The Price of Knowing

Two numbers, two kinds of knowledge

Begin with the diesel price itself.

EIA's weekly retail price is a statistical measurement. Its current methodology uses a stratified sample drawn from a frame of roughly 73,000 service stations and 9,500 truck stops. Prices are collected as of 8 a.m. each Monday. EIA says responding outlets represent at least 80% of weighted national diesel sales; missing observations may be imputed from an outlet's previous response, comparable outlets and commercial information. It publishes its methodology and measures of sampling error.¹

It is difficult for an administration simply to wish such a number away. Private information provides an external check. GasBuddy's measure is not directly comparable because it is not the same sample, collected at the same moment and weighted in the same way. Even so, its crossing $6 on September 10 is consistent with the direction and scale of EIA's Monday observation.²

A forecast is a different kind of number.

EIA's September outlook expects diesel to average $5.55 in the fourth quarter of this year and then decline to $4.40 during 2027. That projection depends on an elaborate view of the future petroleum system. EIA expects Middle Eastern oil production to increase as more petroleum moves through the Strait of Hormuz and alternative export routes expand. It assumes regional crude production will move toward pre-conflict levels by the second quarter of 2027. With production recovering and inventories eventually rebuilding, it expects Brent crude to average about $74 next year.³

Those assumptions are plausible and may turn out to be correct. They remain assumptions rather than observations.

EIA is fairly explicit about their importance. Its petroleum-products analysis says the projected decline in diesel refining margins assumes a return toward normal tanker traffic and greater availability of Middle Eastern crude. If constraints persist beyond the end of 2026, EIA says global distillate crack spreads would be higher than forecast.³

For a reader using the forecast, that condition may be more useful than the $4.40 headline.

Current market conditions are not especially reassuring. EIA expects American distillate inventories, including diesel and related fuels, to fall below 100 million barrels and remain below their recent five-year range through much of 2027. Private-market data cited by Reuters on September 10 put American diesel inventories about 13% below their five-year average and the diesel crack spread at a record $112.17 a barrel.²

None of this proves EIA wrong. It shows how quickly a forecast can age.

The six-day-old future

The September outlook was published on September 9. EIA states prominently that it finalized the inputs to its model on September 3 and that the forecast does not specifically account for events after that date.³

Every serious forecasting exercise requires a cutoff. Data must eventually stop entering the machine so that models can run, analysts can review them, tables can be produced and publications can be prepared. The existence of an input date is not suspicious.

The question is how an agency should respond when reality changes materially between the cutoff and publication, or immediately afterwards.

The answer depends on rules and judgment: what counts as sufficiently material, who decides, and whether the agency has stated its criteria publicly.

The energy system is being buffeted by an active Middle Eastern war, disrupted Gulf transportation, attacks on Russian refining capacity, low American distillate inventories and historically high diesel refining margins. EIA itself substantially revised the outlook between August and September: its forecast for 2027 diesel rose from $4.07 to $4.40, an 8.1% increase in a single month.³

The agency plainly did not ignore adverse information. It raised its forecast, disclosed the model cutoff and described the risks to its baseline. When it discovered that the initial release had mistakenly displayed gasoline crack-spread figures as distillate figures in one table, it issued a correction the following day.⁴ These facts cut against the simplest theory of political manipulation, although they leave open whether the baseline, update rules and presentation of uncertainty are adequate to the environment in which EIA now operates.

The politics of the baseline

Every model needs a central case, but the word “baseline” sounds more objective than the judgments beneath it.

Consider a model attempting to forecast diesel prices during a war. Analysts must make judgments about when damaged infrastructure returns, when tankers resume normal routes, how quickly producers restore output, whether attacks expand or diminish, how governments respond, whether inventories are released, whether demand weakens and how refiners change their product mix.

Those judgments need not be partisan to determine the answer.

