) from 2005 on a quarterly basis and from 1997 on an annual basis.

Furthermore, while this third approach to estimating GDP could yield an additional check on the GDP estimates, in practice the BEA does not do this.  I am not sure precisely why, but in its methodology for estimating these GDP by Industry figures, it scales the estimates so that the sum matches the expenditure-based estimate of GDP for the period.  The BEA may feel that the underlying data for the GDP by Industry estimates are not sufficiently good to provide an independent estimate of GDP, or it might be concerned that a third but different estimate for GDP might cause confusion in the public.

It is thus not surprising that most attention is paid to the expenditure-based estimates of GDP.  They are available first, and thus they provide the figures that first indicate whether GDP is rising or falling.  But there is also a more fundamental reason why they deserve such attention.  As we have known since Keynes, the primary driver of GDP in the near term is what is happening to the various components of demand for GDP, i.e. the expenditure-based components of GDP.  Production (within the bounds of productive capacity) will respond to those demands, and in particular production will fall when the sum of those demands (what economists call “aggregate demand”) falls.  This might be in response to some financial crisis (with chaos in the financial markets leading to less investment), or to the Fed raising interest rates with the deliberate intention of reducing demand (with the higher interest rates leading to less investment), or due to cuts in government spending (possibly due to politics, such as when the Republican-controlled Congress elected in 2010 forced through government expenditure cuts in the subsequent years, thus slowing the recovery from the 2008/09 financial and economic crash while blaming this on Obama).  Similarly, spurs to growth will be found in what is happening to the various expenditure components of GDP.

The interest in this estimate of expenditure-based GDP is thus well-founded.  But one needs to keep in mind that the figures are still estimates, and are imperfect as the data are imperfect.  An independent check on this, such as from the independent estimate of GDP based on estimated incomes (i.e. GDI), is thus of interest.  Henceforward, for simplicity I will generally refer to the expenditure-based estimate of GDP as simply “GDP”, and the income-based estimate as simply “GDI” (the same terms the BEA uses).

The two estimates (GDP and GDI) generally move quite closely together.  This can be seen in the chart at the top of this post.  Note that while the figures here are shown in real terms, the price deflator used for both GDP and GDI is the same.  The reason is that while price indices can be calculated for the goods and services that make up the expenditure-based estimate of GDP, one cannot define such price indices for the wages and profits that make up the income-based GDI. Thus to deflate the GDI estimate to real terms, the BEA uses the same price deflator as it has estimated for GDP.  This is convenient for the interpretation of the figures as well, as any deviation of one from the other cannot then be attributed in some way to two different price deflators being used.  There is only one.

[Technical Note:  The figures are of GDP and GDI each quarter, but they are shown at annual rates from seasonally adjusted figures.  The price indices used are what are called “chain-weighted dollars”, with 2012 as the base year.  One may recall from an Econ 101 class that a Laspeyres price index calculates the index based on the weights of the underlying items in overall expenditures in the base year, and a Paasche price index calculates the index based on the weights of the underlying items in overall expenditures in the final year.  A chain-weighted index calculates the index based on weights that change period by period based on expenditures on the items in each of the periods.]

The estimates of GDI have generally been above the estimate of GDP in recent years – and especially so since late 2020.  That has not always been the case.  One can see in the chart at the top of this post that estimated GDI was below estimated GDP between mid-2007 and the start of 2011.  But broadly they move together, as one should expect and as can be seen in a chart of the data going back to 1947 (when quarterly estimates of GDP and GDI began):

There is, of course, a scale effect over such a long period, as real GDP has grown by a factor of ten between 1947 and 2022.  The difference between GDP and GDI will not then be so apparent in the earlier years, and it is more meaningful to look at the difference between the two estimates as a share of GDP in that year:

The BEA assigns a label to the difference between GDP and GDI:  they call it simply the “Statistical Discrepancy”.  That difference as a share of GDP was quite small and generally within a range of +/- 1% of GDP between 1947 and the late 1970s, and more often positive than negative (i.e. estimated GDP above estimated GDI).  It then moved between greater extremes, but remained generally positive, from the early 1980s to around 1997.  The volatility then continued, but since 1997 the Statistical Discrepancy was more often negative than positive (estimated GDP less than estimated GDI).

Since the fourth quarter of 2020 it has, however, turned more sharply negative than it has ever been before.  Why?  No one really knows, although there is some speculation (and I am sure work underway at the BEA to try to figure this out).  A higher GDI than GDP implies that estimated incomes are higher than what the expenditure-based estimates would imply.  It is possible that some of these incomes are becoming more difficult to estimate.  For example, there are conceptual issues in how properly to account for compensation being paid by transfers of assets – such as happens with stock options – and the BEA data sources may not be good at estimating these.  Individuals may treat these as part of their compensation (as they should), but in the company accounts they may be treated as a transfer of assets (the stock options) that may not then be properly reflected in recorded profits (at least from the viewpoint of the National Income Accounts).

It is also possible that the sharp increase in the Statistical Discrepancy in the last couple of years may in part go away as more complete data becomes available and new and better estimates for GDP and GDI are worked out.  But at this point we just do not know.

Due to these differences in the estimates, many of the more careful economists working with the GDP figures use not solely the GDP estimate nor solely the GDI estimate, but rather the simple average of the two.  By weighting them equally in this simple average, the implication is that the uncertainty on each is similar.  The BEA itself provides this simple average in its monthly releases of the GDP estimates (although with the item blank in the first release of each quarter when only the expenditure-based GDP estimate is available).  But these figures on the average of GDP and GDI do not receive much attention from many.

Focusing in on the last few years:

The chart is as before, but now shows also the simple average of the GDP and GDI estimates.  The path of GDP as estimated by the GDI figures has been substantially above the path as estimated by the expenditure-based GDP figures since the fourth quarter of 2020.  And in the first half of 2022, GDI has continued to grow (although at a slower pace than before) while GDP as measured by expenditures fell.  Neither of the changes are large.  And the simple average of the two comes out as almost flat, but positive (with growth of 0.1% in the first quarter of 2022 and 0.4% in the second quarter – in the estimates as currently published).

Thus by this measure of GDP, the economy has continued to grow in these most recent estimates in the first half of 2022, although at only a slow rate.  This could well change with the revisions to come as more complete data become available, but for now they show positive growth in each of the quarters.

C.  Designating and Dating Recessions

The commonly accepted designation of whether the US economy is in a recession or not is not made by a government agency, nor is it based on some set of specific criteria (such as that GDP fell for two consecutive calendar quarters).  Rather, for historical reasons the designation is made through a private, nonprofit and nonpartisan, organization that supports economic research in the US called the National Bureau of Economic Research (NBER).

The NBER was founded in 1920, on the initiative largely of two individuals – one an executive at AT&T and the other a socialist labor organizer who had a Ph.D. in Economics from Columbia.  While very different in their views on what to do about unemployment, both recognized that the data available at the time were insufficient for an adequate understanding of the conditions.  They founded the NBER with the intention for it to support teams that could produce such data – more than what could be done by individual academics.  They deliberately kept it nonpartisan, where the NBER itself would not produce specific policy recommendations, and were able to obtain funding from a range of sources, including from some of the larger corporations of the time, from certain foundations, and from other private donations.

The NBER’s first director of research was Wesley Clair Mitchell, then a professor at Columbia and an expert on business cycle research.  He assembled a team that produced what was then the best data of the time on business-cycle fluctuations in the US.  This research was published and proved influential.  As part of it, as well as in continued such work later sponsored by the NBER, the researchers would determine, to the best the data they could assemble would allow, the periods when the US economy was expanding and when it was contracting.  Periods of contraction were labeled recessions.

The US Department of Commerce started to produce more systematic data on the state of the economy in the 1930s, due in part to the Great Depression then underway.  They worked out the basic GDP concepts we now use and how to measure them in practice given the data they could assemble, with this early work done often with the help of researchers from the NBER.  A particularly prominent such then-young researcher was Simon Kuznets, a student of Wesley Clair Mitchell who then moved to the NBER, and who is often credited with developing the original concepts for GDP (and who subsequently was granted a Nobel Prize in Economics for this work).

The Department of Commerce (now through its Bureau of Economic Analysis) has since produced the official GDP accounts for the US.  In 1961, a decision was made that rather than have this government agency make a determination on whether the economy was in a “recession” (defined in some way) or not, they would instead simply reference the determinations made at the NBER.

