Full transparency on every assumption, coefficient, and data source that powers the impact estimates.
EstimatedImpactCeiling =
PassThrough
× Σ( FactorChange[i]
× ExposureCoefficient[cat, factor]
× FXImportAdjustment[country, factor]
× LagAdjustment[profile] )Where PassThrough = 1.0 (100% ceiling), FactorChange is the observed or assumed percentage change in an upstream commodity or macro factor, ExposureCoefficient maps each consumer category to its upstream drivers, FXImportAdjustment amplifies for currency depreciation and import dependency, and LagAdjustment weights the shock over the appropriate time horizon.
Every estimate assumes that 100% of upstream cost changes flow through to the consumer. This is deliberately an upper bound. In reality, governments subsidize, retailers absorb margins, and supply chains adapt. The ceiling tells you the worst-case scenario, not the most likely one.
Each consumer category (bread, fuel, cooking oil, etc.) is mapped to a set of upstream factors (wheat price, crude oil, fertilizer, freight). The exposure coefficient for each category-factor pair reflects how much of the final product cost is attributable to that input. These coefficients are derived from USDA cost-of-production surveys and FAO food balance sheets.
For import-dependent countries, currency depreciation amplifies the local-currency cost of dollar-denominated commodities. The FX adjustment multiplies the commodity price change by the degree of depreciation. Countries that are net exporters of a commodity receive a dampened or zero adjustment for that factor.
Price shocks do not hit consumers instantly. The model supports four lag profiles: Immediate (fuel, forex-sensitive goods), 3-month (perishables, short supply chains), 6-month (processed foods, regional supply chains), and 12-month (staples with strategic reserves, long-term contracts). Each profile weights the factor change over the appropriate time horizon.
The model operates at the country level, not the city or province level. Within-country variation (urban vs rural, coastal vs inland) can be significant but is not captured. We chose country-level resolution because the macro inputs (exchange rates, import dependency ratios, commodity prices) are most reliably available at that granularity.
The model does not account for government price controls, subsidy programs, speculative hoarding, supply chain disruptions beyond commodity costs, or local market competition dynamics. It underestimates impacts in countries with structural currency crises (e.g., Türkiye, Nigeria) where parallel exchange rates diverge from official rates.
All figures represent a theoretical ceiling under the stated assumptions. They are not predictions of future retail prices. Actual consumer impact depends on government policy, market competition, supply chain resilience, and retailer pricing strategies, none of which are modeled here. Use these estimates as a starting point for understanding exposure, not as actionable financial guidance.
In countries with relatively stable exchange rates and functioning commodity markets, the model ceiling consistently exceeds realized prices by 20-50%, which is the expected behavior for a 100% pass-through upper bound. The model correctly identifies the direction and approximate magnitude of consumer price impacts.
| Country | Category | Model Ceiling | Realized | Gap | Status |
|---|---|---|---|---|---|
| 🇵🇭PHL | Bread | 18.4% | 12.1% | +6.3pp | Ceiling held |
| 🇪🇬EGY | Oil | 52.1% | 47.8% | +4.3pp | Ceiling held |
| 🇮🇳IND | Veg | 9.7% | 8.1% | +1.6pp | Ceiling held |
| 🇧🇷BRA | Fuel | 11.2% | 6.4% | +4.8pp | Ceiling held |
| 🇹🇷TUR | Bread | 24.1% | 68.2% | -44.1pp | Undershot |
| 🇳🇬NGA | Oil | 38.6% | 44.1% | -5.5pp | Undershot |
| 🇵🇰PAK | Dairy | 19.3% | 31.8% | -12.5pp | Undershot |
| 🇯🇵JPN | Fuel | 8.2% | 5.8% | +2.4pp | Ceiling held |
| 🇰🇷KOR | Fuel | 9.1% | 6.2% | +2.9pp | Ceiling held |
| 🇱🇧LBN | Basket | 28.8% | 34.2% | -5.4pp | Undershot |
“Ceiling held” means the model ceiling exceeded the realized price change, as expected. “Undershot” means the realized price change exceeded the model ceiling, indicating the model failed to capture all cost drivers.
Validation context: Model ceilings shown above are computed at 100% pass-through with immediate lag (1.0x multiplier). In the simulator, selecting a different lag period (e.g., 12-month at 0.75x) will reduce the ceiling proportionally.
The model systematically underestimates impacts in three countries due to structural issues not captured by the commodity-pass-through framework: