do product prices respond symmetrically to changes in crude prices?

Do product prices respond symmetrically to changes in

crude prices?

David Shin

FOLLOWING THE outbreak of the Gulf crisis on 2 August 1990, crude oil prices rose dramatically, resulting in the fourth price ‘shock’ since 1973. The subsequent rise in downstream petroleum product prices initiated much debate about the speed with which product prices respond to changes in crude prices. A large segment of the public appears to believe that, in response to changes in the price of crude oil, retail prices of petroleum products such as gasoline rise faster than they fall - that is, they adjust asymmetrically.

The purpose of this paper is to determine whether the perception of asymmetric price adjustment is valid. The paper will analyze the issue at both the retail and the wholesale level. Retail price symmetry will be analyzed by reviewing recent literature and updating earlier work on the speed of retail gasoline price adjustment, while wholesale price symmetry will be analyzed by collecting and analyzing new data.

The paper is organized as follows. Section 1 presents a general discussion of economic theory on the speed of price adjustment. Section 2 reviews and summar- izes recent literature. Section 3 presents the analysis of wholesale price symmetry, while Section 4 describes the behaviour of wholesale prices and refiner margins before and after the August 1990 crisis.’ Section 5 concludes the paper.

1. Economic theory and the speed of price adjustment The proposition that gasoline prices might adjust upward more rapidly than

downward is not a standard part of economic analysis. Traditional economic theory

The author is now wirh the American Forest and Paper Association, Washington DC, US. The paper wasprepared as a research study (no. 068) for the American Petroleum Institute.

Summer 1994 02774180 (94) OOO14-X 137

suggests that a seller of gasoline, facing a downward-sloping demand curve, maxi- mizes profits by charging a price that results from equating marginal cost with marginal revenue.] If the marginal cost increases because the price of an input, crude oil, has risen, then the profit-maximizing product price also increases. The speed at which this price adjustment takes place is not specified in traditional economic models, although it is recognized that menu costs (i.e. the cost of posting new prices, the cost of deciding what the new price should be, etc) preclude instan- taneous price adjustment.

Despite the lack of a formal theory, however, it is possible to sketch out what some proponents of asymmetrical price adjustment in petroleum product markets seem to have in mind, in terms of an economic model. Starting from an equilibrium in which gasoline prices are established and stable, a fall in the price of crude does not lead to an immediate decline in the retail (wholesale) price, since each re- tailer (or refiner) perceives that demand has not changed.2 Retailers (refiners) do not immediately adjust prices downward, since they expect that sales can be main- tained at the present price. The lower price of crude affords retailers (refiners) an opportunity to earn additional profits. However, they cannot do this indefinitely, since some retailers (refiners) realize that, by reducing their price, they,can sell larger quantities and earn even greater profits. Eventually, all retailers (refiners) will reduce their price to maintain market share. On the other hand, when the crude price rises, retailers (refiners) resist reductions in their normal margins and hence the retail (wholesale) price is adjusted upward more rapidly.3 Asymmetry occurs in the above example, because retailers (refiners) allegedly try to maintain their “normal” margin when prices rise, but try to capture larger margins that result, at least temporarily, when crude prices fall.

However, to the extent that refiners produce more than one output, refiner gasoline margins are inappropriate indicators of true refiner profitability. The single-input, multi-output nature of refineries implies that the input-output relationship between crude prices and product prices is complex. The fact that a barrel of crude oil produces a fixed amount of products suggests that increases in refiner margins on one product may be offset by decreases on another. Although no direct evidence exists for this compensating effect, the existence of this possibility makes the interpretation of single product margins, or single input-output relationships less meaningful. Thus, in this paper, a price series, that takes into account the multi-product nature of refineries, is constructed and analyzed.

While it is possible to provide an explanation of asymmetry based on market power, it is also possible to provide explanations of asymmetry within the context of oligopolistic and competitive markets. For example, Borenstein, Cameron and Gilbert (BCG) describe an oligopolistic model under which price asymmetry can re- s ~ l t . ~ In competitive markets, the role of inventories, expectations and storage costs, among other factors, could conceivably account for asymmetric price adjustment.

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For example, over-production or excess supply in one period can cause surplus stocks to be carried forward to the next period. When current stocks are scarce, however, stocks cannot be borrowed from future periods. As William and Wright show, this inability to borrow from the future creates an asymmetry in market behaviour.5 Prices can and will escalate sharply in any period when supply is reduced and stocks are exhausted. However, prices will not fall as dramatically during periods of over-supply, because buyers will expand stocks, transferring production from one period to the next. Thus, finding that the speed of price adjustment is asymmetric does not provide the basis for an inference regarding the competitive- ness of a market.

2. Literature review This section reviews the results of several studies on price symmetry. After a

brief synopsis of each, a comparison of the results will be discussed. All the studies summarized below use weekly or monthly price data in one of two modelling ap- proaches. The first is a derivative of a partial adjustment model that is potentially non-linear in quantities, so that it includes the possibility of different adjustments to cost increases and decreases. For example, the simple quadratic quantity adjustment model:

has the desirable feature that it encompasses the standard linear quantity adjustment model. Testing the hypothesis that al is equal to zero compares the linear adjustment path to the non-linear one. If aI is equal to zero, then the price adjustment is symmetric. If both % and al are positive, then price adjustment is asymmetric, with adjustment to cost increases being more rapid than adjustment to decreases in costs.

The second modelling approach is to partition the data sample into two, one of which contains observations where positive adjustments are being made and the other where negative adjustments are being made. The two samples provide sep- arate estimates of the upward and downward adjustment paths. If the coefficient for the upward path is equal to the coefficient for the downward path, then the price adjustment is said to be symmetric. If one is greater than the other, then the price adjustment path is said to be asymmetric. The pros and cons of each modelling approach will be discussed below.

One of the first studies to analyze the speed of retail gasoline price adjustment is Bacon’s study of gasoline markets in the United Kingdom.6 Bacon modelled the adjustment of retail prices to changes in wholesale prices using bi-weekly data. The sample period is June 1982 through January 1990. Using a non-linear partial adjustment model, Bacon found that, in the UK, retail gasoline price adjustment was asymmetric, although the degree of asymmetry was slight. Bacon

Summer 1994 139

concluded that the degree of asymmetry was so small that one could not decisively reject the view that the UK retail gasoline market is competitive.

Norman and Shin (NS) used the same non-linear partial adjustment model to analyze the speed of retail gasoline and heating oil price adjustment in the US.7 They used weekly data (from the Oil and Gas Journal) for January 1984 to March 1991 to analyze the speed of price adjustment. They found that retail gasoline prices adjust symmetrically to changes in both wholesale and crude oil prices. The results remained the same when the same non-linear partial adjustment model was re-estimated over a longer and more recent sample period (January 1984 through July 1992).*

Karrenbrock used the lagged partitioning method pioneered by Wolfram to analyze the speed of retail gasoline price adjustment to changes in the wholesale price of ga~o l ine .~ Karrenbrock used the United States Department of Energy’s (DOE’S) monthly data for the period January 1983 through December 1990. He found that, in response to changes in wholesale prices of gasoline, retail prices rise faster than they fall.

Specifically, Karrenbrock found that the pattern of retail price adjustment was such that, for every ten cents increase (decrease) in the price of wholesale gasoline, unleaded regular gasoline prices increased (decreased) 6.8 cents (3.0 cents) in the month of the wholesale price change, and increased (decreased) 3.5 cents (6.9 cents) in the following month. However, Karrenbrock concluded that consumers are not adversely affected, because the length of time in which retail prices respond fully to increases and decreases in wholesale prices is the same, and because wholesale gasoline price decreases are eventually passed along to consumers as fully as are wholesale gasoline price increases.

French used the same lagged partitioning method as Karrenbrock, but specified a different model.lo He used the same monthly price data from the DOE as Karrenbrock, but his sample period was October 1986 through January 1991. French found that retail gasoline prices adjust asymmetrically (rise faster than they fall) to changes in wholesale prices, but wholesale prices respond symmetrically to changes in crude prices (French’s retail results are very similar to Karrenbrock’s). For retail prices, French found that, for a ten cents increase (decrease) in the price of wholesale gasoline, retail gasoline prices increased (decreased) 6.4 cents (3.1 cents) in the month of the wholesale price change, and increased (decreased) 1.7 cents (2.5 cents) in the following month. For wholesale prices, for a ten cents increase (decrease) in the price of crude oil, wholesale gasoline prices increased (decreased) 7.9 cents (9.0 cents) in the month of the wholesale price change. Moreover, the response by refiners and retailers during the period immediately following the outbreak of the August 1990 crisis was typical to more normal, smaller cost increases. The difference in the wholesale and retail results leads French to conclude that price asymmetry at the retail level cannot be explained by the behaviour of major refiners.

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BCG also used the lagged partitioning method to analyze gasoline price symmetry. They used weekly retail gasoline prices from the Oil and Gas Journal. The sample period of their study was from January 1986 through December 1990. BCG found that retail gasoline prices adjust asymmetrically to changes in wholesale and crude prices.ll However, wholesale prices adjust symmetrically to changes in crud: prices. This leads BCG to conclude that refiners, who set wholesale prices, are not the source of the asymmetry.

D i X U S S i O n

The papers summarized above differ with respect to the modelling approach and the data used. Thus, there are two possible sources for the differences in the results. One possibility is that a model may be incorrectly specified, so that, even if identical price series were used for estimating purposes, the results would differ. A logical way to check this is to use the same data set for different models. If the choice of models is crucial, then estimating different models with the same data set should show the difference, although which model is “correct” cannot be deter- mined. Another possibility is a difference in the data sets. The data sets used in the studies could be so different that using two different data sets with one model can generate two different results. To identify the source of the discrepancy, a comparison of some key studies is made.

