Crude Oil Price Elasticity: A Clearer Chart for Gulf Export Shocks

The most useful way to read estimates of an oil supply shock is to put the two quantities that matter most on the chart’s main axes: Gulf exports and the implied change in crude prices. That simple design choice makes a familiar elasticity calculation easier to interpret than a three-dimensional surface, while keeping uncertainty about demand response visible.

The chart in question has circulated in analysis of the U.S.–Iran war and its effects on oil markets. Robin J. Brooks of the Brookings Institution has used it to illustrate possible price impacts, drawing on a graphic attributed to Haver Analytics. The original graphic is difficult to locate, but its central premise is straightforward: crude oil demand responds to price, even over the short run.

That response is captured by price elasticity of demand. Estimates vary, but short-term values of roughly -0.1 to -0.2 are plausible. In the conventional explanation, an elasticity of -0.1 means that a 1% price rise is associated with a 0.1% reduction in quantity demanded. For a supply shock, the relationship can be read in reverse: a 0.1% shortfall in available supply implies about a 1% price increase, assuming demand elasticity remains constant.

What a supply cut implies

At the peak of the disruption described in the analysis, roughly 10% of normal global oil supply was removed from the market. As much as 20% of world supply was exposed to risk, though the author does not believe the actual reduction reached that level. The disruption had eased by the time the post was written, with oil flowing at a higher rate than several months earlier; future export levels remained uncertain.

Under the linear approximation, a 10% supply reduction points to a price increase near 100% when demand elasticity is -0.1, or about 50% when it is -0.2. Those figures aren’t forecasts. They show how strongly the result depends on the elasticity assumption, and they broadly match the range represented in the Haver graphic.

There are important limits to this exercise. The calculation applies a linear elasticity relationship to a large shock, even though a 10% or 20% supply change may stretch that approximation. It also holds the demand curve fixed. Over time, income, policy, consumer preferences and technology can shift demand, including through faster adoption of clean electricity. The model sets those changes aside.

Inventories are outside the calculation as well. Drawing down stored oil can cushion the effect of lower exports, and did so during the disruption. The exercise is best understood as a way to compare assumptions and illustrate a plausible range, not to predict a market price.

Why the 3D view gets in the way

The original chart tries to show how two inputs—oil exported from the Gulf and the price elasticity of demand—shape a third value, the resulting crude price increase. Elasticity is uncertain, with 0.1 to 0.2 used here as the range of absolute values. The negative sign is omitted in the charts for readability.

A three-dimensional perspective plot encodes the price change as height. That can display the full grid of values, but readers have to judge the surface against three axes at once. It’s hard to trace a point to its value, and harder still to see the general relationship between exports and prices. Adding colour to the surface helps only modestly.

A heatmap makes the values easier to look up. Exports and elasticity sit on the horizontal and vertical axes, while colour represents the estimated price increase. Labels inside the tiles make the graphic nearly a table, which is useful for precise comparisons but less effective at showing the pattern as a relationship.

Put exports and prices on the axes

The line chart makes a more consequential choice: it places Gulf exports on the x-axis and the estimated price rise on the y-axis. Each line represents a different demand elasticity assumption. This gives readers a direct view of how the modeled price effect changes as exports recover or fall, while the lines show how sensitive that effect is to the assumed elasticity.

The chart uses colour to indicate distance from the midpoint of the assumed elasticity range, rather than simply sorting values from low to high. The midpoint case is also marked with a solid line, while the others are dashed. Together, those cues frame elasticity as uncertainty around a central assumption.

A secondary y-axis converts the percentage increase into an implied price per barrel, using a pre-war crude price of about US$72.50. That helps connect the model’s percentage changes to prices readers may encounter in market coverage. The chart also marks the pre-war export level, based on the assumption that Gulf exports were 20 million barrels per day before the disruption.

For current conditions, the author suggests testing export levels between 7 million and 15 million barrels per day. Reading up from an assumed export figure to the range of elasticity lines gives an implied price increase. Comparing that result with the observed price can also indicate whether other factors may be affecting the market, such as inventories, perceived risk, or expected changes in supply and demand.

On the assumptions used, the chart suggests that prices may include some additional premium beyond the supply-and-elasticity calculation. The author’s interpretation is that markets may be pricing in the possibility that exports will fall quickly from their then-current levels. That is a reading of the chart, not proof of why prices stand where they do.

A chart that makes assumptions inspectable

The analysis was built in R using simulated values. It assumes global oil consumption of 100 million barrels per day, pre-war Gulf exports of 20 million barrels per day, and a pre-war price of US$72.50 per barrel. The model varies exports from 2 to 20 million barrels per day and elasticity from 0.1 to 0.2, then calculates the percentage price rise as the lost share of global oil supply divided by elasticity.

That setup is simple enough to inspect, which is part of the graphic’s value. Readers can see that the result depends on a handful of assumptions, and can trace how changing exports or elasticity changes the modeled outcome. The line format puts the main relationship in view; the model’s limits remain visible alongside it.

For analysts, the practical lesson is about chart design as much as oil markets. When the question concerns how one variable moves with another under uncertain assumptions, put those variables on the axes and encode the uncertainty in separate series. That makes the model easier to question, update and compare with conditions in the market.