These determinations of the NBER were originally made as a by-product of the research it sponsored on business cycles in the US.  In 1978, the NBER decided to formalize the process and make it independent of specific research projects by appointing a committee of academic economists to make such designations.  The committee members represented a range of views but all members had a focus on macro and business cycle issues.  Formally, it was named the NBER Business Cycle Dating Committee.  There are currently eight members of this Committee, and there has been only limited turnover over time.  There have been only seven other individuals who have served on the Committee in the 44 years since its origin, and the chair (Robert Hall), as well one of the current Committee members (Robert Gordon), have served on it since its start.  Robert Hall is a well-respected economist, a professor at Stanford since 1978, and is politically and economically conservative.  He was a supporter of the Reagan tax cuts and has advocated for a flat tax to replace progressive income taxes in the US.

This NBER committee was set up by Martin Feldstein (a professor at Harvard) soon after he became the president of the NBER.  Feldstein was also a well-respected economist as well as open-minded.  He was the Chair of the Council of Economic Advisers in the Reagan White House between 1982 and 1984.  During that time he brought to the Council two bright and capable young economists with recent Ph.Ds. – one to look at domestic policy issues (Larry Summers) and one to focus on foreign trade issues (Paul Krugman).

The NBER Business Cycle Dating Committee meets when members believe they have sufficient data and other information to determine whether the economy had reached a business cycle peak (following which it would be contracting, with this then a recession), or a trough (after which the economy would be expanding, and the recession would be over).  Such determinations have been made by the Committee anywhere between 4 and 21 months after the dates of those business cycle peaks or troughs (as later determined).  They have no deadline for this, but meet when they believe they may have sufficient data to draw a conclusion.  Indeed, sometimes they have met and then deferred a decision, as they felt that upon review they did not yet have sufficient information to make a decision at that point in time (see this news release for one example).

Keep in mind that an economy in recession is one where economic activity is contracting.  It is not defined as a period where economic activity might be considered “low” in some sense, such as below some previous peak.  Thus unemployment will in general still be relatively high at the point where the economy has started to expand again and has thus emerged from the recession.  This may be confusing to some, as economic conditions “feel” (and in fact are) very similar to how they were the month before a trough was reached.  Indeed, it is common that the unemployment rate will still be growing for a period after that trough even though the economic recession (as defined here) is over.  For example, the NBER Committee determined that the 2007/2009 contraction (and thus recession) ended in June 2009.  At that point, the unemployment rate had hit 9.5% – higher than at any point since Reagan (when unemployment peaked at 10.8%).  But the unemployment rate continued to rise after June 2009, peaking at 10.0% in October 2009.

How then is a “recession” defined?  The NBER Committee defines it as:

“a significant decline in economic activity that is spread across the economy and that lasts more than a few months. The committee’s view is that while each of the three criteria—depth, diffusion, and duration—needs to be met individually to some degree, extreme conditions revealed by one criterion may partially offset weaker indications from another.”

Note that it must be what the Committee determines to be a “significant” decline, spread across much of the economy and not simply concentrated in a few sectors, as well as a decline that lasts for a substantial period (normally more than just a few months).  But no specific minimum values are specified for any of these factors.

The Committee also dates the recession (i.e. the dates of the peak and the trough in economic activity) to a specific month.  For this reason alone, the GDP data will not suffice.  It is only available quarterly.  Rather, the Committee has explained that it pays particular attention to the following data series (from the BEA, the Bureau of Labor Statistics, and other sources), which are made available and published monthly:

Real personal income less transfers;

Real personal consumption expenditures;

Employment (both nonfarm payrolls from the Survey of Establishments and employment as reported in the Current Population Survey of households);

Real manufacturing and wholesale/retail trade sales;

Index of industrial production.

But while the Committee has explicitly noted it pays attention in particular to these data series, they can and will look at whatever they feel may be relevant to their decision.

Once they determine the month in which the economy reached a peak or a trough, they will also report on which calendar quarter they believe the economy reached its peak or trough.  This is normally, but not always, the calendar quarter of the respective peak or trough of the months marking a recession, but not always.  Sometimes it might be the quarter before, or the quarter after.  For example, in the short but extremely sharp downturn in the spring of 2020 due to the lockdowns required to deal with Covid, the date marking the start of the recession (when the economy had reached its peak) was February 2020 and the trough was set as April 2020.  But the peak quarter was determined to be the fourth quarter of 2019, not the first quarter of 2020.

It also should be noted that for these determinations of the quarters where the economy had reached its peak or trough, the Committee does not focus on the expenditure-based estimate of GDP, but rather on the simple average of this GDP and GDI.  And as noted above, by this measure GDP rose in the first half of 2022 (according to the current estimates).

Could the Committee get this dating wrong?  Certainly – they are only human, and judgment is required in making these decisions.  Others can and sometimes do disagree, as one would expect in any science.  But the Committee has been careful, makes its decisions only when they believe sufficient time has passed to allow them to make a decision, and the members of the Committee represent a range of perspectives.  And while they do not say so explicitly on the NBER website where they explain their work, I strongly suspect that the Committee operates by consensus, and that if there is not a consensus when some such meeting has been called, they defer their decision until more complete data allows a consensus to be reached.

For this reason, the dates set by the NBER Committee for the beginning and the end of a recession are generally accepted as soundly based.

D.  Conclusion and the Prospects for 2023

Was the economy in a recession in the first half of 2022, as a number of  commentators have asserted?  (See, for example, this report on Fox Business, that asserted the US was in what they called a “technical recession” in the first half of 2022, or these unsurprising statements from Republican Senators Rick Scott of Florida and Rob Portman of Ohio.)

Formally, the NBER Committee has not met on this, so no such determination has yet been made.  But more fundamentally, based on the criteria the Committee uses it is highly doubtful that it will at some point decide the economy was in a recession in the first half of 2022.  The job market as well as other measures have been extremely strong.  Furthermore, even the GDP measure has been misinterpreted in the media as the Committee pays more attention to the average of the estimates for the expenditure-based GDP and the income-based GDI rather than just the former.  By this measure, the economy in fact grew in the first half of 2022 – although not by much and where future revisions in the data might change this.  But even if future data should indicate there was in fact a decline, it would certainly not be by much.

I should hasten to add that this does not mean the economy might not soon be in a recession.  Personally, I believe there is a significant possibility that the economy will be in a recession in 2023, possibly starting later in 2022.  Government spending is coming down sharply from the giant packages passed under Trump in 2020 and then continued under Biden in 2021 to provide relief from the Covid crisis; households are now spending savings that some had accumulated during the pandemic period; and the Fed is raising interest rates with the deliberate intent to slow the economy in order to reduce inflation.  I will expand on each of these in turn.

Using data from the Congressional Budget Office, total federal government spending rose by $2.1 trillion dollars in FY2020 under Trump, an increase of close to 50% from the $4.4 trillion spent in FY2019.  It rose from 21.0% of GDP in FY2019 to 31.3% of GDP in FY2020.  That was gigantic and unprecedented in the US other than during World War II.  It then stayed at roughly that level in FY2021, the first year under Biden (or rather two-thirds of a year as Biden was inaugurated on January 20 and the fiscal year starts on October 1).  In FY2021 federal government spending in fact fell as a share of GDP to 30.5% while rising in dollar value by $269 billion.  But in FY2022 it has now been reduced under Biden by $1.0 trillion – falling as a share of GDP by 7 percentage points to 23.5% of GDP.  There has not been such a fall in government spending since 1947 (as a share of GDP).

In terms of the federal government fiscal deficit, the deficit was at 4.7% of GDP in FY2019 (already substantially higher under Trump than the 2.4% of GDP it was in FY2015, as Trump increased spending while cutting taxes – mostly on the rich and on corporations).  The deficit then jumped to an unprecedented level (other than during World War II) of 15.0% of GDP in FY2020, before falling to 12.4% of GDP in FY2021 under Biden and an expected 3.9% of GDP in FY2022.  Note that this deficit in FY2022 is well less than the 4.7% of GDP in FY2019 under Trump before the Covid crisis.

This sharp cutback in federal government spending under Biden (not the story normally told by Republican politicians) would in itself be deflationary.  It has not been, however, as households as well as businesses are now spending balances many had saved and built up in 2020 and continuing into 2021.  These saving balances were built up from what they received under the various government support programs as well as due to other Covid-related programs (such as the option to suspend payments on certain debts), while spending was kept down (one did not go out to eat at restaurants as often, if at all, for example).  Note this was not the case for everyone.  Many households could only continue to barely get by – spending what they received.  But for other households, the programs led them to increase their savings balances.

The constraints on spending lifted during the course of 2021, and as accumulated savings were spent there was greater demand for goods than supply.  Prices were bid up despite the sharp cutback in government spending in FY2022.  Amplified also as a consequence of the Russian invasion of Ukraine in February 2022 that led to jumps in the prices of foods and fuels, the year-on-year increase in the CPI hit 9.1% in June 2022, before falling some to a still high 8.5% in July 2022.