A baseline in which Gulf production normalizes relatively quickly will produce one price path. A baseline in which disruption persists for another year will produce another. Either model can be internally coherent and the work of respectable economists. The consequential choice is which one becomes the forecast and which is left to the risk discussion.

Political economy can therefore enter the forecasting room even when politicians do not.

The executive branch has an obvious interest in lower energy prices. Every administration does. Fuel prices are visible, frequently paid and politically salient. In 2026 they are particularly awkward because the supply shock is connected in part to a conflict in which the United States is directly involved. The White House has separately invoked the Defense Production Act to expand domestic petroleum production, refining and logistics capacity, describing current constraints as a threat to prosperity and national security.⁵

Nothing here shows that EIA analysts altered their model to help the White House. The narrower point is that a reassuring baseline has unusual political value.

An institution designed to produce independent knowledge should be strongest precisely when the government surrounding it has the greatest reason to prefer a particular answer.

Independence can be starved

Congress anticipated the problem of political influence when it created EIA. The agency is not structurally independent in the manner of the Federal Reserve: it sits inside the Department of Energy, and its Administrator is appointed by the President with the advice and consent of the Senate. But it enjoys unusually explicit statutory protection.

Under 42 U.S.C. § 7135(d), the Administrator is not required to obtain another DOE official's approval when collecting or analyzing information, and does not have to secure another federal officer's approval before publishing the substance of statistical or forecasting technical reports.⁶

This architecture reflects a broader principle of official statistics. The National Academies' 2025 Principles and Practices for a Federal Statistical Agency says federal statistical bodies must be independent of political and other undue external influence when developing, producing and disseminating statistics. It identifies control over content, methods, frequency and release, without prior political clearance, as necessary protections.⁷

These protections exist to preserve credibility.

An oil trader can observe futures prices. A household can see what it pays at the pump. But hardly any individual citizen, business or investor can reproduce the government's statistical machinery. Official statistics therefore depend on trust. Once users apply a partisan discount to every official number, even accurate statistics become less valuable.

Statutory protection means little without enough qualified staff.

DOE's own budget documents show 353 federal EIA positions in fiscal 2025 and 246 in fiscal 2026. That is a decline of 107 posts, or about 30%. The department's fiscal 2027 request keeps the agency at 246 full-time-equivalent positions.⁸

Staff reductions do not prove political capture. Governments set budgets, and an agency can sometimes become more efficient. But there is no serious conception of statistical independence in which human capacity is irrelevant.

The National Academies treats the recruitment and retention of qualified professional staff, methodological expertise, quality assurance and continued technical improvement as parts of statistical integrity.⁷ A legislature can grant an agency formal authority to decide its methodology and still leave that authority increasingly theoretical if the agency lacks enough experienced people to maintain surveys, improve models, test alternative assumptions and respond quickly when the world changes.

An institution can therefore lose integrity even when no censor ever arrives from the White House.

Capture without conspiracy

Economists normally associate “capture” with regulation: the political process through which concentrated interests bend public power toward themselves. A statistical institution presents a different problem. It does not principally tell companies what they may do. It tells society what is happening.

Its valuable asset is epistemic authority: the public's willingness to trust its account of reality.

The relevant danger might therefore be called epistemic capture: a process in which the machinery that determines how reality is measured, modeled and communicated becomes aligned with a narrower set of incentives or assumptions. Recent scholarship uses the term to describe how institutions can privilege particular frameworks of expertise until those frameworks constrain what is considered knowable or reasonable.⁹

For a government forecasting agency, capture need not look like a political operative ordering analysts to lower next year's diesel price. It can take less theatrical forms: an alternative scenario is no longer produced because nobody has time to maintain it; a normalization assumption persists because changing it requires a lengthy review; a release schedule becomes more important than the freshness of its inputs; politically awkward forecasts attract more internal scrutiny than convenient ones; technical caveats survive in the report but vanish from its public meaning.

