Update 0114 July 2026 — The June FOMC held rates and turned hawkish, validating the study’s Fed call. The oil path has not resolved. The central finding is unaffected.Read the update ↓
Assumption set
Household Economics · Forecast · June 2026
The Uneven Month
A war-driven oil shock is pushing U.S. inflation higher through late 2026. The national total is one number. What it does to a single mother, a young renter, or a paid-off retiree is a different number entirely.
Scenario-weighted modelSources: BLS · Census · IRS · Federal ReserveInteractive — adjust the scenario below
The short version
A 2026 war in the Persian Gulf has pushed up oil — and with it fuel, food, and shipping — through the year. This report estimates what that costs U.S. households by December, and who feels it most.
The most likely outcome is a status-quo rise of about $23 billion a month by December; the average across scenarios is $25 billion, and a full Monte Carlo puts the 90% range at roughly $4–49 billion — with most of that spread driven by how the war plays out, not by measurement (you set the odds with the sliders). A tougher “coupled” scenario, where a Gulf shock cascades across energy, shipping, food and credit at once, keeps that most-likely figure but fattens the bad tail — a severe-crisis December could top $70 billion, at low but non-trivial odds. The biggest dollar totals come from higher-income households, because they spend more. But the real squeeze lands at the bottom: the lowest-income households lose about 9% of their take-home pay, versus under 2% at the top. Most dollars at the top, most pain at the bottom — and that holds however you set the scenarios.
How to read this. (1) The “expected” figures are a weighted blend of three scenarios — ceasefire, stalemate, escalation — that you control with the sliders; they are not a forecast. (2) A figure “per month” is a December run-rate (the monthly cost once the price rise has fully built up), not the year’s total — the cumulative June–December total is about $110B. (3) “Burden” means the extra money needed to buy the same things as before: a real budget hit, and an upper bound, since people cut back.
Plain-language guide to the terms used here
Regressive — hits people with less money harder, as a share of income. Suits / Kakwani index — a single score (about −0.26) for how regressive the hit is; negative = lands hardest on those with least, roughly like a gasoline tax. Basket factor — how concentrated a household’s spending is in what spiked (food, fuel, utilities); higher for lower incomes. Run-rate vs. cumulative — run-rate = one month’s cost; cumulative = the total added across June–December. Marimekko (Figure 1) — width = number of households, height = cost each, so area = total dollars. Gini — a 0–1 score for how unequal incomes are. “Not-QE, QE” / Treasury liquidity — government borrowing choices that quietly pump money into markets, acting like stimulus. Liquidity-sensitive (crypto) — assets that move with how much money is flowing through markets, more than with oil itself. Floor vs. coupled tail (Figure 9) — the “floor” is the calmer estimate that treats each month as independent; the “coupled tail” allows a crisis where energy, shipping, food and credit go wrong together, which fattens the worst-case end. p95 / p99 — the 95th / 99th percentile: a bad outcome exceeded only about 1 month in 20, or 1 in 100. Copula / tail dependence — the statistical dial for how strongly different shocks move together specifically in the bad tail.
+2.9%
Expected cumulative price rise by Dec (probability-weighted)
$24B
Estimated added household cost per month, nationwide
Median AGI per tax return2 — pre-tax; ~$48.5k after tax
Income is not money in hand. Median household income ($83,730) counts a couple's combined earnings; median AGI per return ($50,400) is the pre-tax tax base. The budget views below use after-tax "available money" as the baseline.
Control · The scenario
Set the odds. Everything recomputes.
The shock's size hinges on the Strait of Hormuz. Even a ceasefire is slow relief: the Pentagon estimates up to six months to clear mines, and clearing can't begin until the war ends.8 Drag the probabilities — the model reweights and updates every chart, trajectory, and market estimate on the page.
Probability of each path (Jun–Dec 2026)
Subjective weights, not market-implied. Each path's cumulative price impact is fixed from its monthly inflation track.
slow relief · +1.1% by Dec
grinding on · +2.8% by Dec
oil re-spikes · +5.2% by Dec
+2.9%expected rise by Dec
$25Badded cost / month (Dec)
$110BJun–Dec cumulative
$296BDec run-rate ×12
Probabilities normalize to 100%. Default weights reflect early-June 2026 — active ceasefire talks alongside ongoing strikes and regional spillover.