From a modelling perspective, the studies can be grouped into two broad categories. The studies by Bacon and NS are in one group, because both use the quadratic partial adjustment technique to model gasoline price adjustment. The remaining studies are in another group, since they all use the lagged partitioning method. From a data perspective, the studies can also be grouped into two different categories. This grouping, however, is different. Here, the studies by NS and BCG are in one group, because both studies use weekly retail gasoline prices from the Oil and Gas Journal. The remaining studies - excluding the study by Bacon - use DOE monthly retail gasoline prices.

Instead of attempting to compare the results of each study individually, we compare studies across groups. First, we compare the studies by NS and BCG. This comparison is useful, because the data sets used for both are similar but the models and the results are different. Similarly. the comparison of the NS study with Karrenbrock’s study is useful, because the former uses weekly prices while the lat- ter uses monthly prices. French’s study is omitted from the analysis, because it is similar to Karrenbrock’s study, in terms of both model structure and data used. Bacon’s study is omitted from the analysis, because it is the only study using non- US price data.

The studies by NS and BCG both use the weekly retail prices for unleaded gasoline, exclusive of taxes from the Oil and Gas Journal, as the measure for the retail price of gasoline. For the price of crude oil, the BCG study uses the one-month

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futures price for sweet crude oil on the New York Mercantile Exchange (NYMEX), while the NS study uses the spot market price Df West Texas Intermediate (Wn). The sample period used in the BCG study is January 1986 through December 1990, while the sample period for the NS study is January 1984 through March 1991 (expanded to July 1992 in this paper).

To determine whether the choice of models was crucial, the sample period of the NS data set was truncated, so it corresponded to the sample period in the BCG study. No substitution of the futures price for the spot crude oil price was made, since BCG found that using the futures price of crude oil rather than the WTI spot price caused only a slight difference in their results.12

When the truncated data set was used in the NS model, the speed of price adjustment was asymmetric. Indeed, the level of statistical significance of the price asymmetry coefficient was almost identical to that found when BCG used their data set in a quadratic partial adjustment model (about seven per cent).l3 This finding strongly indicates that the differences in the sample periods chosen in the two studies, rather than the differences in the models, cause the discrepancy in the results.

To determine the sensitivity of the price asymmetry result to changes in the sample period, the truncated sample was then expanded, so the sample period becomes June 1985 through March 1991. rather than 1986 through 1990. This results in an additional 43 observations. Using this expanded sample in the NS model results in a finding of symmetric adjustment speed.14 This reaffirms the observation that measures of asymmetrical price adjustment are sensitive to the sample period selected.

The reason for the sensitivity of the estimate to different sample periods becomes obvious when retail gasoline prices are plotted over time (figure 1). Re- stricting the sample period to January 1986 through December 1990 excludes relevant historical experience, including modest oil price declines in the 1984-86 period, as well as the sharp decline after December 1990. Since the sample period used in the NS study is richer in detail - it captures an extended period of price increases and price decreases - the results of the NS study are more representative of the historical experience.15

For model preference, it is unclear u priori whether the model used by BCG is better than that used by NS. The BCG approach is statistical in nature; it attempts to estimate the impact of current and lagged crude oil prices on the retail pnce of gasoline. In contrast, the approach developed by Bacon and used by NS is based on the behavioural premise that the retail price of gasoline adjusts partially in the current period to differences between the target retail price of gasoline and the last period’s actual price. The target price is estimated as a function of the wholesale price of gasoline (or the price of crude oil).

BCG contend that the quadratic partial adjustment model imposes equal pro- portional adjustments toward the new equilibrium in all periods after a shock to

142 OPEC Review

Figure 1 Nominal weekly retail gasoline* prices exclusive of tax

1984-92

cen ts/gallon

I 1

55 ’ 1

1984 1985 1986 1987 1988 1989 1990 1991 1992

*Unleaded regular. Source: Oil and Gas Journal Energy Database.

crude oil prices. While it is recognized that the quadratic model imposes a particu- lar signature -the time shape of the lagged adjustment path- on the price adjustment process, this constraint does not diminish the model’s ability to detect asymmetry, if indeed it is present. BCG also contend that the presence of a quadratic term imposes a constraint on the nature of the asymmetry. Specifically, the asymmetry becomes proportionally larger, as the difference between the current retail price and the long-run equilibrium price increases. Although no empirical evidence exists that the speed of price adjustment is a function of the magnitude of the crude oil (or wholesale) price change, it is conceivable that the degree of price asymmetry is related to the size of the price change. For instance, the larger the increase in the wholesale price of gasoline, the faster retail prices will adjust upward, since the cost of failure to adjust will be greater. Conversely, the larger the decrease in the wholesale price of gasoline. the slower retail prices will adjust downward, since the “cost” of adjusting quickly will be greater. This proposition, however, would have to be tested explicitly.’6

The approach adopted by BCG, as they recognise, is not without its.own re- strictions. In the BCG model, all crude price changes are categorized according to whether they are positive or negative. From this categorization, the effects of this