The jump in the CPI – which started in mid-2021 – has led the Fed to raise interest rates.  Their aim is that the higher interest cost will lead to lower investment, which will reduce aggregate demand.  It hopes to do this without tipping the economy into a recession, but coupled with the sharp cuts in federal government spending and depletion of the excess savings that had built up during the pandemic, there is a significant danger that the Fed will not succeed in this.

It is always tricky, as interest rates are a blunt instrument for moving the economy.  Also, interest rates affect demand only with some lag that is hard to predict.  Finally, if a sharper than desired downturn does appear imminent and some boost in federal government spending becomes warranted to offset this, a Congress controlled by Republicans following the November elections would almost certainly block this.  As discussed above, one saw such dynamics during the Obama presidency following the election of a Republican-controlled Congress in November 2010.  They forced through government spending cuts in the subsequent years, despite the still weak economy following the 2008/09 collapse – the first time there were such cuts in government spending (since at least the 1970s) when unemployment was still high following a recession.  This slowed the pace of the recovery.

There could very well be a repeat of that mistake in 2023.  A recession cannot be ruled out.

Contribution to GDP Growth of the Change in Inventories: Econ 101 Again

A.  Introduction

The contribution of changes in inventories to changes in reported GDP is easily misunderstood.  One saw this in reports on the recent release (on July 28) by the Bureau of Economic Analysis (BEA) of its first estimate of GDP for the second quarter of 2022.  It estimated that GDP fell – at an annualized rate of -0.9% in the quarter – and that along with the first quarter decline in GDP (at an estimated rate of -1.6%), the US has now seen two straight quarters of falling GDP.  While there will be revisions in the coming months of the second quarter figures, as additional data become available, a fall in GDP for two straight quarters has often been used as a rule of thumb for an economy being in recession.

News reports on the figures noted also that were it not for the estimated change in inventories, GDP would have gone up rather than down.  The estimate was that GDP fell by -0.9% (at an annual rate) in the second quarter, and that the change in private inventories alone accounted for a 2.0% point reduction in GDP.  That is, if the inventory contribution had been neutral, GDP would have grown by about 1% rather than fallen by almost 1%.

But it would be wrong to attribute this to “decreases in inventories”, as some reports did.  Inventories grew strongly in the fourth quarter of 2021, with this continuing at a similarly strong pace in the first quarter of 2022 and still (although at a slower pace) in the second quarter of 2022.  How, then, could this have contributed to a reduction in GDP in 2022?

It is easy to become confused on this.  While really just a consequence of some basic arithmetic, it does require a good understanding of what GDP is and how changes in inventories are reflected in GDP.  I discussed this in a January 2012 post on this blog, but that was more than a decade ago and a revisit to the issue may be warranted.  This post will examine the problem from a different perspective from that used before.  It will start with a review of what GDP measures, and then use some simple numerical examples to show how changes in inventories affect GDP.  It will then use a series of charts, based on actual numbers from the GDP accounts in recent years, to show how changes in inventories have mattered.

A note of the data:  All the figures used come from the BEA National Income and Product Accounts (NIPA), as updated through the July 28 release.  These are often also called by many (including myself) the GDP accounts, but NIPA is the more proper term.  Also, the figures for inventories in the NIPA accounts are for private inventories only.  Inventories held by government entities are small and are not broken out separately in the accounts.  Instead, changes in such inventories are aggregated into the figures for government consumption.  While I will often refer to “inventories” in this post, the measures of those inventories are technically for private inventories only.

B.  Inventories and GDP, with Some Simple Numerical Illustrations

GDP – Gross Domestic Product – is a measure of production (product).  Yet as anyone who has ever taken an Econ 101 class knows, GDP is typically described as (and measured by) how those goods and services are used:  for Consumption plus Investment plus Government Spending plus Net Foreign Trade (Exports less Imports).  In symbols:

GDP = C + I + G + (X-M)

Where “C” is private consumption; “I” is private investment; “G” is government spending on goods or services for direct consumption or investment; and “X-M” is exports minus imports, or net foreign trade.

(Imports, M, can be thought of either as an addition to the supply of available goods or netted out from exports, X, to yield net exports.  To keep the language simple, I will treat it as being netted out from exports.)

Private investment includes investment both in new fixed assets (such as buildings or machinery and equipment) and in accumulation of inventory.  This accumulation of inventory, or net change in inventory, is key to why this equation adds up.  As noted above, GDP is product – how much is produced.  Whatever is produced can then be sold for consumption, fixed asset investment, government spending on consumption or investment, or net exports.  If whatever is produced exceeds what is sold in the period for these various purposes, then the difference will accrue as inventories.  If the amount produced falls short of what is sold, there will have to have been a drawdown of inventories for the demands to have been met.  Otherwise it would not have been possible – the goods had to come from somewhere.

The balancing item is therefore the change in inventories.  It is what allows us to go from an estimate of what is sold to an estimate (if one knows how much inventories changed by) of what was produced, i.e. to Gross Domestic Product.

How then do changes in inventories affect measured GDP?  This is best seen through a series of simple numerical examples, tracing changes in the stock of inventories over time.

Period

Stock

Change

Change in the Change

0

2000

1

2200

200

2

2400

200

0

Start with a stock of inventories in the economy as a whole in period 0 of say 2000 (in whatever units – perhaps billions of dollars).  This stock then grows to 2200 in period 1 and 2400 in period 2.  The change in inventories in period 1 will then be 200, and that change in inventories will be one of the components making up GDP (along with private consumption, private fixed investment, and so on).  It is an investment – an investment in inventories – and thus one of the uses of whatever product was produced in the period.  It will equal the total of what was produced (GDP) less what was sold for the sum of all final demands (private consumption, private fixed Investment, government, and net foreign trade).

With the stock of inventories growing to 2400 in period 2, the change in inventories in that period will once again be 200.  Hence the contribution to GDP will once again be 200.  This is the same as what its contribution to GDP was in the previous period, and hence the higher inventories would not have been a contributor to some higher level of GDP – its contribution to GDP is the same as before.  The change in the change in the stock of inventories is zero.

But this does not mean that inventories fell in period 2.  They grew by 200.  But that was simply the same as its accumulation in the prior period, so it did not add to GDP growth.

To make a contribution to GDP growth in period 2, the addition to inventories would have had to have grown.  For example:

Period

Stock

Change

Change in the Change

0

2000

1

2200

200

2

2500

300

100

In this example, the stock of inventories grew to 2500 in period 2.  The change in inventories was then 300, which is higher than the change in inventories of 200 in period 2 – it is 100 more.  This would be reflected in a GDP in period 2 which would be 100 higher than it would have been otherwise.

If, on the other hand, the pace of inventory accumulation slows, then inventory accumulation will subtract from GDP:

Period

Stock

Change

Change in the Change

0

2000

1

2200

200

2

2300

100

-100

In this example, inventories are still growing in period 2 – to a level of 2300.  This is 100 higher than what it was in period 2.  But the change in inventories is then only 100 – which is less than the change of 200 in period 1.  Inventories are still growing but they will add less to GDP than they had in period 2.  Hence they will subtract from whatever growth in GDP there might have been otherwise.

This is what happened in the recently released estimates for GDP growth in the second quarter of 2022.  Inventories were still growing, but they were growing at a slower pace than in the prior quarter.  In terms of annual rates (and with seasonally adjusted figures), inventories grew by $81.6 billion in the second quarter (in terms of constant 2012 dollar prices; see line 40 of Table 3 of the BEA release).  But this was less than the $188.5 billion growth in inventories in the first quarter of 2022.  In percentage point terms, that difference (a reduction of $106.8 billion) subtracted 2.0% from what GDP growth would have otherwise been in the second quarter (see line 40 of Table 2 of the BEA release).  With the changes in the other components of GDP, the end result was that estimated GDP fell by 0.9% in the quarter.  Thus one can attribute the fall in GDP in the quarter to what happened to inventories, but not because inventories fell.  It was because they did not grow as fast as they had in the previous quarter.

C.  Changes in Inventories in the Data

Based on this, it is of interest to see how inventories have in fact changed quarter to quarter in recent years.  These changes, and especially the changes in the changes, are volatile.  They can make a big difference in the quarter-to-quarter changes in GDP.  Over time, however, they will even out, as there is some desired level of inventories in relation to their sales and producers will target their purchases to levels to try to reach that desired level.

Start with the chart at the top of this post.  It shows the stock of private inventories by quarter going back to 1998.  The figures are in constant 2012 dollars so that inflation is not a factor (and more precisely using what are called “chained” dollars where the weights used to compute the overall indices are based on prior period shares of each of the goods – so the weights shift over time as these shares shift).