How this is measured — run-rate vs. cumulative
A run-rate is the cost in a single month once the price rise has fully built up; ×12 gives a steady-state annual rate, not the realized 2026 total. Because the shock builds month over month, the actual June–December cumulative (~$110B in the consensus lens) is about 4.45× the December run-rate, not 7×.
Figure 1 · Where the dollars come from
The biggest bills, the biggest backs
In plain termsmost of the national bill comes from the top, because they spend the most — even though each high-income household pays a smaller slice of its own income.
Each block is an income bracket. Width is the number of households; height is the monthly hit per household; so the area is the total dollars that bracket adds to the national bill. The top dominates — biggest budgets, ~21 million households — even at the lowest rate.
Aggregate monthly burden by income bracket
$ billions / month · national
Area = households × per-household monthly hit. Hover any block.
Reading itthe $200k+ block is ~21M households × ~$291 each ≈ $6.1B a month — wide and tall, so it dominates the national total even at the lowest rate.
A national total leans toward the top of the distribution simply because that's where spending — and much of the population — sits. That is the opposite of who feels it most.356
Figure 2 · What the month actually looks like
After the essentials, what's left to give?
In plain termsafter essentials, housing, and debt, this is what’s left — and where the shock bites. For some groups it bites into nothing.
The honest view. Take after-tax money, subtract what doesn't flex — food and utilities, housing, debt — and what remains is the monthly breathing room. The dashed line is the shock. When it lands in thin or negative space, something gives.
Monthly take-home split into where it goes — each block shows its dollar amount. The dashed line is where fixed commitments end; the hatched red bite is the expected December shock eating into what's left. Hover any block.
Tenure totals are exact CE 2024 figures (owner-with-mortgage $104,329/yr, owner-free $70,906, renter $57,108);129 age and household-type budgets use Census median incomes7 with CE category shares,13 and the new under-25 bracket draws on the CE youth cross-tab;26 the young-adult living-situation view uses Pew/Census shares of those living with family (its budget split is estimated).27 Dollar splits within each bar are derived from those sources; treat the shape as solid, the cents as estimates.
How the basket factor works
Lower-income households spend a larger share of their budget on the things that spiked — food, fuel, utilities — so the same headline inflation rate hits them harder. The basket factor scales the price rise by that share (about 1.32 at the bottom vs 0.80 at the top), per the New York Fed’s analysis of the Consumer Expenditure Survey.
Figure 3 · Month by month to December
The slow build of the squeeze
In plain termsthe cost is not a one-time jump; it builds month by month to December.
The shock doesn't arrive all at once — it compounds from June to December. These trajectories track the cumulative monthly hit as it accrues. Both charts are wired to the sliders above: reweight the scenario and the curves bend in real time. The second chart follows whichever lens you pick in Figure 2.
Cumulative monthly hit by income bracket
$ / household / month
$/household/month of added cost, accruing Jun→Dec under the current scenario weighting.
Cumulative monthly hit by demographic group
$ / household / month
Follows the Figure 2 lens. Switch the tabs above to change the groups shown.
Both charts share one vertical scale, so the income and demographic views are directly comparable. Each line's December value is that group's per-household monthly hit at the expected rate — the same number drawn as bar height in Figure 1. The color dots in Figure 2 match these lines.
Figure 4 · The flip
Most dollars at the top, most pain at the bottom
In plain termshigher-income households lose more dollars, but lower-income households lose a bigger slice of what they have — that flip is the unfairness.
Rank brackets by raw dollars lost and the affluent lead. Rank by share of after-tax income and the order inverts — lower-income households spend a far larger slice on the exact things that spiked.
