Cheapflation Cycles: How Upstream Costs Drive Inflation Inequality

 The core mechanism behind cheapflation cycles is a phenomenon known as pass-through in levels. This concept posits that when firms face upstream cost shocks, they tend to adjust their retail prices on a "dollars-and-cents" basis rather than a percentage basis. Consequently, when the cost of raw materials or manufacturing rises, both premium and budget varieties within a specific product category experience similar absolute price increases.

The Logistics of the Mechanism

While the absolute price increase might be identical across varieties, the percentage impact differs significantly:

• Lower-priced products: A fixed absolute increase (e.g., a few cents per ounce of coffee) constitutes a larger percentage change for cheaper varieties.
• Higher-priced products: The same absolute increase results in a smaller percentage change for premium varieties.

Because low-income households disproportionately purchase lower-priced varieties, they face higher inflation rates than high-income households during periods of rising upstream costs.

Cheapflation in the Context of Cycles

This mechanism generates predictable cycles in inflation inequality that fluctuate alongside upstream cost movements:

• Rising Upstream Costs: When commodity or manufacturing prices spike—as seen during the Great Recession (2008–2011) and the post-pandemic period (2021–2023)—the gap in inflation between low- and high-income households widens significantly.
• Falling Upstream Costs: Conversely, when input costs decrease relative to the aggregate price level, the inflation gap narrows or can even become negative.

The sources indicate that this mechanism is highly effective at accounting for observed fluctuations, explaining nearly 60 percent of the variation in the inflation gap between top and bottom income quintiles for food-at-home purchases.

Theoretical Foundation: Shift-Invariance

The sources reconcile this behavior with models of imperfect competition through a property called shift invariance in demand systems. Under shift-invariant demand, firms maintain positive markups but pass through common cost shocks (like commodity price changes) one-for-one in levels to their retail prices. This explains why firms don't simply maintain fixed percentage markups, which would lead to proportional price increases across all varieties.

Limitations of Official Statistics

A critical consequence of this within-category mechanism is that it is largely invisible in official inflation data. Standard statistics, like those from the Bureau of Labor Statistics (BLS), aggregate prices at a category level (e.g., "roasted coffee"), which masks the internal price divergence between cheap and expensive varieties. Research shows that official data can understate differences in inflation experienced by low- and high-income households by 70–90 percent.

Beyond food, there is evidence that this mechanism applies to other consumption categories, including automobiles, apparel, and services, suggesting that cheapflation cycles are a broad feature of the economy.

Inflation inequality refers to the differences in inflation rates faced by households across the income distribution, a phenomenon that is systematically driven by Cheapflation Cycles. These cycles are characterized by fluctuations in the inflation gap between low- and high-income households, primarily triggered by upstream input cost shocks.

The Mechanism of Inequality

The sources identify pass-through in levels as the fundamental driver of this inequality. When upstream costs (like commodity prices) rise, firms tend to increase retail prices by similar absolute amounts (e.g., cents-per-ounce) across all product varieties in a category.

Because low-income households disproportionately purchase lower-priced varieties, these fixed absolute increases result in a larger percentage price change for their typical consumption basket. For example, in 2011, when coffee commodity prices spiked, the lowest unit price products saw a 40% inflation rate, while the highest price varieties rose by only 12%—even though both had nearly identical absolute price increases in cents-per-ounce.

Cyclical Nature of the Inequality Gap

Inflation inequality is not static but fluctuates in a predictable cycle tied to input costs:

• Rising Input Costs: During periods of high food-at-home inflation (e.g., 2008, 2011, and 2021–2023), the inflation gap between the bottom and top income quintiles widens significantly.
• Falling Input Costs: When input costs decrease, the gap narrows and can even become negative, meaning low-income households temporarily face lower inflation than high-income households.

In food-at-home purchases, this "within-category" mechanism accounts for nearly 60% of the variation in the inflation gap between the top and bottom income quintiles over time. When combined with different expenditure shares across product categories, it explains approximately 70% of the variance.

Understatement by Official Statistics

A major finding in the sources is that official statistics largely mask this inequality. The Bureau of Labor Statistics (BLS) aggregates price data at the category level (such as "roasted coffee"), which hides the internal price divergence between budget and premium varieties. Consequently, official data may understate the difference in inflation experienced by low- and high-income groups by 70–90% during periods of rising costs. For the 2021–2023 period, aggregation understated the growth in food prices for the lowest-income quintile relative to the highest by a factor of seven.