Any one of those decisions may be defensible. In combination, they can change the epistemic character of an institution. Unlike ordinary falsification, which creates a false number, epistemic corruption can produce a perfectly reproducible number through a process that has become less capable of discovering when its assumptions are wrong.

The dismissal of the BLS Commissioner provides a concrete precedent. On August 1, 2025, President Trump fired Erika McEntarfer immediately after a disappointing employment report and large downward revisions to earlier estimates. He accused her of manipulating the figures to damage him politically, but presented no evidence. Economists and former officials warned that the dismissal could damage confidence in American statistics.¹⁰

The episode matters even if no EIA employee ever received a political instruction. Analysts notice what happens at neighboring agencies. If senior statistical officials can lose their jobs immediately after releasing unwelcome information, career employees elsewhere do not need an explicit threat to understand that political consequences exist.

It does not establish that EIA changed its work. It does explain why statistical independence must protect analysts from having to consider whether an inconvenient conclusion will carry personal consequences.

The peculiar economics of diesel

Why devote so much attention to a fuel forecast? Because diesel is unusually close to being an economy-wide input.

America's trucks moved 65% of domestic freight by weight and 72% by value in 2023, according to the Federal Highway Administration.¹¹ The American Transportation Research Institute calculates that the average cost of operating a truck reached a record $2.336 per mile in 2025. Excluding fuel, it was $1.854, implying roughly 48 cents per mile of fuel cost at last year's prices.¹²

The 2025 national diesel average was $3.660. The latest EIA reading of $5.967 is about 63% higher. It is nearly 20% above even 2022's full-year average of $4.989.¹³

A rough calculation shows the scale. If a carrier's fuel cost per mile simply increased in proportion to the national pump price, 48 cents would become roughly 79 cents. Holding everything else constant, total operating cost would move from about $2.34 to roughly $2.64 a mile, an increase of around 13%.

Actual fleet economics are more complicated. Carriers negotiate discounts, impose fuel surcharges, change routes and improve utilization, but most of the cost ultimately reaches a shipper, farmer, builder, retailer or consumer.

Modern research generally finds that oil shocks have a more modest effect on underlying inflation than the experience of the 1970s might suggest. A Federal Reserve model of the 2022 oil shock estimated that the rise in prices added almost one percentage point to headline inflation on impact, while the effects on core inflation and output were much smaller.¹⁴

History still counsels against treating energy shocks as trivial. James Hamilton's work on the 2007–08 oil-price surge concluded that it materially reduced consumption and vehicle purchases and contributed to the initial recessionary dynamics, even though the financial crisis ultimately dominated the downturn.¹⁵

Diesel also faces its own supply constraints. Expensive crude is not passing cleanly through an otherwise normal refining system: distillate inventories are low, global refinery production has been disrupted and diesel crack spreads have become extreme.²³

Whether those conditions disappear in six months or eighteen has economic and political consequences, which is why the institution making the forecast deserves unusual scrutiny.

A model is not a promise

Public discussion often strips forecasts of their conditions. An agency publishes a central estimate, a press release reduces it to a sentence, and politicians and journalists repeat the sentence as a factual claim. In the process, an assumption becomes an expectation and then a promise. Thus:

EIA forecasts diesel at $4.40 in 2027.

quietly becomes:

Diesel will fall to $4.40 next year.

The distinction matters. The first says that, given EIA's current assumptions about production, transportation, refining, demand and geopolitical conditions, the model's central annual estimate is $4.40. The second presents that conditional result as settled knowledge.

EIA already discloses its assumptions, and the September report deserves credit for doing so. During an extraordinary geopolitical period, however, those assumptions should be harder to miss.

EIA could pair its baseline with two explicit alternatives: a faster-normalization scenario and a persistent-disruption scenario. Instead of leaving geopolitical uncertainty in prose, the report could show how $4.40 changes if Gulf flows do not substantially normalize by the second quarter of 2027. Headline forecasts could carry a conspicuous “information as of” date, along with public criteria for rerunning a forecast after the normal input cutoff.