The crossing lines are the regressivity signature of an energy-and-food shock. Collapsed to one signed scalar, the Suits index is −0.26 on pre-tax income (0 = proportional, negative = regressive) — squarely in the band of a gasoline excise tax (−0.25 to −0.35) — and −0.22 on after-tax income; we lead with pre-tax for benchmark comparability and report both, since the difference is purely the income base.31 The burden concentration (0.19) and the ~5.5× bottom/top share ratio need no income-base choice. These indices are invariant to the scenario weights — they measure the shape of the shock, not its size — and because they are computed on grouped brackets they are conservative (the true values are modestly more negative). A parametric bootstrap over the modeled inputs (basket factors, incomes, tax rates, counts) puts the pre-tax Suits at −0.26 (90% CI −0.31 to −0.21) and finds the shock regressive in 100% of 100,000 replicates on both income bases.33
How the index is computed
The Suits index plots each bracket’s cumulative share of income against its cumulative share of the burden: 0 = the burden falls proportionally to income, negative = it falls harder on lower incomes, positive = on higher incomes. Kakwani is the burden’s concentration coefficient minus the income Gini. Both use pre-tax income for comparability with the public-finance benchmark (after-tax shown alongside), computed from the seven income brackets in the Incidence & Sensitivity sheet.
Figure 5 · The debt underneath
The bills were heavy before the shock
In plain termsmany households were already stretched before the shock, especially the lowest earners, whose card balances equal about 85% of a month’s income.
From the Federal Reserve's 2022 Survey of Consumer Finances: who holds which debt, and how heavy credit-card balances are relative to monthly income across the distribution.
Credit-card debt as a share of monthly income, by income decile
% of monthly income
2022 SCF, lowest-income decile to highest. Carry the broad gradient — ~85% of a month's income at the bottom, ~8% at the top — alongside the upper-middle bump noted below.
card balance ÷ monthly income5th–7th deciles — highest share carrying a balance (up to 61%)
The ratio generally falls as income rises — ~85% of a month's income at the bottom to ~8% at the top — but it is not monotonic: the 5th–7th deciles bump up (66 / 49 / 48%) because those upper-middle deciles have the highest share of households carrying a balance (57–61%, versus 28% at the bottom and 26% at the top). Verified against the corrected 2022 SCF analysis.4
What U.S. families owe — 2022 Survey of Consumer Finances
Share holding each debt type, and the median balance among those who hold it.110
Debt type
% holding
Median (holders)
Mean (holders)
Any debt
77.4%
$80,200
$163,800
Credit card
~45%
$2,700
$6,100
Student loans
22%
~$25,000
~$47,000
Mortgage (primary home)
42%
$156,400
—
Auto loan
~35%
—
—
Other installment (incl. BNPL)
18.5%
—
—
Student debt skews toward higher earners in dollars,5 but delinquency skews low: 27% of borrowers under $50k are behind versus 10% of those over $100k.11
Figure 6 · What gives first
The order of sacrifice
In plain termswhen money runs short, households pay bills in a fairly predictable order, and the lowest-priority ones slip first.
When the shock eats the breathing room, households rarely cut at random. Counseling guidance describes a consistent payment hierarchy, and cross-sectional delinquency data are consistent with it — lower-priority unsecured bills tend to fall behind first. This is a description of typical prioritization, not a prediction that this shock will cause a particular order; establishing that would need an event study around prior energy spikes.
1
Food & essential utilities
Survival, and utilities can be shut off within weeks. Paid first, almost always.
2
Housing — rent or mortgage
Eviction (renters, faster) or foreclosure (owners, slower) is the hardest setback to recover from.
3
Transportation — auto loan, insurance, fuel
Usually required to keep earning. Repossession threatens income itself.
4
Insurance & required taxes
Legal exposure and loss of coverage; real but less immediate.
5
Credit-card minimums
Protect the score and revolving access, but the first unsecured bill to slip when cash is short.
6
Student loans (especially federal)
Most deferrable — forbearance and income-driven plans — so the slowest severe consequences. Typically the first to go.
Renter-heavy and single-earner households reach this ladder fastest, because their breathing room was thinnest to begin with.
Figure 7 · The market read
What the shock does to portfolios
In plain termshow the shock might ripple into stocks, bonds, and crypto — illustrative directions, not predictions. Don’t trade on them.
An oil-driven inflation shock ripples beyond the kitchen table. The mainstream macro chain: higher inflation keeps the Fed higher-for-longer, which lifts bond yields (pressuring bond prices), squeezes growth equities, and leaves crypto ambiguous. These are illustrative directional sensitivities, scaled to the scenario you set above — not forecasts or advice.