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week’s and the previous ten weeks’ changes in crude prices on this week’s retail gasoline prices are estimated. The difficulty of using this “switching regimes” approach is that it relies on a correct identification of when prices should be rising or falling. Only with an accurate knowledge of the lag structure, which is exactly what has to be estimated, can the correct switching points be identified. For example, the partitioning of price changes constrains the BCG model from addressing a price adjustment pattern of a price increase (decrease) that is immediately followed by a price decrease (increase). If the price of crude oil increases by $l/b in week I, and decreases by the same amount in week t+l, the BCG model does not allow the retail price adjustment to the initial increase to reverse when the crude price does. Thus the extent to which this week’s and the previous week’s changes in crude prices affect this week’s retail price depends on whether the crude price has increased or decreased this week. The fact that BCG uses a ten-week lag structure amplifies the problem, since crude prices are bound to vary over such an interval. Similarly, the extent to which the estimated lagged coefficient values are a result of overlapping changes in crude prices is uncertain. Is the value of the estimated coefficient in week t+2 the result of a change in the price of crude in week t, or in week t+l or in both weeks t and t+l?

BCG’s statistical results indicate that, for crude price increases beyond week 3, the estimated coefficients are not statistically different from zero.’’ For crude price decreases, only weeks 1, 4 and 8 are statistically significant. The presence of noise in the data, especially as the lag structure increases in length. highlights the problem of using the BCG approach to estimate the lagged effect of crude prices on retail prices when there are price reversals.

According to BCG’s results, the change in the crude oil price, as measured by the change in the futures price, explained only 56 per cent of the variation in retail prices. In contrast, Karrenbrock explained 90 per cent of the variation in the change in retail prices, using the change in wholesale prices as the explanatory variable. The large difference in the level of statistical fit suggests that the wholesale price of gasoline, rather than the price of crude, is the relevant cost for gasoline retailers. The link between retail prices and the price of crude oil may be weaker, due to shifts over time or over seasons in the demand for petroleum products derived from crude oil. Such shifts can result in changes in relative petroleum product prices and hence weaken the relationship between crude and retail prices.l* Thus, the BCG model of the crude retail link may be incorrectly specified. A mis- specified model has biased coefficient estimates. This fact, plus the insignificance of some lagged coefficients, indicates that BCG’s estimates of the cost of asymmetric price adjustment to changes in crude prices are q~es t ionab le .~~

To compare Karrenbrock’s study with the study by NS. Karrenbrock’s monthly data set was run through NS’s model. The result was a finding of an asymmetric price adjustment process. When NS’s data set was used in Karrenbrock’s

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model, price adjustment was found to be symmetric.20 This strongly indicates that the differences in the data sets used in the two studies, rather than the differences in the models, cause the discrepancy in the results.

The only difference in the two data sets is the retail gasoline price. The source of Karrenbrock’s retail gasoline prices, inclusive of taxes, is the DOE’S Monthly Energy Review (MER). These retail prices are Bureau of Labour Statis- tics (BLS) prices derived from the consumer price index. Although the sampling techniques of the BLS data are well known, the methods used to deduct taxes from the retail gasoline price series may have affected Karrenbrock’s results.

The NS study uses weekly OGJ retail gasoline prices, inclusive and exclusive of taxes in their model estimations.21 The average taxes on gasoline (computed from the difference in the retail prices, inclusive of taxes, and retail prices, exclusive of taxes), inferred from the OGJ data, approximate very closely the average taxes independently estimated by the API.22 This suggests that the OGJ’s survey accurately reflects gasoline taxes. The retail gasoline price series, exclusive of taxes used by Karrenbrock, was constructed by deducting the sum of the federal gasoline tax and a simple average of the 50 states’ gasoline taxes. No attempts to interpolate tax rates between the months where tax rates were actually observed were made. Therefore, even assuming that the gasoline prices, inclusive of taxes, reported in the MER, are accurate, the procedure for deducting taxes used by Karrenbrock may have introduced some b i a ~ . ~ 3

To summarize. the contradictory results in price symmetry literature are mostly the result of differences in the data used in the various studies. Although some have produced evidence that retail gasoline prices adjust asymmetrically to changes in wholesale prices, overall the hypothesis that retail price adjustment is symmetrical cannot be rejected by the data, particularly over longer periods. Wholesale gasoline prices, however, generally seem to adjust symmetrically to changes in crude prices. Also, unless improvements in retail gasoline price data are forthcoming in the near future, a more definitive conclusion on the nature of the retail price adjustment process will be difficult to ascertain.

3. The relationship between crude prices and wholesale product prices

crude prices is to estimate a cost-based model of wholesale product prices: One way to analyze the relationship between wholesale product prices and

PW, = % + a,PC, + y, (2)

Equation ( 2 ) states that the current volume-weighted wholesale product price, PW,, is a function of crude prices, PC,, and a random error term, y,.24 The

Summer 1994 145

constant term, %, can reflect the level of other costs. The volume-weighted wholesale product price is defined as:

n

i=l PW, = CPlai, (3)

n x R i = 1, c v i i = l

i=l

where Ri =+,

and Pi is the wholesale price of the ith refined product, Vi is the volume of output of the irh product and n is the number of refined products. A more comprehensive model of gasoline price formation would have a demand component, in addition to other supply factors, such as changes in refinery utilization rates, changes in inven- tory and changes in capital costs. For this analysis, however, a simple cost-plus model of price formation is assumed.