Stocks generally move up over time as the economy grows, although there have been reductions in periods when the economy was in recession or otherwise disrupted.  Thus one sees a fall in 2001, due to the recession in the first year of the Bush II administration, an especially sharp fall in 2008 with the onset of the economic and financial collapse in the last year of the Bush II administration with this then carrying over into 2009, and then a fall again in 2020 due to the Covid lockdowns.  The trough in the most recent downturn was reached in the third quarter of 2021, following which the stock of inventories grew rapidly.  They are still, however, slightly below the level reached in mid-2019 even though GDP is higher now than what it was then.

One starts with the stocks, but as was discussed above, the contribution to GDP comes from the accumulation of inventories – the change in the stocks.  These changes, based on the figures underlying the chart at the top of this post, have been:

There is considerable quarter-to-quarter volatility.  Note that the figures here are expressed in terms of annual rates.  That is, they are each four times what the actual change was (in dollar terms) in the given quarter.  One sees that the change in the fourth quarter of 2021 was quite high – higher than in any other quarter of this 24-year period – and was still almost as high in the first quarter of 2022.  The increase was then less in the second quarter of 2022, but was still a substantial increase (of $81.6 billion at an annual rate) in the quarter.

The changes in inventories are a component of GDP, but the contribution to the growth in GDP comes from the changes in the change in inventories.  These are easily computed as well by simple subtraction, and were:

These are now very highly volatile, and one sees especially sharp fluctuations in the last couple of years.  With all the disruptions of the lockdowns, the subsequent supply chain disruptions, and the very strong recovery of the economy in 2021 (with GDP growing faster than in any year in almost four decades, and private consumption growing faster than in any year since 1946!), it has been difficult to manage production to meet expected demands and allow for some desired target level of inventories.

This had a substantial impact on the quarter-to-quarter changes in GDP, both positive and negative.  Focussing on the recent quarters, the changes in inventories were a $193.2 billion increase in the fourth quarter of 2021, and as noted before, a further $188.5 billion increase in the first quarter of 2022 and a further although smaller increase of $81.6 billion in the second quarter of 2022.  These were the changes in inventories.  But the changes in the changes, which is what will add to or subtract from GDP growth, were a very high $260.0 billion in the fourth quarter of 2021, and then a fall of $4.7 billion in the first quarter of 2022.  This reduction in the first quarter of 2022 came despite inventories increasing in that quarter by close to a record high level.  But they followed a quarter where inventories rose by a bit more, so the change in the change was small and indeed a bit negative.

In the second quarter of 2022 inventories again rose – by $81.6 billion.  But following the close to record high growth in the first quarter of 2022, its contribution to the growth in GDP in the quarter was substantially negative.  The $81.6 billion increase in inventories in the second quarter was $106.9 billion less than the increase of $188.5 billion in the first quarter.  And it is this $106.9 billion which is a contribution to (or in this case a subtraction from) what GDP growth would have been in the quarter.

Finally, one can show this also in the possibly more helpful units of the percentage point contribution to the growth in GDP:

Although in different units, the chart here mirrors closely the preceding one, as one would expect if one has been doing the calculations correctly.  The only difference, in principle, is that with GDP growth over time, the dollar values of the quarter-to-quarter changes will look larger when expressed as a share of GDP in the earlier years of the period.

There are, however, some minor differences deriving from the nature of the data used.  The chart here was drawn directly from the figures presented in the BEA NIPA accounts for the percentage point contributions to GDP growth from changes in inventories.  One can also calculate it by taking the quarterly changes in the change in constant dollar terms (from the preceding chart, in red), dividing it by the previous quarter’s GDP (as one is looking at growth over the preceding quarter), and then annualizing it by taking one plus the ratio to the fourth power.  I did that, and the curve lies very close to on top of the curve shown here (in orange).

But not quite, due in part to rounding errors that compound when one is taking the changes and then the changes in the changes.  In addition, inventories by their nature are highly heterogeneous, with some going up and some down in any given period even though there is some bottom line total on whether the aggregate rose or fell.  This makes working with price indices tricky.  The BEA figures are based on far more disaggregated calculations than the ones they present in the NIPA accounts, and their underlying data also have more significant digits than what they show in the tables they report.

D.  Inventories to Sales, and Near Term Prospects

What will happen to inventories now?  Given how important changes in inventories are to the quarter-to-quarter figures on GDP growth, economists have long tried to develop some system to predict how they will change (as have Wall Street analysts, where success in this could make some of them very rich).  But they have all failed (at least to my knowledge).

One statistic that many focus on, quite logically, is the ratio of inventory to sales:

The figures here were computed from data reported in the BEA NIPA Accounts, Table 5.8.6B, where inventories include all private inventories while sales are of goods (including newly built structures) sold by domestic businesses.  Inventories are by nature of goods only, and hence one should leave out services (as an increasing share of services in GDP would, on its own, lead to a fall in the ratio).  Sales of newly built structures are included as one has inventories of building materials.  The figures on the sale of goods by domestic businesses are provided by the BEA.  Note that “sales” here are expressed on a monthly basis.  Hence the ratio is of inventories in terms of months of sales.

As one sees in the chart, the ratio of inventory to sales has been coming down over time.  This is consistent with all the literature advising on tighter inventory management.  There was then an unusually sharp decline in 2020 – a consequence of the Covid lockdowns – that bottomed out in the second quarter of 2021 (as a share of sales) and has since grown strongly.  But the ratio is still below where it was prior to the pre-Covid trend, although how much below depends on how one would draw the trend line pre-Covid.

Where will it go from here?  While important to what will happen to the quarter-to-quarter figures for GDP growth, as discussed above, I doubt that anyone has a good forecast of what that will be.  While there might well be room for the inventory to sales ratio to rise from where it is now, keep in mind that the ratio can rise not only by adding to inventories but also by sales going down.  And while GDP growth was exceptionally strong in 2021, it has been weak so far this year (indeed negative) and that weakness might well worsen.  Personally, while I do not see that the economy is in recession now (employment growth has been strong, with 2.7 million net new jobs in the first half of 2022, and the unemployment rate has been just 3.6% for several months now), the likelihood of a recession in 2023 is, I would say, quite high.

There also have been recent announcements by major retailers that the inventories they are currently holding are well in excess of what they want, and that they will take exceptional measures to try to bring them down.  Target announced a plan to do so in June (with a warning it will squeeze their near-term profits), Walmart announced in July they had similar issues (and that it would slash prices to move that inventory), and other retailers have announced similar problems.  If this is indeed a general issue, then those efforts to bring down inventories in themselves will act as a strong drag on the economy, making a recession even more likely.  And as was discussed above, the stock of inventories does not need to fall in absolute terms to cut GDP growth – a change that is less than what the change had been in the prior period will subtract from GDP growth, even though the inventories may still be growing in absolute terms.

Firms such as Target and Walmart employ many highly trained professionals to manage their inventories.  Yet even they find it difficult to get their inventories to come out where they want them to be.  If they and others now begin a concerted effort to bring down their inventory levels in the coming months, the impact on GDP in the rest of this year could be severe.

Gas Prices are High, But Don’t Blame the Usual Suspects: Implications for Policy

A.  Introduction

Gasoline prices in the US (and indeed elsewhere) are certainly high.  Given that in the US much of the voting population views cheap gas as much of a right as life, liberty, and the pursuit of happiness, this has political implications.  It is thus not surprising that politicians, including those in the Biden administration, are considering a range of policy measures with the hope they will bring these gas prices down.  And while fuel prices have indeed come down some in the last few weeks from their recent peak, they remain high, and their path going forward remains uncertain.

One of the most common such measures, already implemented in six states (as of July 6) and under consideration in many more, has been to reduce or end completely for some period state taxes on fuels.  And President Biden on June 22 called on Congress to approve a three-month suspension of federal gas and diesel taxes.  The political attraction of such proposals is certainly understandable.  A Morning Consult / Politico public opinion poll in March found that 72% of those surveyed would favor “a temporary break from paying state taxes on gasoline”, and 73% would favor a similar “temporary break from paying federal taxes on gasoline”.  It is hard to find anything these days that close to three-quarters of the population agree on.

But would this in fact help to reduce what people are paying at the pump?  The answer is no.  One has to look at what led to the recent run-up in gas and other fuel prices, and only with a proper understanding of that can the appropriate policy response be worked out.  Cutting taxes on fuels should not be expected to lead to a reduction in what people pay at the pump for their gas.  Indeed, what could lower these prices would be to raise fuel taxes, and then use the funds generated to cover measures that would, in the near term, reduce the demand for these fuels.

This post will first examine the recent run-up in fuel prices, putting it in the context of how that market has functioned over the last decade and what is different now.  Based on this, it will then look at what the impact would be of measures such as cutting fuel taxes, releasing crude oil from the nation’s Strategic Petroleum Reserve, encouraging more drilling for oil, and similar.  None of these should be expected, under current conditions, to lead to lower prices at the pump.