Scenario-linked market sensitivity
% · bps
Weighted column updates live with the sliders. Values are illustrative scenario impacts, not price targets.
Metric
Ceasefire
Status quo
Escalation
Weighted
For market watchers — Treasury-driven liquidity overlay (illustrative)
Treasury-driven liquidity overlay
Howell’s “not-QE, QE”: the Treasury’s financing mix — T-bill share, its cash balance at the Fed (the TGA), and reverse-repo / bank reserves — now injects or drains money-market liquidity like stealth QE, independent of the oil shock.2829 Pick the impulse; the liquidity-sensitive lines re-price.
Treasury / Fed lever
Injecting
Draining
2026 reading
How the selected impulse re-prices the liquidity-sensitive lines from the table above (weighted column):
Liquidity-sensitive line
Oil-shock read
Treasury overlay
Net read
Not investment advice. These directional figures illustrate how an oil-inflation shock tends to transmit to markets; the relationships are contested and regime-dependent. The inflation and yield links are the firmest — oil shocks reliably lift headline CPI16 and tend to push Treasury yields up.17 The equity and crypto responses are regime-dependent, not reliable directional bets: historically the S&P 500 rose in six of seven oil-spike episodes since 1986 (averaging +24% over the following year), with supply-driven shocks like this one skewing more negative and recession the swing factor.18 A stagflationary oil shock can weigh on both stocks and bonds at once.14 Crypto is best read as a liquidity gauge rather than a simple risk asset: this conflict has been characterized as tightening global liquidity through the capital account,24 and with Bitcoin the most liquidity-sensitive major asset, a deepening shock reads as a near-term headwind — while the same refinancing wall and currency-debasement pressures could later flip liquidity, and crypto, to a tailwind.25 The U.S. is a net exporter of petroleum products but a net importer of crude oil, and pump and diesel prices are set globally — so households are not meaningfully shielded by “energy independence.”2023 Yields sat near 4.1–4.3% with the funds rate at 3.5–3.75% in mid-2026.15 Do not trade on this.
In plain termsthe Fed meets five more times before December; what it decides is the biggest wildcard the model cannot price.
The shock builds across the exact window in which the FOMC meets five more times in 2026. Each is a decision point; June, September, and December also publish projections (the “dot plot”). What the Fed does at these dates is the largest swing factor the model itself cannot price — and the two lenses imply different Fed reactions.
FOMC 2026 — June to December
★ meeting includes the Summary of Economic Projections. Schedule: Federal Reserve.
Figure 9 · The number is a distribution
From an envelope to a forecast
In plain termsthe most likely outcome is a status-quo ~$23B a month, and the wide range around it is about how the war goes, not measurement. The second line asks a harder question: if a Gulf shock sets off a chain reaction — energy, shipping, food and credit going wrong at once — the bad end gets fatter. A truly awful month (worse than 99 out of 100) rises from about $55B to $69B, and a month above $60B goes from a 1-in-1,000 rarity to roughly 1-in-50.
A 200,000-draw Monte Carlo replaces the corner range with a predictive distribution — reported as two bands. The floor calibrates volatility on history (independent month-to-month noise); the coupled tail adds what a war shock actually does — a crisis regime, fat tails, and a tail-dependent copula so energy, shipping, food and credit go wrong together, with the realist basket-broadening switched on harder in the tail. The gap between the two bands is the quantified “novel territory.”
Predictive distribution of the monthly aggregate burden
density · $B / month (Dec, national)
Median ~$23B in both bands — coupling does not move the center. The story is the upper tail: the 99th percentile rises from $55B (floor) to $69B (t-copula) to $75B (rotated-Clayton).
Two things the floor distribution already showed. The spread is structural, not statistical — 82% of it is which scenario realizes (geopolitics), only 18% is path noise, so a tighter forecast needs better information on the war, not finer measurement — and it is multimodal, with the mean sitting in a low-density valley between a status-quo mode (~$23B) and an escalation tail.32
What the coupling layer adds is all in the upper tail. The center holds, but the chance of a burden above $60B/month rises from 0.1% to 2.1% (~18× once a cascade is admitted) and the p99 gap is ~$14B.34 One finding worth your scrutiny: the copula’s tail-dependence parameter turns out second-order — a discrete crisis regime already supplies most of the joint-tail co-movement — so the honest levers to argue over are how likely and how severe a crisis is, and how far the basket broadens, not the copula family.