The effect of a change in crude prices on the volume-weighted wholesale prices is measured by:

where S1 = yt -

or:

APW, = &*APc, + 6,. ( 5 )

Equation ( 5 ) is estimated using data from various monthly DOE publications. The data include the monthly composite refiner acquisition cost of crude oil, monthly refiner prices of finished petroleum products for resale (exclusive of tax), and the monthly refinery net production of finished petroleum products.25

The finished products included in the volume-weighted price series are shown in figure 2. All but the products that comprise the “other” category are included, because monthly prices for liquefied natural gases and petrochemicals are not available in the DOE publications, nor are they easily obtainable. The effects of omitting these products should be negligible, because they account for only 15-20 per cent of total refinery output.26

The results of estimating equation ( 5 ) using ordinary least squares are presented in table l.27 The estimation sample period is January 1986 through May 1992.28

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Figure 2 US average production of finished petroleum products

1990

Table 1 Estimates with the weighted product price as the dependent variable

Coefficient t-statistic APC 0.9 1 20.8

R2 = 0.83, DW statistic = 2.13, SEE = 1.69

As expected, the change in the refiner acquisition cost of oil has a significant effect on the change in volume-weighted wholesale product prices.29 A one-dollar increase in the refiner acquisition cost of crude causes the volume-weighted product price to increase by 9l'cents in the same month. The validity of the cost-plus model of wholesale price determination is confirmed by the fact that 83 per cent of the monthly variation in the change in wholesale prices is explained by the variation in the change in crude prices (the R2-statistic is equal to 0.83).

To determine whether crude price increases and decreases symmetrically affect the volume-weighted wholesale price of refined products, the crude price changes in equation ( 5 ) are partitioned into price increases, PC+l, and price decreases, PCt :

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The magnitude of the estimated coefficients al and a2 will determine the existence of price asymmetry. If al is greater than a2, the volume-weighted wholesale prices of refined products rise faster than they fall and the price adjustment process is said to be asymmetric. If a1 is equal to a2, the volume-weighted prices of refined products rise as fast as they fall and the price adjustment is said to be symmetric. A trend variable and a lagged first difference in crude prices were included in the initial specification, but the terms turned out to be insignificant. The insignificance of the lagged term indicates that, at the refiner level, contemporaneous price changes are the most important determinant of the monthly wholesale product

From a statistical perspective, the lack of significance of the lagged variables could be due to the price adjustments occurring in shorter intervals. If the adjustments take a few weeks, monthly data will not pick up these responses.

The results of estimating equation (6) are presented in table 2. Table 2 indicates that, for every dollar increase (decrease) in crude prices, the volume-weighted

Table 2 Estimates with the weighted product price as the dependent variable

APC+ APC-

Coefficient t-statistic

0.9 1 15.20 0.92 14.20

R2 = 0.83. DW statistic = 2.13, SEE = 1.70

wholesale prices of refined products increase (decrease) by 91 (92) cents a gal- 1011.3' Thus, refiners adjust to crude price decreases essentially as fast as they do to crude price increases.

The results of using the refiner gasoline price for resale, APR, as the dependent variable in the partitioned model are presented in table 3.32 As expected, the explanatory power of this model is not as strong as the model with the volume- weighted wholesale product price as the dependent variable (the Rbtatistic is 0.7 1

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Table 3 Estimates with the wholesale price of gasoline as the dependent variable

Coefficient t-statistic

APC+ 0.80 9.37 APC- 0.90 9.55

R2 = 0.7 1, DW statistic = 1.98, SEE = 2.22

versus 0.83 and the standard error of the estimate is higher). Table 3 indicates that, for every dollar increase in the price of crude, the wholesale gasoline price increases by 80 cents a gallon. However, for every dollar decrease, the wholesale price decreases by 90 cents.33 Thus, refiners adjust to crude price decreases faster than they do to crude price increases.34 To determine whether the difference in the coefficient estimates is statistically significant, an F-test is performed.35 The null hypothesis that the estimated parameters for APC+ and APC- are equal cannot be rejected by the F-test. This is evidence that the wholesale gasoline price adjusts symmetrically to crude oil price increases and decreases.

To confirm the symmetry result, the same data set was run through a non- linear partial adjustment model. The model estimated (using a non-linear least squares routine) was:

where P, is the volume-weighted product price or the wholesale gasoline price, depending on which model was being estimated, P,-I is the appropriate price lagged one period, T is a time trend, and h and p are the various parameters to be estimated.36 For both the volume-weighted product price model and the gasoline price model, the quadratic adjustment term, hl ,was statistically insignificant. With 7 1 degrees of freedom, the estimated t-statistics for L I were 1.43 and 0.27 respec- tively. This suggests that both prices adjust symmetrically to changes in crude prices.