Rather, one could raise fuel taxes and use these funds to support measures that would reduce the nation’s usage of gas.  For example, an immediate action that would be effective as well as easy to implement would be to encourage ridership on our public transit systems by simply ending the charging of fares on those systems.  One could stop charging those fares tomorrow – nothing special is needed.  Some share of those driving their cars for commuting or for other trips would then switch to transit, which would lead to a reduction in fuel demand and from this a reduction in fuel prices.  The lower price will benefit all those who buy gas, including those in rural areas who have no transit options.  And as will be discussed, the cost to cover what is being collected in fares would be really quite low.

A note on usage:  All references to “gas” in this post are to gasoline.  They are not to natural gas (methane) nor indeed any other gas.  Fuels will refer to gasoline and diesel together, where statements made with a specific reference to gas will normally apply similarly to diesel.

B.  The Rise in Fuel Prices and the Factors Behind It

Fuel prices have certainly gone up in the first half of 2022.  As shown in the chart at the top of this post, despite the fall in recent weeks fuel prices (the line in red) are still 75% above where they were in early-December (in June they were more than double), with those December 2021 prices double what they had been in October / November 2020.  Crude oil prices (the line in black) have also been going up, and have been since late 2020 (following the dip earlier in 2020 due to the Covid lockdowns).  This rise in the price of crude oil can explain the rise in the retail prices for fuels up through early this year.  But as we will discuss, the factors behind the more recent rise in fuel prices changed in late February 2022 – coinciding with Russia’s invasion of Ukraine.

First, some notes on the data.  The figures all come from the Energy Information Administration (EIA), part of the US Department of Energy, and weekly averages are used.  For reasons to be discussed below, the price of “fuel” is a 2:1 weighted average of the prices of regular unleaded gasoline (unleaded) and diesel (ultra low-sulfur no. 2), both wholesale FOB spot prices and for delivery at the US Gulf Coast.  While it is an average, this does not really matter much in practice as the wholesale prices of gas and diesel have not, at any point in time, differed by all that much from each other.  They move together.  Nor have their average prices over time differed by all that much.  For the period since the start of 2014, the average wholesale cost of gas was $1.81 per gallon while that for diesel was $1.90 – a difference of just 9 cents.  While there can be larger differences at various points in time, for the purposes here the distinction between the two fuels is not central.

The cost of crude oil (the line in black) is for West Texas Intermediate (FOB spot price, for delivery at Cushing, Oklahoma), the benchmark crude most commonly used in the US and also the basis for the main financial contracts used to hedge the price of oil in the US.  It is presented here on a per-gallon basis to make it comparable to the other prices, where one barrel of oil is equivalent to 42 gallons.

A refinery will purchase crude oil and then through various processes refine that oil into gasoline, diesel, and other petroleum products that can then be used as fuels by our cars and trucks as well for other purposes.  The difference in price between what the refinery can sell these finished products for and the cost of the crude it buys as the primary input is called the “crack spread”.  While the crack spread will be unique for each refinery, as it will depend on the technology it has (how modern and efficient it is), what types of crude it has been designed to process most efficiently (as different crudes have different characteristics, such as viscosity and sulfur content), the mix of specific products it produces (the share ending as gas or diesel, but also jet fuel, heating oil, etc.), and the location of the refinery (as the crude oil must be delivered to it, and it then must arrange for the delivery of its products to the ultimate purchasers), a simplified standard spread is often calculated to provide an indication of how market prices are moving.  The most common such standard spread is called the “3-2-1 crack spread”.

The 3-2-1 crack spread is calculated for a refinery that would process 3 barrels of crude oil into 2 barrels of gasoline and 1 barrel of diesel.  For the calculations here, all were expressed on a per-gallon basis, and the specific fuels and delivery locations are as specified above.  The 3-2-1 crack spread is then simply calculated as the value of two gallons of gasoline plus one gallon of diesel, minus the cost of three gallons of crude oil, with that total then divided by three as three gallons of fuel are being produced.  It is a gross spread, as a refinery will of course have other operational costs (including the cost of labor), plus the refinery will need to generate a return on the capital invested for it to be viable in the long term.  But this simple gross spread is often used as an indicator of what is happening in the market.

That calculated 3-2-1 crack spread is presented as the blue line in the chart at the top of this post.  From 2014 through 2021, it rarely moved above $0.50 per gallon, and it averaged just $0.36 per gallon over the period.  In 2021 it was not much higher, averaging $0.42 per gallon over the year.  But from late February 2022, coinciding with the Russian invasion of Ukraine, it has shot upward.  As of the week ending June 24 it had reached $1.46 per gallon, but as of the week ending July 8 it had come down to $1.02.  That is still high – it is still close to three times what it had averaged before.

To understand the factors that led to this jump in the crack spread this year, one should first consider how prices are determined in these markets.  The key is that the crack spread is not itself an independently determined price, but rather a spread between the price of the final product (gasoline and diesel fuels) and the price of crude oil, both of which are determined independently.

Start with the final products – gasoline and diesel:  These are sold in highly competitive markets of numerous gas stations pricing their product to sell at the best prices they can get, but where for the nation as a whole, stocks of the fuels are kept within a narrow range.  One can calculate (again from EIA data), that in recent years (2017 through 2022H1), the nation’s stocks of motor gasoline have averaged 236 million barrels, with no clear upward or downward trend.  While the stocks will vary over the course of the year due to seasonality, at comparable weeks in the year they have been kept in a relatively narrow range, with a standard deviation of just 2.1% of the weekly averages over this period.  This means (assuming a normal distribution, which is reasonable) that in about two-thirds of the weekly cases, the stocks will be within +/- 2.1% of the average for those weeks (one standard deviation), and in 95% of the cases will be within +/-4.2% of the averages (two standard deviations).  That is, the stocks are managed to stay within a relatively narrow range, although at a target level that depends on the season of the year.

In such a market, if producers (either directly or through the gas stations they contract with) price their gasoline at too low a price for the conditions of the time, they will find that their stocks will be running down – soon to unsustainable levels.  They would need to ration what they sell, either by long lines at the pumps or by some direct rationing system.  And if they price their gasoline at too high a price, they will find their stocks accumulating to levels that exceed what they can store.  They sell their gas for the highest price they can get, but that price will be constrained to be such that they will be able to manage their inventories of refined gasoline (and similarly for diesel fuels) to within a certain range.  And as noted above, that range is a narrow one of normally just +/- 2% or so.

Crude oil prices are determined differently.  Here there is a world market, where OPEC producers (as well as a few producers who cooperate with OPEC, where the most prominent is Russia) set production ceilings by OPEC member (and cooperative partner) with the aim of achieving some price target.  They do not always succeed in achieving that target, as global conditions can change suddenly.  Recent examples include conditions triggered by the Covid crisis in 2020, or by the global financial crisis that began in the US in 2008.  OPEC also responds sluggishly to changes in the markets, particularly when crude oil prices are rising – which many OPEC members are rather pleased with – as the production quotas must be negotiated among the members.  But it is correct to say that the market for crude oil is a managed one, although often not a terribly well managed one due to the inherent difficulty in forecasting global demands and then responding on a timely basis to unexpected changes.

With the retail price of the fuels determined on the one side by conditions in the competitive markets for fuels, and the price of crude oil determined on the other side by the actions of OPEC and those who cooperate with it, the crack spread will be a margin that has now been determined.  That is, it is not a price that the refiners themselves will normally be able to set.  There is a lower limit, as a gross crack spread that is too low to cover their other operating costs (and is expected to stay that low for some time), will lead refiners to shut down their operations.  But based on what we observe for the period from 2014 in the chart at the top of this post, it appears that a crack spread of $0.36 per gallon (the average from 2014 through 2021) is sufficient to cover such costs as well as provide a return on the capital invested, as refineries stayed open and continued to produce over this period with such a spread.

This spread then jumped in late February of this year – coinciding with the Russian invasion of Ukraine – to a level that has been between three and four times what it was before.  What happened?  While the Russian invasion was clearly significant, one should look at this in the context of where the market was just prior to the invasion.  It was tight, and the Russian invasion should be seen as a tipping point where refinery supplies of these fuels could no longer meet the demand.

First of all, demand has been growing, both in the US and in the rest of the world, as economies have recovered from the lockdowns that were necessary at the start of the Covid crisis.  The US enjoyed a particularly strong recovery in 2021, with real GDP growing by 5.7% – the fastest such growth in any calendar year in the US in close to 40 years.  And the personal consumption component of GDP rose by 7.9% in 2021 – the fastest such growth in any year since 1946!  But it should be recognized that this was coming after the sharp falls in 2020 due to Covid (of 3.4% for GDP and 3.8% for personal consumption).  The rest of the world recovered similarly in 2021, although at various different rates.