How the two bands are built — and the one nuance for the thesis
Floor: independent Gaussian monthly noise (σ=0.08 / 0.15pp/mo), no crisis regime, consensus basket — the future as a draw from the past, hence a lower bound. Coupled: a Student-t copula across the seven months (ρ=0.35, ν=4; tail-dependence λ=0.18) with fat-tailed Student-t marginals (ν=5), a Bernoulli crisis regime (p=0.12) that multiplies variance ×2.5, and a state-dependent basket-broadening (the realist +0.12 becomes +0.18 in a crisis) so a bad draw is hit on both the rate and the breadth; a rotated-Clayton variant (λU=0.71) gives a directional upper bound. Independently reproduced from the raw draw vectors and on a different seed. The nuance for the thesis: that state-dependent broadening is the only channel that touches incidence, and it attenuates the pre-tax Suits only slightly (−0.26 → −0.25 at +0.12, −0.24 at +0.30) — regressive throughout. The coupling parameters are deliberately assumptions with reported sensitivity, not fitted estimates.
Methods & sources
How this was built
A probability-weighted blend of three scenarios produces an expected cumulative price rise by December (default +2.91%), anchored to realized monthly CPI (March +0.9%, April +0.6%). That rate is adjusted per group by a basket factor derived from food-and-energy budget shares by income — about 18.5% for the lowest quintile versus a ~13.75% aggregate, per the New York Fed's analysis of the Consumer Expenditure Survey19 — then applied to monthly spending and scaled by household counts for the national total. The monthly trajectories compound each scenario's inflation track and reweight live. Measured (sourced) inputs: incomes, AGI, CE spending by quintile / age / tenure, debt holdings, household counts, current rate and yield levels. Modeled (estimated): basket factors, within-bar dollar splits, market sensitivities, and the scenario probabilities themselves.
Reading the numbers like an analyst. The headline monthly figure is a December run-rate; ×12 gives a steady-state annualized rate, not the realized 2026 total. Because the shock builds month over month, the honest Jun–Dec cumulative (in the panel) is far smaller — about $110B in the consensus lens — roughly 4.45× the December run-rate, because the burden builds over the window rather than sitting at its December level all seven months. Figures are nominal and undiscounted (immaterial over seven months). The burden is the cost to hold consumption constant — a real-income hit, and therefore an upper bound, since households substitute and economize. In the energy-realist lens the basket factors carry an extra +0.12 (embedded-energy broadening) on top of higher scenario inflation; that double lever makes it the most aggressive assumption in the model and should be read as a stress case, not a forecast.
Not financial, legal, or tax advice. The triage ordering describes how obligations are typically prioritized in counseling; the right action depends on individual circumstances. Aggregate totals are a central estimate (read as ~$20–30B/month), not a precise figure.
Sources
Sources are grouped by which dataset they power. The shared foundation underlies both lenses; the lens you are not viewing is dimmed — flip the toggle at the top to compare.
Shared foundation — both lenses
U.S. Census Bureau, Income in the United States: 2024 (median $83,730; mean $121,000). census.gov
IRS Statistics of Income / Tax Foundation, Latest Federal Income Tax Data, TY2022 (median AGI ~$50,400/return). taxfoundation.org
U.S. Bureau of Labor Statistics, Consumer Expenditures — 2024 (spending by quintile; housing share). bls.gov/cex
Yu-Ting Chiang & Mick Dueholm, Which U.S. Households Have Credit Card Debt?, Federal Reserve Bank of St. Louis (May 2024; figures corrected July–Sept 2024). Card-balance-to-monthly-income by income decile (1st–10th): 85, 62, 58, 42, 66, 49, 48, 36, 27, 8 percent — verified against the corrected source. The non-monotonic 5th–7th-decile bump is a real feature: those deciles hold the highest share of balance-carriers (28→61% peak at the 7th decile). stlouisfed.org
Federal Reserve Bank of Cleveland, The Evolution of Student Debt 2019–2022. clevelandfed.org
U.S. Census Bureau, household income distribution & counts, 2024. census.gov
U.S. Census Bureau, median household income by age of householder, 2024 (peak $116,800 at 45–54; $56,680 at 65+). census.gov (PDF)
Washington Post / Associated Press, Strait of Hormuz mine-clearing could take six months (Apr 2026). washingtonpost.com
USAFacts / U.S. Census ACS, renter housing cost burden (median rent $1,487; 32.8% of renter income). usafacts.org
Federal Reserve, Changes in U.S. Family Finances from 2019 to 2022 (2022 SCF). federalreserve.gov
Federal Reserve, Survey of Household Economics and Decisionmaking (student-loan delinquency by income). federalreserve.gov/shed
BLS Consumer Expenditure Survey, age of reference person & composition tables, 2023–24 (category shares; single-parent housing 37.3%). bls.gov/cex/tables
CNBC, Federal Reserve rate & Treasury market coverage (May 2026) — funds rate 3.5–3.75%, markets pricing no further 2026 cuts. cnbc.com
Federal Reserve Bank of New York, Liberty Street Economics — food-and-energy budget shares by income quintile (lowest ~18.5% vs ~13.75% aggregate; the 4th quintile sits near the aggregate); cross-checked with BLS experimental CPI by income. newyorkfed.org
U.S. Energy Information Administration, U.S. energy facts — imports and exports — the U.S. remained a net crude oil importer in 2024; crude oil was about 67% of total U.S. energy imports. eia.gov
Darius Dale / 42 Macro, A Global Liquidity Crisis Is Underway… What’s Next? (Mar 2026) — the U.S.–Israel–Iran oil shock has moved beyond a supply disruption into a global liquidity crisis, tightening financial conditions via the capital account and pressuring risk assets and safe havens alike; 42 Macro’s inflation regimes feature elevated cross-asset correlation. 42macro.com
Michael Howell, CrossBorder Capital / Capital Wars — the ~65-month global liquidity cycle peaked in Q3 2025 with a downswing likely into 2027; “Bitcoin is the most liquidity-sensitive asset on the planet”; a ~$40T debt-refinancing wall (2026–2028) absorbs liquidity, though fiscal dominance and currency debasement support crypto over the long run. capitalwars.substack.com
BLS Consumer Expenditure Survey, consumer units with reference person under age 25 (Table 3203, 2021–22): income before taxes ~$47,100, after taxes ~$45,400; the youngest, most renter-heavy cohort, with 1.4 earners and the thinnest cushion. bls.gov/cex/tables
Pew Research Center (2024–25) & U.S. Census ACS 2024 — 57% of 18–24-year-olds (and ~32.5% of 18–34) live with a parent, driven by housing affordability. The personal-budget split for this non-householder group is estimated from young-adult earnings and the housing-buffer / transport-exposure structure. pewresearch.org
Michael Howell, CrossBorder Capital / Capital Wars, “Prospects for a Puzzling 2025” — a “not-QE, QE” (and “not-YCC, YCC”) regime: heavy short-dated T-bill issuance plus reverse-repo (RRP) and Treasury General Account (TGA) drawdowns add net money-market liquidity even during Fed QT, while bill issuance creates coupon scarcity that suppresses long yields. capitalwars.substack.com
Hudson Bay Capital (S. Miran & N. Nordvig), Activist Treasury Issuance and the Tug-of-War over Monetary Policy — tilting issuance toward bills / cutting coupons is “stealth QE” with many of the same liquidity consequences as Fed QE (a quarter’s ~$245B of “missing coupons” scaled to a ~$800B-equivalent in their example). hudsonbaycapital.com
Federal Reserve Bank of New York (Perli, Mar 2026) & BNY, Treasury Road Trip — a TGA drawdown lifts system liquidity (reserves + ON RRP); after the July 2025 debt-limit increase the Treasury rebuilt the TGA to ~$900B via >$600B of net bill issuance, dropping system liquidity to its lowest since QT began and pressuring repo rates — after which the Fed halted QT on Dec 1, 2025. newyorkfed.org · bny.com
Incidence indices computed from the seven-bracket burden and representative incomes (see the Incidence & Sensitivity sheet). Suits = 1 − 2∫(burden-vs-income concentration curve); Kakwani = burden concentration − income Gini, by trapezoidal integration. The pre-tax base is comparable to the public-finance benchmark literature; the after-tax base measures burden as a share of disposable income. Grouped point-masses understate within-population inequality, so both are conservative.