4. The behaviour of refiner margins The definition of price symmetry used in this paper implies that retail or

wholesale prices may change by a greater or lesser extent than wholesale or crude prices, during any given time interval. For example, suppose a ten p e r cent change in the wholesale price of gasoline is associated with a five per cent change in the retail price of gasoline within the first month following the change in the wholesale price. Symmetry merely requires that this relationship hold, regardless of

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whether the change in the wholesale price is positive or negative. Accordingly, a symmetrical relationship between wholesale and retail prices, or between crude and wholesale prices, does not imply that the retail-wholesale or the wholesale- crude margins are constant.

The change in the “refiner index margin”, AIM (the difference between the volume-weighted product price and the refiner acquisition cost of crude oil), is related to the change in the crude price by the following relationship:

where E is a random error term.37 Equation (8) can be rewritten as:

Since IM, = PW, - PC,, expanding equation (9) gives us:

Rearranging equation (10) results in:

or simply:

Thus:

The estimated coefficients for the margin relationship expressed in equation (8) can be derived using the results of estimating equation ( 5 ) and the relationship between the a and the p coefficients. Using expression (13). if al = 0.91. then p = -0.09. The magnitude of the p coefficient indicates that, if the price of crude increases by a dollar, the refiner index margin decreases by nine cents in the first month. Symmetry merely requires the refiner index margin to increase by nine cents (in the first month) if the price of crude decreases by a dollar.

The allegation that refiners are ‘profiteering’ - as evidenced by increasing margins - has been one suggested effect of asymmetrical price a d j ~ s t m e n t . ~ ~ However, as explained above, price symmetry can occur simultaneously with chang- ing refiner margins. To determine whether there is any evidence of excessive

150 OPEC Review

Figure 3 Real refiner gasoline and index margins

1986-92

30

2 5

20

IS

10

5

cents/gallon

*Gasoline +Index

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I F M A M J I A S O N D I F U A M I I A S O N D I F M A M l J ~ S O N D l F M A ~ l l A S O N O l F M A M J l ~ S O N D l F M A M l l A S O N D l F M A M Bb I 81 I BI I I V I 0 I 01 I 92

Source: Petroleum Marketing Monthly, Petroleum Supply Monthly.

profiteering, the refiner index margin is compared with the gasoline margin for the period before and after the outbreak of the 1990 Gulf conflict.

Figure 3 compares the index margin with the gasoline margin for the years 1986 through 1992. As the figure shows, both margins have fluctuated consider- ably. However, there has been little net change in the two margins over the period. Moreover, no time trends were detected in the two margins.39 The gasoline margin has consistently been greater than the index margin. This difference, however, has not been stable.

Table 4 shows some statistical properties of the index margin and key com- ponents in computing it. The index margin varies less than the gasoline margin. The coefficient of variation, which measures variability relative to the mean value of a series, is about 15 per cent greater for the gasoline margin than for the index margin. The refiner acquisition cost of crude oil also shows much more variation than the resale price of gasoline.

Figure 4 depicts the same two margin series from January 1990 through July 1992. It is apparent from this figure that the refiner index margins fell after the events of 2 August - from 17 cents per gallon in July 1990 to 15 $/g in October 1990, while they rose earlier in the year - from 17 $/g in January to 19 g/g in June.

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Table 4 Statistics on reala refiner prices and margins

1986-92 flgallon

,

Standard Coefficient Mean deviation of variation

Resale gasoline price 5 1.72 6.02 0.12 Acquisition cost of 34.09 6.13 0.18

Gasoline margin 17.63 2.78 0.16 Index margin 13.74 1.94 0.14

crude oil

Figure 4 Real refiner gasoline and index margins

1990-92

The margin averaged 15glg for August through December in 1990, which is in line with the previous year averages of 14 q/g in 1989 and 15 $/g in 1988. Thus, both the direction of change before and after the outbreak of the Gulf conflict and the magnitude of the margin were not unusual compared with earlier years.

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5. Conclusion The empirical evidence presented in this paper strongly supports the hypoth-

esis that the adjustment of the volume-weighted product prices to changes in crude oil prices is symmetric. Similarly, there is conclusive evidence that wholesale gasoline prices adjust symmetrically to changes in crude prices. If any asymmetry exists, it is in the opposite direction to conventional wisdom: wholesale gasoline prices fall faster than they rise.

For retail gasoline prices, the findings are mixed. A review of the relevant literature indicates a lack of consensus on the speed of gasoline price adjustment. Some studies find that retail gasoline prices rise as fast as they fall to changes in wholesale and crude prices, while others find that they adjust asymmetrically. A detailed comparison of the studies, however, reveals that the contradictory results are largely the result of differences in the data used. Although some have produced evidence that retail gasoline prices adjust asymmetrically to changes in wholesale prices, overall the hypothesis that retail price adjustment is symmetrical cannot be rejected by the data, particularly over longer periods.

References

Bacon, R., Rockets and Feathers: The Asymmetric Speed of Adjustment of UK Retail Gasoline Prices to Cost Changes, Ojord Institute for Energy Studies, April 1990.