Broader Context Beyond Food

This pattern of inflation inequality extends to various other sectors and units:

• Automobiles: Lower-priced car models and those with lower-income buyer bases exhibit higher inflation sensitivity to overall price changes.
• Geography: Low-income cities experience higher and more volatile inflation because they consume lower-priced varieties that are more sensitive to national cost shocks.
• International Trade: Countries that import lower-priced varieties face greater import price inflation when global commodity prices rise.

Ultimately, the sources suggest that these cycles in inequality are a parsimonious explanation for the disproportionate burden of inflation on the poor during economic shocks, often leaving little room for alternative theories like price-gouging or changes in demand elasticity.

Official statistics produced by agencies like the Bureau of Labor Statistics (BLS) significantly mask the inflation inequality generated by cheapflation cycles because they aggregate price data at a level that is too broad to capture within-category shifts,.

The Core Problem: Aggregation Bias

The primary issue with official statistics is that they report inflation rates pooled across various products within a single category, such as "entry-level items" (ELIs),. Because the mechanism of "cheapflation" operates through pass-through in levels—where absolute price increases hit cheaper varieties harder in percentage terms—the resulting price divergence occurs within these categories,.

The sources highlight several critical ways this aggregation fails to represent the economic reality of low-income households:

• Understating Inequality: Official statistics can understate the difference in inflation experienced by low- and high-income households by 70% to 90%,.
• Invisible Fluctuations: Large swings in the inflation gap are often completely invisible in public data. For example, when coffee commodity prices spiked in 2011, disaggregated scanner data showed an 8 percentage point inflation gap between low- and high-income households within the coffee category alone; however, because the BLS tracks "roasted and instant coffee" as a single item, this gap was lost in the official average,.
• Sensitivity to Upstream Costs: Aggregation masks the fact that food-at-home inflation for low-income households is much more sensitive to upstream producer prices. Official data masks nearly 90% of this disproportionate sensitivity for food manufacturing costs and 72% for farm product costs,.

Case Study: 2021–2023 Post-Pandemic Inflation

The discrepancy between official statistics and actual experiences was particularly "dramatic" during the 2021–2023 period of rising input costs,.

• Official View: Price indices built on ELI-aggregated data showed a negligible difference in food price growth (only 0.3 percentage points) between the bottom and top income quintiles.
• Actual View: Disaggregated data revealed that low-income households experienced 2.4 percentage points higher growth in food prices than high-income households.
• Factor of Seven: In this instance, official aggregation understated the differential growth in food prices for the lowest-income quintile relative to the highest by a factor of seven,.

Generalization Beyond Food

This issue is not limited to groceries; the sources provide evidence that official statistics similarly obscure inequality in other major spending categories:

• Automobiles: The BLS tracks new and used vehicles under broad ELI codes (TA011 and TA021), but does not disaggregate by specific makes and models. Consequently, they miss the fact that lower-priced car models are more sensitive to overall vehicle inflation,.
• Geographic and Import Data: Similar aggregation issues lead to a failure to capture how low-income cities or countries importing cheaper varieties are more exposed to national or global cost shocks,.

The sources conclude that official statistics are especially biased precisely when input costs rise sharply—which is when policymakers are most likely to be concerned about the distributional burden of a rising cost of living.

The sources provide evidence that the mechanism of pass-through in levels and the resulting cheapflation cycles are a broad feature of the economy, extending well beyond the primary data on food-at-home.

Evidence in the Automobile Sector

One of the most detailed areas of evidence outside of groceries is the automobile market. Using microdata on household vehicle purchases and manufacturer-suggested retail prices (MSRPs), the research finds:

• Price Sensitivity: Within a specific vehicle make, a model with a 10% lower initial price exhibits year-over-year growth that is 3.9% to 7.0% more sensitive to average price growth.
• Income Base Sensitivity: Models with a 10% lower-income customer base have inflation rates that are 8.6% to 9.1% more sensitive to overall vehicle inflation.
• Economic Impact: This is significant because vehicle purchases constitute a major share of consumer expenditures—roughly 6.8% of spending between 2021 and 2023, nearly as much as food-at-home.