Monte Carlo predictive distribution — companion methodological supplement (research_analysis.py, seed 20260605, N=200,000), independently reproduced here with a different seed. Realized December cumulative rate 90% interval 0.4–5.8% (median 2.8%); monthly aggregate burden 90% interval $3.7–49.3B (median $23.3B). A law-of-total-variance decomposition attributes ~82% of the spread to which scenario realizes and ~18% to within-scenario path noise; weight uncertainty is negligible. About 28% of mass falls outside the deterministic $9.4–44.2B corners (~15% below, ~13% above). Within-scenario noise is Gaussian (persistent σ=0.08pp/mo, transitory σ=0.15pp/mo); this is the historical-volatility floor — a coupled-tail extension is reported alongside it (note 34).
Parametric bootstrap of the incidence indices over modeled inputs (basket factors 6% CV, representative incomes 10% CV, effective tax ±2pp, counts/spend 2.5%/3%; 100,000 replicates), independently reproduced. Pre-tax Suits −0.26 (90% CI −0.31, −0.21), Kakwani −0.25 (−0.29, −0.21); after-tax −0.22 / −0.21; regressive in 100% of replicates on both bases. Captures modeled-input, not survey-sampling, uncertainty; a microdata resample (SCF/CE public-use files) would add survey confidence intervals and is specified but unestimated.
Coupled-tail Monte Carlo — companion specification (coupled_tail_mc.py, seed 20260605, N=200,000), independently reproduced from the raw draw vectors and on a different seed. Retains the floor and adds a tunable coupling layer: a Student-t copula across months (ρ=0.35, νcop=4; tail-dependence λ=0.18), fat-tailed Student-t marginals (ν=5), a Bernoulli crisis regime (p=0.12, variance ×2.5), and a state-dependent basket-broadening (BFADD 0→0.18 in crisis); a rotated-Clayton variant (λU=0.71) gives a directional upper bound. The median is preserved (~$23–24B in every run); coupling reweights the upper tail — aggregate p99 $54.8B (floor) → $68.7B (t) → $75.3B (Clayton); P(burden>$60B) 0.1%→2.1%; p99 “novel-territory” gap ≈$14–20B. Knobs set to the agreed central values (p_crisis=0.12, crisis_mult=2.5, bfadd_crisis=0.18). The tail is driven by crisis probability/severity and breadth; the copula degrees-of-freedom are second-order once a regime is present. The broadening attenuates pre-tax Suits only mildly (−0.26→−0.25 at +0.12, −0.24 at +0.30), never flipping sign.
Consensus / macro lens only
Vanguard, Oil shock complicates central bank outlooks (Apr 2026) — a stagflationary oil shock can weigh on both stocks and bonds, with policy rates likely higher-for-longer. vanguard.com
IMF, Oil Prices and Inflation Dynamics (Choi et al., 2017) & Federal Reserve DSGE model (2024) — a 10% oil rise adds ~0.15–0.4 percentage point to headline CPI on impact. imf.org · federalreserve.gov
European Central Bank working paper, Global financial markets and oil price shocks in real time — 10-yr government bond yields tend to rise on oil price shocks. ecb.europa.eu
Real Investment Advice / Kedia Advisory (2026) — the S&P 500 rose in 6 of 7 oil-spike episodes since 1986 (avg +24% over the following year); the equity response is regime-dependent. realinvestmentadvice.com
Energy-realist lens only — Berman & Hagens
Art Berman, America Has Plenty of Oil—Just Not the Right Kind (artberman.com, May 2026) — the U.S. imported ~6.3 Mb/d of crude and exported ~4.0 Mb/d in 2024–25 (a net crude importer); refineries need imported medium/heavy grades, losing them implies a diesel deficit and margin dislocation; “energy independence” is an accounting artifact. artberman.com
Arthur Berman & Nate Hagens, The Great Simplification — “Shale Oil and the Slurping Sound” and “Peak Oil: The Hedonic Adjustment”: U.S. shale has been high-graded and cannibalized, implying steep declines; roughly 40% of what the U.S. labels “oil” is NGPLs, ethanol, biofuels, and refinery gain. thegreatsimplification.com
Nate Hagens, The Great Simplification — oil underpins transport, agriculture, mining, and trade (“oil is the economy”), so energy shocks transmit more broadly than a narrow cost-share implies; “Wide Boundary News” (May 2026) flags the misleading blurring of crude vs. petroleum-product statistics. thegreatsimplification.com
Updates since publication
What we have learned since this went out
The study above is preserved exactly as published. Nothing in it has
been rewritten, re-run, or quietly corrected — it is a reading of the world taken at a moment,
and its value depends on it staying that way. Everything learned since lives down here, dated,
so you can see what held, what didn’t, and when we knew.
