Select a country and a consumer category. We'll show you how upstream commodity and currency shocks could flow into everyday prices — transparently, with every assumption visible.
Scenario estimator only. All outputs show an upper-bound ceiling assuming 100% pass-through of upstream costs. Actual retail prices depend on competition, policy, logistics, and market structure. This is not financial advice.
Each card below shows a sample scenario. Click any card to open the full simulator with that selection pre-loaded.
Each number is a scenario ceiling — the maximum estimated consumer price movement if 100% of upstream cost changes were passed directly through to buyers. In reality, actual pass-through is often 30–70% of this ceiling due to competition, regulation, and retailer margin absorption. The methodology page explains this in full.
Most people hear about oil spikes, currency crises, or grain shortages — but they can't connect those headlines to what they pay for bread, fuel, or cooking oil. This product builds that bridge, transparently, with every assumption and confidence level visible. We are a scenario estimator, not a prediction engine.
Select a conflict and consumer category to see which countries are most and least affected, with estimated price impact ceilings.
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Select multiple categories to build a weighted household basket and see the combined estimated impact ceiling.
Every number on this site comes from a simple, transparent calculation. This page explains the logic, the assumptions, and the limitations in full.
The model follows a single directional pipeline. Upstream commodity or currency changes are scaled by category-level exposure coefficients, adjusted for import dependence and FX, then time-lagged to produce an estimated ceiling impact.
We assume the entire upstream cost change is passed onto the consumer with no absorption by retailers, distributors, or government. This is almost never true in practice. It gives the maximum plausible ceiling and is clearly labeled as such on every output.
Direct exposure: the category uses the commodity directly (e.g., bread uses wheat). Indirect exposure: the commodity affects production costs that flow into the category (e.g., diesel raises transport costs for all food). Both are modeled separately with distinct coefficients.
For imported commodities, the local-currency cost depends on both the world price and the exchange rate. We apply an import-weighted FX adjustment per country and commodity. Fully domestic categories receive a lower FX sensitivity score.
Commodity shocks rarely reach shelf prices instantly. We offer four lag horizons: immediate, 3-month, 6-month, and 12-month. Longer lags produce lower ceiling estimates because shocks dissipate. The default 6-month lag is a reasonable midpoint for food categories.
Local pricing depends on local competition, logistics, regulation, and retailer structure. Our model has defensible national-level data from official CPI and trade statistics. City-level claims from macro data alone would be misleading precision.
It cannot predict exact future prices, capture policy interventions (subsidies, price controls), model informal economy dynamics, or account for supply disruptions not reflected in commodity prices. These limitations are real and we do not hide them.
Every output on this site represents what would happen if 100% of upstream cost changes reached consumers — which is an upper bound that rarely occurs in practice. The gap between our ceiling and realized inflation is expected and is shown explicitly on the Validation page. We treat that gap as a feature, not a flaw.
Where we have realized inflation data, we compare our model's ceiling estimate to what actually happened. We never hide poor model fit.
A good scenario ceiling should consistently be above realized inflation — since full 100% pass-through is the upper bound. A gap of +3 to +8 percentage points is consistent with the model working as intended. A gap near zero suggests the model may be under-estimating exposure. A very large gap (15pp+) may indicate coefficient problems or structural market interventions.
| Country | Category | Modeled Ceiling | Realized Inflation | Gap (pp) | Implied Pass-Through | Validation Source | Notes |
|---|---|---|---|---|---|---|---|
| 🇵🇭 Philippines | Bread & Cereals | +18.4% | +12.1% | +6.3 | ~66% | PSA CPI | Consistent with partial retail absorption |
| 🇪🇬 Egypt | Cooking Oil | +52.1% | +47.8% | +4.3 | ~92% | CAPMAS | High pass-through environment, limited subsidies |
| 🇮🇳 India | Vegetables | +9.7% | +8.1% | +1.6 | ~83% | MoSPI CPI | Near-full pass-through for unprocessed category |
| 🇧🇷 Brazil | Household Fuel | +11.2% | +6.4% | +4.8 | ~57% | IBGE IPCA | Petrobras pricing policy absorbed ~43% |
| 🇹🇷 Türkiye | Bread & Cereals | +24.1% | +68.2% | –44.1 | >100% | TÜİK | ⚠ TRY collapse amplified far beyond commodity exposure. Model structural miss — currency crisis outpaced model inputs. |
| 🇳🇬 Nigeria | Cooking Oil | +38.6% | +44.1% | –5.5 | >100% | NBS | FX black market premium not fully captured in model inputs |
| 🇵🇰 Pakistan | Milk & Dairy | +19.3% | +31.8% | –12.5 | >100% | PBS | ⚠ Energy subsidy removal during period not captured |
The model systematically underestimates in countries experiencing parallel currency crises (NGN, PKR, TRY), because the official FX rate diverges from actual import prices. We flag this explicitly with a "Structural Caveat" label in affected country outputs.
Every factor, coefficient, and validation series used in this model is listed here with its source, coverage, and update cadence.
Monthly commodity price series covering crude oil, natural gas, wheat, maize, soybeans, DAP fertilizer, and major base metals. Used as primary macro factor input series.
Official bilateral and effective exchange rates for all model countries. Used for FX adjustment in import-exposed categories. Monthly frequency.
Aggregate and sub-component food commodity indices. Used as secondary validation for grain and oil price trend direction and magnitude.
Brent and WTI crude prices, refined product prices, and weekly storage data. Used for energy factor inputs and refinery margin estimates.
Official CPI data from PSA (Philippines), CAPMAS (Egypt), MoSPI (India), IBGE (Brazil), NBS (Nigeria), and equivalent national statistical offices. Used for validation overlay and CPI basket weights.
Country-level commodity import shares for calibrating import-dependence coefficients. Used to weight FX sensitivity by category and country.