This raised the demand for gas, diesel, and other fuels.  Petroleum refineries could keep up in 2021, as this followed the lower demands they had for their products in 2020.  But the lower demands (and hence lower refinery throughputs) in 2020 due to Covid did have an effect.  It led to decisions to close some of that refinery capacity, leading to a reduction in capacity in 2021 for the first time in decades.  Albeit small, worldwide, refinery capacity fell from 102.3 million barrels per day in 2020 to 101.9 million barrels in 2021 (a fall of 0.4%).  Refinery capacity in the US fell similarly, from 18.1 million barrels per day in 2020 to 17.9 million barrels in 2021 (a fall of 1.1%).  With the recovery in demand for fuel products in 2021, this placed producers at closer to their limits.

But the limit to how much petroleum refineries can produce is pretty rigid.  They normally operate on a continuous, 24-hours a day, basis – at a rate as close as possible to their design capacity.  Thus they cannot increase production by adding an extra work shift or by running processes at a faster rate.  They do need to shut down periodically for preventive maintenance, as their systems are complex and they must deal with flammable liquids that are being processed at often high temperatures and pressures, where a failure of some part can lead to a catastrophic explosion.  They must also shut down on occasion for safety reasons, such as when a hurricane or other major storm threatens (an increasingly frequent occurrence in recent years in the US Gulf Coast, where much of the US refinery capacity is located, due to climate change – such weather-related shutdowns are discussed further below).  In general, then, refinery throughput is highly constrained in the short run by existing available capacity, which is being run continuously at as high a rate as they can.

Over the longer term, refinery capacity will depend on what investments are made to expand that capacity.  But new refineries cost billions of dollars, are rare, and when undertaken take many years to plan and then build.  Significant expansions in existing refineries are also very costly, and also require significant time to plan and then build.  Thus such investments are very carefully considered and are only made when they expect there will be a demand for the products of those refineries for many years to come – at least a decade or more.  It is not something they rush into.  Even if capacity is tight right now, such investments will not be made unless the owners expect those conditions to last for an extended time.  And even if the decision is made to make such an investment to expand capacity, it will normally take years before the added capacity will become available.

Thus in the near term, when one is already operating at close to the design limits of the refineries it will not be possible to supply much more than what the existing available capacity will allow.  Economists call this “inelastic supply”, as the percentage increase in supply of some product for some given percentage increase in the price that would be paid for that product (an “elasticity”) is low.  For refineries that are already operating at close to their technical limits, it will be very low.

The other factor in price determination is demand.  And for fuels such as gas or diesel, many will say the price elasticity of demand for such fuels is also low.  Indeed, a common view in the general population is that the price elasticity of demand for gas is zero – that they will have to buy the same number of gallons each week whatever the price is.  This is not really true (and contradicted by the assertion that they also cannot “afford” to pay more – if true, then at a higher price they will have to buy less).  But studies have found that while not zero, it is low.

For example, the Energy Information Agency in 2014 estimated the price elasticity of demand for gasoline in the US was just -0.02 to -0.04.  That is tiny.  It implies that if the price of gas were to rise by 10% (say from $4.00 to $4.40 per gallon), the demand for gas would decline only by 0.2 to 0.4%.  Other estimates that have been made have often been somewhat higher, although still low.  A widely cited review in 1998 by Molly Esprey, for example, examined 300 published studies, and found that the median estimate of this elasticity across those studies was -0.23.  This is still low.  It implies that a 10% increase in the price will be met by only a 2.3% fall in demand.

With a demand for fuel that does not go down by much when prices rise, and a supply for fuel that does not go up by much when prices rise (i.e. when refineries are already operating at close to their capacity), one should expect prices for fuels to be volatile.  And they are.  Even small shifts in the available supply or in the demand can lead to big changes in prices.

In these already tight markets of early 2022, Russia then invaded Ukraine on February 24.  The crack spread rose from $0.49 per gallon for the week ending February 25, to $0.64 the following week and to $0.74 the week after that.  It reached $0.88 by the end of March and $1.35 by the end of April.  As of the week ending June 24 it had reached $1.46, but then came down to $1.02 two weeks later.

The Russian invasion not only affected production at refineries in Ukraine, but international sanctions on Russia meant a significant share of Russian refineries would also no longer supply global markets.  While refineries in Ukraine are not a significant share of global capacity (just 0.2% in 2021), refineries in Russia are significant, with a 6.7% share of global capacity in 2021.  As a comparison, US refineries account for 17.6% of global capacity.

One should note that this does not mean that global capacity was effectively reduced by 6.7% of what it was.  Russian refineries continued to produce for their own markets, while also supplying others.  But the sanctions have reduced the volume effectively available by a significant amount.

In a market that was already tight, with refineries operating at close to capacity following the strong recovery demand in 2021 in the US and much of the world, such a reduction in effective supply acted as a tipping point.  The 3-2-1 crack spread shot up immediately.

C.  Policy Implications

What, then, can be done to reduce fuel prices?  I will take it as a given that that is the objective.  A case could well be made that to address climate change and the consequent need to reduce the burning of fossil fuels, high prices are good.  But while important, that is a separate issue I am not trying to address in this post.

First, where are gas prices now?:

The figures here are based on data gathered by the Bureau of Labor Statistics (BLS) for its calculations of the monthly CPI.  The figures are a consistent series going back to 1976 (further back than any other consistent series I have been able to find), are available in current price terms per gallon, and are not (here) seasonally adjusted so they reflect the actual prices paid that month.  And like the overall CPI that is commonly cited, it is an estimate of prices in urban areas.

As of June 2022, the average retail price of regular unleaded gasoline in the US was $5.058 per gallon.  For the chart, I have then shown what the historical prices would have been when adjusted for general inflation to the prices of June 2022 (based on the overall CPI).  The June prices are not the highest gas prices have been – they hit $5.51 a gallon in July 2008 – but they are close.  Although declining in recent weeks as I am writing this, it remains to be seen whether gas prices might resume their upward trend sometime soon.  The markets continue to be volatile, and prices could soon set a new record.

Whether that will happen will depend in part on what the policy response now is.  There are measures that can be taken that will reduce prices, but also measures that are being discussed that would likely have little effect, or might even raise prices. In this section, I will first discuss why, given the underlying causes of the price increases this year discussed above, some of the measures being discussed will likely do little and might indeed be counterproductive.  I will then discuss measures that could help lead to a reduction in prices.

1)  What Not To Do

First, some policies that will not lead to lower prices, or might even lead to higher prices:

a)  Perhaps the most widespread assumption is that if OPEC produced more crude oil, gasoline prices would then fall.  But that should not be expected given the current situation.  As seen in the chart at the top of this post, the crack spread widened sharply starting in late February, as a certain share of global refining capacity became not usable.  In the already tight markets refinery capacity became the effective binding constraint, not the price of crude oil.

More crude oil production by OPEC (or indeed by anyone) could well lead to lower crude oil prices – and indeed likely would.  But unless more of that crude oil can be refined into final fuel products such as gasoline, the available supply of gas in the market would not be affected.  Retail prices would remain the same.  What would change is that if crude oil prices decline by some amount with the increased supply of crude, the crack spread would widen.  That is, refiners would gain by this.  Consumers would not.

b)  For the same reason, sale of crude oil out of the Strategic Petroleum Reserve should not be expected to lead to lower retail prices for gas either.  President Biden announced on March 31 that the US would start to sell one million barrels of crude oil per day (an unprecedented amount) out of the US Strategic Petroleum Reserve for at least six months.  This announcement may well have had some effect on crude oil prices:  Crude oil prices had been rising through late March and then fell a bit (before returning to March levels in late May, and then continuing to rise until mid-June).  But this did not affect retail prices for fuels, which continued to rise until the last few weeks.  Rather, the crack spread rose (as seen in the chart at the top of this post) as refiners were able to obtain a larger margin between what they could sell their products for and what they had to pay for their crude oil.

c)  Also popular has been the proposal to reduce or eliminate taxes on the sale of gas and other fuels.  The federal tax is 18.4 cents per gallon on gasoline and 24.4 cents on diesel, while state taxes are of varying amounts.

President Biden on June 24 called on Congress to approve a temporary suspension of federal taxes on gas and diesel for three months.  As of my writing this, Congress had not approved such a suspension (it would complicate infrastructure funding, as such funding is linked to fuel tax revenues), and it does not look likely that it will.  But one never knows.  And as of July 6, six states had suspended their state fuel taxes for varying periods, with many more considering it.