Update 01Scope: June FOMC · oil path · § Five Fed meetings
What was validated
The study’s consensus lens projected that the Fed would hold at 3.50–3.75% and signal
higher-for-longer, with markets pricing no further 2026 cuts. On 17 June the FOMC held at
3.50–3.75% on a unanimous 12–0 vote — a fourth consecutive hold — struck the
easing-bias language from its statement, and removed the 2026 cut from its dot plot. The median
year-end projection rose to 3.8% (from 3.4% in March), and 9 of the 18 submitting
participants penciled in at least one hike; markets began pricing one as early as October. The Committee also raised its 2026 headline PCE inflation
projection to 3.6% (core 3.3%), up from 2.7% in March, attributing part of the rise to energy
supply shocks — and the June minutes name the closure of the Strait of Hormuz explicitly.
The direction of the call was right. If anything the Committee moved more hawkish than the
consensus lens described.
What has not resolved
Which scenario the world is actually on. Within days of publication a U.S.–Iran framework
agreement pulled Brent from a wartime peak above $120 down to roughly $72 by late June
— briefly back to pre-war levels, tracking the study’s ceasefire path (+1.10% cumulative
by December). It has since reversed: renewed strikes, an attack on a Qatari LNG tanker on 7 July,
and a reinstated U.S. blockade of Iranian vessels with a proposed 20% transit fee pushed Brent back
above $80 on 13 July, its highest in about a month.
The scenario has not collapsed onto a single path. It is oscillating across the range the study
modeled — which is the case for reporting a distribution and a set of sliders rather than a
forecast, not against it.
What is untouched
The central finding. The incidence indices are invariant to the inflation rate — Suits and
Kakwani describe the shock’s shape, not its size. Whichever path realizes, the bill
still concentrates in dollars at the top and in share of income at the bottom. A smaller shock is a
smaller version of the same regressive shape, not a different shape. Nothing in this update moves
the thesis; it moves the magnitude’s odds.
What we are watching
The 28–29 July FOMC meeting, and whether the Hormuz transit regime stabilizes. If the
December run-rate resolves materially away from the ~$24.7B/month central estimate, we will
say so here — with the same specificity we are asking of you.
Every study here has a surface a non-specialist can read, and a floor directly beneath it with
the numbers, code, and assumptions a specialist needs to trust it. This is the floor. The engines
run from a fixed seed (20260605, N = 200,000), and every headline statistic
in the report above is recomputable from what follows — including the raw draw vectors, so you
can re-derive any percentile yourself rather than take ours.
If a number in the report and a number in these files disagree, the files
win — and we want to know. That is what the next box is for.
Found a discrepancy?
Hold this study to the standard it holds others to.
This study exists because someone caught an annual inflation rate being applied as a monthly one
— an error that overstated a real problem by roughly fifteen- to twentyfold. A number that
exaggerates a genuine crisis doesn’t help the people it describes; it hands ammunition to
anyone who wants to dismiss them. The same standard applies here. Be specific, cite something,
and we’ll do the work — and if you’re right, we amend the study and log the change in the open.