Bacon, R.. Chadwick, J. Dargay. D. Long and R. Mabro, Demand, Prices and the Re- fining Industry: a Case Study of the European Oil Products Market, Ogord University Press, 1990.

Borenstein, S., A.C. Cameron and R. Gilbert, “Asymmetric Gasoline Responses to Crude Price Changes.” University of California at Davis Working Paper, I1 March 1992.

Butz. D.A., “Intertemporal Resource Allocation: Distributive Issues Surrounding Gasoline Price Hikes.’’ Economic Inquiry. X X I X , July 1991, pp . 591-600.

Dougher, R., The Cost of Motor Gasoline to Consumers: 1991 Annual Review, API Working Paper, February 1992.

French, M., Asymmetry in Gasoline Price Changes, Drafi Working Paper, Board of Governors of the Federal Reserve System, August 1991.

Karrenbrock, J.D., “The Behaviour of Retail Gasoline Prices: Symmetric or Not?,” Federal Reserve Bank of St Louis Review, JulylAugust 1991, pp. 19-29.

Kmenta, J., Elements of Econometrics, Macmillan, 1986.

Norman, D., and D. Shin, “Price Adjustment in Gasoline and Heating Oil Markets,” API Research Paper #060. August 1991.

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Williams, J.C., and B.D. Wright, Storage and Commodity Markets. Cambridge Uni- versity Press, 1991.

Wolfram R.. “Positivistic Measures of Aggregate Supply Elasticities: Some New Ap- proaches - Some Critical Notes.” American Journal of Agricultural Economics, May 1971, pp. 356359.

Footnotes

1.

2.

3 .

4 .

5.

6.

7 .

8.

9.

When two or more outputs are technically interdependent, joint production is said to exist. Refinery production of gasoline, heating oil, jet fuel, kerosene, etc, is a classic example of joint production. Although the profit-maximizing conditions for joint production are more complex than for the single output case, the basic idea of determining price by equating marginal cost with marginal revenue remains the same.

Refiners sell gasoline to a variety of different groups. They sell gasoline directly to “end users” such as utilities and large trucking firms. They also sell directly to retail gasoline oullers. The bulk of refiner sales, however. are sales to resellers (dealers and marketeers). About 75-80 per cent (or so) of refiner sales are sales to resellers, while 15-20 per cent of refiner sales are sales to motorists through refiner-operated sta- tions. Sales to large end-users comprise only a small percentage of total sales. Gaso- line sales made by refiners to resellers are referred to as “sales for resale” in Depart- ment of Energy publications.

The difference in the input and output prices at various levels of the distribution chain are referred to as “margins.” The difference between the refiner acquisition cost of crude and the wholesale price (the sales for resale price) is referred to as the refiner margin, while the difference between the wholesale price and the retail price is referred to as the retail margin.

See Borenstein, Cameron, and Gilbert /1992]. Although Borenstein et a1 propose several plausible theoretical explanations for the price transmission process, they could not link the results of their empirical analysis to one specific theoretical model.

See Williams and Wright [1991].

Bacon [ I 9901.

Norman and Shin [1991].

For example, the t-statistic on the quadratic adjustment term for the retail-wholesale model was -0.71 with 440 degrees offreedom.

See Karrenbrock [I9911 and Wolfram [1971].

10. French [1991]. FrenchS model specification differsfrom Karrenbrock’s in that French has an “error correction” term representing the tendency of retail mark-ups to revert to a long-term trend path. His results, however, differ in no significant way from Kar- renbrock’s result.

154 OPEC Review

11. The specific results are not reported here, because many of the lagged variables in their regression model were not statistically different from zero. This topic will be discussed in detail below.

12. The difference in the futures price and the spot price can vary, depending on the time period selected. For example. the difference in the five-day average of the NYMEX oil futures price and the Wl (Cushing) spot price varied from a low of one cent to 78 cents per barrel in the first two weeks of September 1990. Expanding their sample to include a period in which this variability is higher could cause a greater divergence between estimates based on the futures price, as opposed to the spot price.

13. See footnote 16 in BCGS study.

14. The t-statistic for the coefficient of the quadratic term was I .03.

IS. The sample period used by Bacon was June 1982 through January 1990, a seven-and- a-half-year period. The study by Karrenbrock used a sample period from January 1983 through December 1990, a seven-year period.

16. French suggests that price asymmetry is present whether price changes are large or small. See footnote I5 in French [1991). The ability to pass along large versus small price changes will also affect the relationship between the speed of price adjustment and the size of the price change.

17. Week 9 is statistically significant. This result. however. may be an anomaly, since week 4 through 8 are not statistically significant.

18. Bacon also examined the relationship between retail and wholesale prices, arguing the same point.

19. The BCG study addresses this issue by analy:ing the relationship between retail gasoline prices and terminal gasoline prices. Here, they find asymmetry also.