What effect would such a tax cut have?  First, consider the federal tax, as it applies across the entire country.  As discussed above, the supply of fuels such as gas and diesel is constrained by available refinery capacity.  Economists refer to this as operating where the supply curve is “vertical”, in that a higher price for the fuel cannot elicit a significant increase in the supply of the fuel in the near-term, due to the capacity constraint.  A lower tax will not then lead to a lower price, as a lower price (if one saw it) would lead to greater demands for the fuels and refiners cannot supply more.  In such a situation, refiners are earning a rent, and a lower tax to be paid on the fuels will just mean that the refiners will be able to earn an even larger profit than they are already.  The crack spread will go up by the amount the tax on fuels is reduced.

The situation would be different if refiners could supply a higher amount.  Retail prices would fall by some amount due to the reduction in the tax, supplies would rise by some amount, and in the end consumers and refiners would share in the near-term gains from the lower tax.  What those relative shares will be will depend on how responsive the supply of fuels would be from the refiners (the elasticity of supply).  In the extremes, if refiners are able and willing to supply the increase in demand at an unchanged price (the supply curve is flat), then retail prices will fall by the entire amount of the tax cut and consumers will enjoy all of the benefit.  But if refiners are unable to supply more due to capacity constraints, then retail prices will be unchanged by the tax cut and refiners will pocket the full amount of the tax cut.  Currently, we are far closer to the latter set of circumstances than to the former.

The situation is a bit different at the state level.  If one state cuts its taxes while the taxes remain the same elsewhere, refiners will be able to move product to meet the higher sales of fuels in the state where taxes were cut.  This would, however, be at the expense of lower supply in the states that did not cut their taxes.  Fuel prices in the state cutting its taxes (and not matched by others) will fall by some amount due to the now higher availability of fuels in that state.  But with the overall supply constrained by what the refineries can produce, the lower amounts supplied to the rest of the country will lead to higher prices in the rest of the country.

Overall there will be no benefit, and indeed on average prices (net of taxes) will rise.  But there will be some redistribution across the states.  The amount will depend on what share of the states decide to cut their taxes.  At one extreme, if only one state does it and that state does not account for a large share of the overall US market, then the retail price (inclusive of taxes) will fall in that state.  If that state is small, prices elsewhere in the country would only rise by a small amount, but they still would rise.  But if more and more states decide to cut their fuel taxes, then one will approach the situation discussed above with the cut in federal taxes on fuels.  The full benefits of the lower taxes will accrue to the oil refiners, not to any consumers.

Finally, one needs to recognize that there is no free lunch.  The states cutting their fuel taxes will need to make up for the revenues they consequently lose.  To fund the expenditures paid for by the fuel taxes (often investments in road and other infrastructure), those states would need to raise their taxes on something else.

2)  What To Do

So what would lead to lower fuel prices given the current conditions?  The simple fact is that for prices to go down, one will need either to increase the supply of the refined products, or reduce the demand for them.  Taking up each:

a)  As was discussed above, refineries normally operate at close to their maximum capacity, and there is not much margin to respond to unforeseen demands.  Refineries are expensive, hence are not designed with much excess capacity to spare, and when operating are operated on a continuous, 24-hour a day, basis.  They also need to be shut down periodically for scheduled maintenance, as well as when unscheduled maintenance is required or when a strong storm threatens.

Still, there might be some measures that can be taken to push refinery throughput at least a bit higher.  Refiners certainly have an incentive to do so, given how high the crack spread is now (three to four times higher in recent months than what it was on average between 2014 and 2021).  But the crack spread does not need to be anywhere close to that high to provide a strong incentive.  A spread that is double what it would be in more normal times should more than suffice to elicit refiners to do whatever they can to maximize refinery throughputs.

There will also be an element of luck, given the increasingly volatile weather conditions that climate change has brought.  One can see this in a simple snapshot of a chart available on the EIA website, showing idle US refinery capacity (which is more properly measured by and referred to as distillation capacity) by month going back to 1985:

Volatility rose significantly starting in 2005 (the year of Hurricanes Katrina and Rita) and has been high since.  The sharp peaks seen in the chart are all in September or October – the peak months of hurricane season for the US.  Especially prominent peaks in the capacity that had to be idled were in September 2008 (Hurricanes Gustav and Ike), September 2017 (Hurricane Harvey), and September 2021 (Hurricane Ida).  With hurricanes threatening, refineries must be shut down for safety.  How fast they can then reopen depends on how much damage was done, but will require some time even if there was only limited damage.

It is impossible to say what will happen in the upcoming hurricane season.  But with the market so tight, any closures could have a large impact on prices.

b)  The other side to focus on is demand.  This could also be more productive in the near term given that little more may be possible on the supply side (as well as subject to chance, given the uncertainty in what will happen in the upcoming hurricane season).  But progress on demand-side measures will depend on political will, and Americans have been historically averse to measures that would reduce the near-term demand for fuels.

But it is important to recognize that not much would be needed in terms of reduced demand in order to reduce fuel prices by a substantial amount.  This is precisely because the demand for fuels is so price inelastic, as discussed before.  That is, a substantially higher price for gas does not lead to all that much of a reduction in the quantity of it purchased.  What economists call the “demand curve” (the amount purchased at any given price) is close to vertical.  When this is coupled with an also close to vertical supply curve for refined products (as refineries are operating close to their capacity, and cannot produce more no matter what price they can get), small shifts in the amount demanded at any given price will have a major effect.

[An annex at the end of this post uses simple supply and demand curves to examine this graphically.]

Given this lack of sensitivity to price under current conditions for both supply and demand, it would not take all that much to get prices to fall by a substantial amount.  Supply of refined products is constrained by refineries operating at close to their maximum, while on the demand side, purchases of fuels do not adjust by much when prices change.  As was noted above, the EIA in 2014 published an estimate of the price elasticity of demand for gasoline of just -0.02 to -0.04.  That implies that a 10% rise in the price of gas would reduce demand by only 0.2 to 0.4%.  Others have estimated higher elasticities, but all still relatively low.

Suppose, for the sake of illustration, that the price elasticity of demand was -0.10, so that a 10% rise in the price would lead to a reduction in demand of 1%.  This relationship also tells us a good deal about the shape of the demand curve – specifically its slope (locally).  If facing a completely vertical supply curve, then it implies that a 1% reduction in the demand for gasoline at any given price (meaning a shift in that demand curve to the left by 1%) would lead to a new price that is 10% lower than before.  And a 2% shift would lead to a price that is 20% lower.  While extrapolating in this way from what might be true for small changes to something substantially larger is dangerous, a 20% fall in the price of gas that is at $5.00 per gallon would lead to a new price of $4.00 per gallon – all resulting from just a 2% shift in the demand.  This is substantial but depends, as noted above, on how responsive demand is to the price.  If truly not very responsive, as is commonly held by many, then it will not take much of a reduction in demand (at any given price) to lead to a very substantial reduction in the price.

How, then, might one reduce the demand for fuels?  One possibility would be to encourage more work from home.  One saw the effect of this on fuel demands (and hence prices) in 2020, when working from home was required for health reasons at the start of the Covid crisis.  Workers are now returning to the office, but perhaps our political leaders should encourage a delay in this, or at least a slower pace on the return.  But it probably could not be mandated, and indeed probably should not be simply for the sake of cutting the price of gasoline.  And while opinions differ on this, some would say that extending work-from-home even further will reduce worker productivity.

A better way to reduce fuel demands would be to provide a greater incentive to take public transit rather than drive a car for a higher share of the trips one undertakes.  One could do the following:  First, raise tax revenues that could be used for these measures by raising federal taxes on fuels by, say, $0.25 per gallon.  As was noted above, when one is operating with a vertical supply curve, as we are now, increasing taxes on fuels will not lead to higher prices for the consumer.  The crack spread would fall, but with that spread that has varied between $1.00 and $1.50 per gallon in recent months, a higher fuel tax of $0.25 per gallon would still leave that crack spread at two to three times the $0.36 it averaged before.

According to EIA data, the total supply of motor gasoline in the US averaged 9.3 million barrels per day between 2016 and 2019 (taking a four-year average, and excluding 2020 due to Covid), while diesel supply averaged 4.0 million barrels per day.  Mutliplying this sum of 13.3 million barrels per day by 42 gallons per barrel and 365 days per year, the annual supply of these fuels averaged 204 billion gallons.  Rounding this to 200 billion gallons, a tax of $0.25 per gallon would raise $50 billion on an annualized basis.

This could be used to support public transit.  Something that could be done instantly (starting literally the next day) would be simply to stop charging fares on public transit systems – including buses, rail (subways), commuter trains, and whatever.  According to the National Transit Database, in 2019 all these public transit systems generated a total of $16.1 billion in revenues, mostly from fares but including also other locally-generated revenues such as from the sale of advertising.  (Again, 2020 was an unrepresentative year due to Covid so it is better to use 2019 figures.)  The database does not separate out fares from other revenues, but even if one treated it all as fares, the $16 billion needed would be far below the $50 billion that would be generated (on an annualized basis) by increasing the federal tax on gasoline and diesel by $0.25.