A retrospective table could attribute forecast errors to demand, production, geopolitical assumptions, refinery outages or model misspecification. During an extraordinary shock, EIA could also issue an interim scenario update without pretending to have produced an entirely new central forecast. None of this would make the agency omniscient; it would make the uncertainty easier to see.

Has EIA been captured?

The public evidence does not support that conclusion.

Several facts weigh against it. EIA has strong statutory protections and continues to publish politically inconvenient information: record fuel prices, depleted inventories and severe Middle Eastern supply constraints. It raised rather than reduced its diesel forecast this month. It also disclosed the September 3 input freeze, warned that longer-lasting Gulf disruption would raise diesel margins beyond its baseline, and corrected an error one day after publication.³⁴⁶

That record is inconsistent with crude propaganda.

Scrutiny is warranted nonetheless. EIA exists inside an executive department whose political leadership is appointed by the President. Its Administrator is a presidential appointee. The federal statistical system has already experienced an extraordinary political intervention in the firing of the BLS Commissioner after an unfavorable report. EIA has undergone a large reduction in federal staffing. Its most politically salient current forecast also depends on a geopolitical normalization assumption concerning a conflict in which the government that houses the agency is itself a participant.

Taken together, those facts make the integrity of the process unusually important; they do not prove corruption. Verification is the proper response, not credulity or conspiracy.

Statistical agencies earn a credibility premium. Markets can act on an EIA inventory report because traders generally assume the government has not constructed it to help the President. Businesses can write contracts around BLS inflation statistics because both sides accept the measurement infrastructure. The Federal Reserve can cite government employment data because investors believe the numbers were generated before anyone knew whether they would flatter the incumbent party.

Once users lose that assumption, the price of information rises. Every release requires political interpretation and every revision becomes suspicious. Private alternatives proliferate, but not evenly: large companies and hedge funds can buy proprietary freight, tanker, satellite and credit-card data, while ordinary businesses and citizens remain dependent on public statistics.

Weakening official statistics does not eliminate information. It makes good information expensive.

The relevant standard cannot be limited to the question: Did anyone falsify the number? That is the lowest imaginable bar.

A statistical agency worthy of a sophisticated economy must answer harder questions. Did we use the best information reasonably available? Did we update when circumstances materially changed? Did we stress-test the assumptions most likely to fail? Did professional staff have adequate resources? Could analysts reach an inconvenient conclusion without worrying about political consequences? Did our public communication convey the conditionality of the model rather than merely its most reassuring number?

And would we have made the same methodological choices if the resulting forecast were politically inconvenient to a different administration?

They concern both process and power.

Reality eventually marks the model to market

Energy forecasters have one advantage over many political institutions: petroleum markets are unforgiving.

If diesel remains near $6 next year, no press release will make it $4.40. If tanker traffic remains constrained, inventories will reveal it. If refineries cannot obtain crude, crack spreads will reveal it. If freight costs rise, trucking accounts will reveal it.

Reality will eventually mark the forecast to market.

The danger lies in what happens before then. Governments make decisions using forecasts, the Federal Reserve forms expectations, businesses set budgets and voters evaluate policy. An optimistic baseline can shape behavior while its accuracy remains unknowable.

The integrity of a forecasting institution therefore matters before its forecast is proved right or wrong.

In an authoritarian state, the most obvious statistical pathology is fabrication: the ruler dislikes the number, so the number changes. A modern bureaucracy allows subtler failures. The models may remain technically competent, the analysts conscientious and each procedure defensible in isolation, even as the overall process becomes less reliable.

Yet a government can still drift toward a world in which the official production of knowledge becomes systematically less capable of contradicting the assumptions on which the government would prefer to operate.

This is institutional capture in its quieter form: an immaculate spreadsheet produced by an institution that is gradually losing its ability to challenge the assumptions around it. The first obligation remains reality, not the model, the release calendar, the department or the administration.