20. To facilitate the comparison, the weekly prices used in the NS study were converted to monthly prices using a simple averaging procedure. The use of monthly data by Kar- renbrock and French is problematic in that. i f retail price adjustments occur in a mat- ter of weeks ( i f not days), as alleged by the media and the public. then monthly observations will not pick up these responses. This is the major motivation for using weekly data.

21. The OGJ retail gasoline prices are estimates rather than survey prices, It was used because it was the only publicly available weekly retail price series (the estimation technique is not a simple linear transformation of wholesale prices into retail prices, since asymmetry between wholesale and retail prices was detected for shorter time periods). The Lundberg series, although widely reported in the press, is not available to the general public. Further, there are problem with the Lundberg series. For example, the Lundberg prices for selected US cities (42 altogether) are collected semimonthly on either the first and third or second and fourth Friday of each month. This irregular sampling period means that, in order to make the Lundberg prices correspond to other weekly data, an adjustment needs to be made. Since the model to be estimated includes lagged variables, this type of adjustment will affect the estimates of the lagged variables. Hence the symmetry results will be affected also. The Lundberg prices have the additional problem of the sample of cities in the first survey of each month being dq- ferentfrom the second survey.

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22.

23.

24.

25.

26.

2 7.

28.

29.

30.

31.

32.

See Dougher (19911. API estimates of gasoline taxes come from a variety of sources, including the Bureau of Labour Statistics, the Federal Highway Administration and various State Motor Fuel Tar Guides.

Karrenbrock acknowledges this possibility.

Testing for theflow of causality assumed in equation (21 is difficult. Since the two price-series move in tandem over time, the standard (Granger) test of determining whether lagged changes in crude prices are correlated to changes in current wholesale prices cannot be used.

In order to prevent seasonal demand factors from affecting the supply relationship, the crude oil, gasoline and distillate prices are seasonally adjusted using a 12-month averaging procedure for nine years (1982-91) of data. The technique used is a simplified version of the Census I1 method that the US government uses to adjust its economic data. Crude oil prices are seasonally adjusted, because there is a systematic tendency for crude prices to be lower in the second quarter of each year following the low demand winter months. Jet fuel and residual fuel oil prices are not seasonally adjusted because they do nor display any seasonal tendencies. Monthly, rather than weekly, prices are used because some series are only available at monthly intervals.

This percentage has been relatively stable in recent years. However, to the extent that the omitted products create a greater share of value added, the effects of omitting these products will be larger.

A Cochrane-Orcutt adjustment for a first order autoregressive process was used to correct for serial correlation in this and other linear models estimated in this paper. A unit root test indicates that the null hypotheses, that most of the variables used in the estimations are random walks, can be rejected. The exception is the wholesale price of gasoline. The computed Dickey-Fuller statistic of 4.14 is less than the critical value of 6.49 (for 100 degrees of freedom) at a 95 per cent confidence level; thus, the null hypothesis cannot be rejected. However, we proceed with the estimation, since a failure to reject (at a high significance level) is only weak evidence in favour of the random walk hypothesis.

Although data going back to 1982 are available, the reason for selecting 1986 as the starting point is to avoid the structural change (many small refineries went out of business) that occurred in the refining industry in the early 1980s. The statistical results. using all nine years in the estimation, were not as strong as using the smaller 198692 sample.

Initially, a trend variable and a variable representing lagged changes in crude prices were included in the estimation, but the terms turned out to be insignificant.

Bacon (19901 found the same contemporaneous link for European refineries. Simi- larly, French [I9911 found that monthly lagged crude prices were not statistically significant in explaining the variations in wholesale gasoline prices.

The sign of the coeficient on the APC variable is positive, because the variable is restricted to negative values.

We included a trend variable and a variable representing lagged changes in crude prices, but the terms turned out to be insignificant.

156 OPEC Review

33. The sign of the coefficient on the PC variable is positive, because the variable is restricted to negative values.

34. This is consistent with thefindings of Borenstein et al,

35. Following Kmenta [ I 9861, the null hypothesis for the F test is that ax is equal to a2. Using equation ( 5 ) as the one-parameter restricted model and equation (6) as the un- restricted model with 74 degrees offreedom, an F value of 0.57 is computed. This F value is less than the critical F value (-I .84) at a 99 per cent significance level. Thus, we cannot reject the null hypothesis.

36. A detailed explanation of this model is given in Norman and Shin [1991].

37. As in equation (6), the change in crude prices in equation (7) can be partitioned into price increases and price decreases. For the sake of simplicity, however, this adjustment is not made.

38. The finding that the monthly variation in the change in crude prices explains 84 per cent of the monthly variation in the change in the volume-weighted wholesale product prices suggests that the crude oi l price is the major cost component for the (short-run) period under analysis. Therefore, changes in other refiner variable costs, such as capital costs, which hefp to determine refiner profits and refiner behaviour in the long run. can be ignored or assumed to be constant.

39. A trend variable was not statistically significant in explaining the change in the refiner index margin over time.

40. The margins are adjusted for inflation using the all urban consumer price index available in various issues of the Survey of Current Business. The base years are 1982 through 1984.

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do product prices respond symmetrically to changes in crude prices?

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