Filling empty seats on buses and subways also does not cost anything.  Indeed, operating costs would in fact go down by not having to collect fares.  There are significant direct costs in collecting fares (and to ensure too much is not stolen), but one would also gain operational efficiencies.  Buses now take a relatively long time to cover some route in part because at each stop people have to line up and go one-by-one through the front door to pay their fares in some way.  Not having to take so long at each stop would allow the buses to cover their routes at a faster pace.  This would increase effective capacity or, if capacity were to be kept the same as before, one could provide that capacity with fewer buses and their drivers.

The aim is to shift people from driving their cars to taking public transit for a higher share of the trips they take.  To the extent this simply fills up some of the empty seats, there is then no additional cost.  But if ridership increases by a substantial amount (something to hope for), capacity would need to grow.  This could most easily be accommodated by additional buses.  This would cost something, but according to the National Transit Database figures, the total spent in 2019 from all sources (federal, state, and local), for all modes of public transit, for both operating and capital costs, was $79 billion.  With the $34 billion left after using $16 billion to cover fares (out of the $50 billion that the $0.25 per gallon would collect), one could cover an increase in spending on public transit of more than 40%.  This would be far more than what would be needed even with a huge increase in ridership.  But we are now going beyond the very short-term measures that could be taken to reduce fuel demand.  However, with the long-term need to reduce the burning of fossil fuels, it is good to see that even a relatively modest fee of just $0.25 per gallon of fuel could support such an expansion in public transit.

Such an approach would lead to a reduction in the demand for those fuels.  How much I cannot say with the information I have, but it should be substantial.  And as discussed before, even a small reduction in the demand for these fuels should lead to a substantial fall in their price.  That fall in price would also be of benefit to all those who purchase these fuels, including those in rural areas who are far from any public transit option.  It would be a mistake to presume that stopping the collection of fares on public transit systems would only be of benefit to the users of public transit.

D.  Concluding Remarks

The price of gas is certainly high.  Although not quite a record (when general inflation is accounted for) it is close.  This has led to a number of proposals aimed at reducing those prices.  Particularly popular politically has been to cut fuel taxes for at least some period, with this championed both by President Biden (for federal fuel taxes) and in a number of states (where several have done this already for the state-level fuel taxes).  Many also blame OPEC for managing supplies in order to drive crude oil prices higher.  To address this, there have both been major sales out of the Strategic Petroleum Reserve (of one million barrels of crude a day), as well as diplomacy to try to get others to boost their supply of oil.

Under current market conditions, however, these initiatives should not be expected to reduce prices.  The issue right now is that refineries are the binding constraint.  They are producing as much of the refined products (fuels, etc.) as they can, but limits on their capacity keep them from producing more.  One sees this in the crack spread, which jumped up in late February immediately following the Russian invasion of Ukraine.  A substantial share of Russian refinery capacity became unusable, and this served as a tipping point in an already tight market.

Under such conditions, a lower price for crude oil will not lead to lower retail prices for fuels.  While it would benefit refiners (the crack spread would widen), the prices at the pump would not be affected unless refiners were somehow then able to raise their production.  Similarly, a cut in fuel taxes should not be expected to lead to lower fuel prices at the pump.  Rather, refiners would receive a windfall as they would receive a higher share of the retail price.  Refiners are already doing extremely well, with a crack spread in recent months that has been three to four times what it averaged between 2014 and 2021.  There is no need to make this even more generous.

To reduce retail prices, one should instead reduce demand.  One measure that would do this would be simply to stop charging fares on public transit.  Inducing only some of those now driving to use transit more often could have a significant impact on prices.  This is because the demand for fuels is not terribly responsive to price (consumers in the US do not cut back on their car use all that much when prices are higher), at the same time as the supply of fuels is limited by refinery capacity (so the supply of fuels cannot go up by much despite higher prices).  With both the demand and supply curves close to vertical, a small shift left or right in the curves can have a big impact on prices.

It would not cost all that much to end the collection of transit fares either.  Not only can it be done instantly (simply stop collecting), but the total public transit systems received in 2019 in fares paid (as well as in other revenues, such as from advertising) was only $16 billion.  One could easily cover this by increasing the federal taxes on fuels.  As noted above, a cut in fuel taxes would not lead to lower fuel prices.  For the same reason, an increase in fuel taxes (within limits) would not lead to higher fuel prices.  And just a $0.25 per gallon increase in federal fuel taxes would raise roughly $50 billion on an annualized basis.

It should be kept in mind that all this is based on current market conditions.  Those conditions can change, and change suddenly – as we saw in late February with the launch of the Russian invasion.  Thus, for example, while the crack spread is currently very high, this is in part a function of where crude oil prices are.  As of the week ending July 8, the price of West Texas intermediate was $103 per barrel.  With gas and diesel prices where they were then, the crack spread was $1.02 per gallon – far above the $0.36 per gallon it had averaged between 2014 and 2021.  But at a higher price for crude oil, the crack spread would fall.  At $131 per barrel (and with gas and diesel prices where they were as of the week ending July 8), the crack spread would be back at $0.36 per gallon.  And at $146 per barrel, the crack spread would be zero.  Presumably, if crude prices approached such a level refiners would cut back on production, leading to higher gas and diesel prices.  Crude oil prices would then be the binding factor, and efforts to lower those prices (e.g. by sales out of the Strategic Petroleum Reserve, or more OPEC production) could then matter.

The point of this blog post is that that is not where we are now.  Current conditions call for a different policy response.

 

=========================================================

Annex:  Supply and Demand Curves to Show the Impacts of the Options

For those of you familiar with simple supply and demand curves, it is easy to see the impacts of the policy options discussed verbally in the text above.

The supply curve of fuels from refineries slopes upward from a curve that is relatively shallow to something increasingly steep and ultimately to vertical.  At relatively low levels of production, where there is a good deal of excess capacity in the refineries, a small rise in prices for the fuels will elicit a strong supply response.  But as production approaches the maximum capacity of what the refineries can produce (in the near term, given existing plant), there can only be little and ultimately no more production no matter how high the price goes.

The demand curve is steep.  That is, if prices rise by some amount, the quantity of fuels demanded does not fall by all that much.  The price elasticity of demand is low.

Retail taxes per gallon of fuel add to the supply cost.  That is, in the figure above, the red curve (marked S2) is what the supplies would be at some lower (possibly zero) retail fuel tax per gallon sold, while the blue curve (marked S1) is what the supply would be at some higher tax rate.  The supply curve will shift upwards.  That is, for any given quantity of supply, a higher price will be needed for that amount to be supplied.

When the supply curve is relatively shallow and upward sloping, as in the lower left of the diagram, then a cut in the tax (from the blue curve to the red), with a demand curve such as D3, will lead to some increase in supply and a significantly lower price.  The price, in the diagram, would fall from P3 to P4.  This is the logic behind the proposals, such as have been made by President Biden, for a temporary cut in federal fuel taxes.

However, this is not where current market conditions are.  Rather, refineries are operating at close to their maximum capacity, and one is in an area where the supply curve is close to vertical.  When the supply curve is vertical, a reduction in fuel taxes will simply shift that vertical curve downwards, but with one vertical curve simply sitting on top of the other vertical curve.  While a reduction in the tax per gallon will increase how much the refiner receives, after taxes, it will not lead to a higher amount being supplied (refiners cannot produce any more) nor will it lead to a lower price for consumers.  The lower taxes will simply be reflected in higher profits for the refiners.

In terms of the supply and demand curves depicted above, one would be in an area such as that depicted with the demand curve D1 with a price of P1.  If the supply curve is shifted downwards due to the tax cut (from the blue curve S1 to the red curve S2), with nothing done to affect the demand curve, then the price remains at P1.

In contrast, if the market conditions are such that the demand curve is at D1 and the supply curve is close to vertical, yielding a price of P1, a relatively modest shift in the demand curve to the left, i.e. from D1 to D2, leads to a sizeable fall in the price – from P1 to P2.  The fall in the price is large because both the demand curve and the supply curve are steep, and indeed close to vertical for the supply curve.  In such conditions, modest changes in demand can have a big impact on the price.

A shift of the demand curve shows how much demand would change (at the given price) due to a change in some underlying factor other than price.  Inducing drivers to shift to public transit by ending the charging of fares on transit systems is one such example.  There are others, such as encouraging more work from home (so no commute at all is needed).  And should the economy fall into a recession (which I see as increasingly likely in 2023), there will also be a reduction in fuel demands.  But the latter is not a cause of lower prices that one should hope for.