Evidence Across Other Goods and Services

Using the C2ER Cost of Living Index (COLI), which tracks standardized products across roughly 300 urban areas, the sources identify cheapflation patterns in several other sectors:

• Apparel: Patterns of higher inflation for cheaper items were observed in products like boys' and men's denim jeans.
• Services: Cheapflation was detected in service costs, such as barbershop haircuts and washing machine repair calls.
• Housing and Utilities: The data also showed evidence of these cycles in apartment rents and utility bills.
• General Merchandise: Other goods, including generic medicines (aspirin), newspaper subscriptions, and tennis balls, follow the same pricing logic.

Geographic and Metropolitan Evidence

Beyond specific product categories, the sources use Bureau of Labor Statistics (BLS) metropolitan area price indices to show that this mechanism affects entire cities based on their income levels.

• City-Level Volatility: Consumer price inflation in lower-income cities is higher and more volatile than in high-income cities because they consume lower-priced varieties that are more sensitive to national cost shocks.
• Sector Aggregates: This reduced inflation sensitivity in high-income cities holds true across broad aggregates, including food away from home, goods excluding food, and services excluding shelter.

Implications for Generalization

The consistency of these findings across food, automobiles, apparel, and services suggests that cheapflation cycles are not an isolated phenomenon of the grocery aisle. Instead, they represent a systematic cross-sectional variation in how different income groups experience inflation based on the varieties they consume. The sources conclude that because official statistics aggregate data at the category level (e.g., "new cars and trucks" or "apparel"), they likely miss quantitatively important differences in the cost of living for households across the income distribution in all these sectors.

The theoretical framework of cheapflation cycles is centered on the principle of pass-through in levels, which posits that firms respond to upstream cost shocks by adjusting retail prices on a fixed absolute basis (cents-per-ounce) rather than a fixed percentage basis. The sources provide a rigorous theoretical foundation for this behavior and subject it to extensive robustness testing to ensure its validity across various economic conditions.

Theoretical Foundation: Shift-Invariance

To reconcile the observed "dollars-and-cents" price adjustments with economic theory, the sources highlight a property of demand systems called shift invariance.

• Contrast with Standard Models: In many standard models of imperfect competition, such as those using Constant Elasticity of Substitution (CES) demand, firms maintain fixed percentage markups. This would imply that pass-through in levels is strictly greater than one, as a cost increase would be multiplied by a gross markup.
• Unitary Pass-Through: Shift-invariant demand systems allow for positive markups and downward-sloping demand curves while predicting that common cost shocks are passed through one-for-one in levels. This theoretical property is essential for explaining why premium and budget brands raise prices by the same absolute amount despite having different initial price points.

Robustness of the Mechanism

The sources conduct several tests to confirm that the pass-through in levels is a robust phenomenon and not an artifact of specific data or time periods:

• Exogenous Cost Shocks: To address concerns about reverse causality (e.g., demand shocks affecting commodity prices), the author uses instrumental variables (IV). Specifically, they use exchange rates for major exporters (Brazil and Colombia) and weather shocks to coffee-growing regions to isolate exogenous changes in input costs. The results consistently show uniform pass-through in levels across all price groups.
• Time Horizons: The baseline results use a 6-quarter horizon to measure long-run pass-through. Robustness checks show that changing this horizon to 4 or 8 quarters does not meaningfully alter the finding of uniform pass-through.
• Substitution Effects: A common critique of inflation measurement is the failure to account for consumers switching to cheaper products. The sources test this by comparing the baseline Laspeyres index to a Törnqvist price index, which accounts for substitution. They find that while the absolute level of inflation changes, the inflation gap between high- and low-income groups remains remarkably similar.

Nonhomotheticities and Trading Down

The research further explores how nonhomothetic preferences—where consumption patterns change as real income changes—affect cheapflation.

• Amplification of Inequality: During periods where food prices rise faster than income, households tend to "trade down" to lower-priced varieties.
• Theoretical Irony: Because lower-priced varieties are precisely the products experiencing the highest inflation due to pass-through in levels, the act of trading down actually amplifies the rise in the cost of living for low-income households. Accounting for these nonhomotheticities shows that low-income households face even higher inflation volatility than standard measures suggest.

Quantitative Accuracy

The sources propose a specific mathematical formula (Proposition 1) to predict inflation differences based on the "price gap" between varieties and the "excess inflation" of a category relative to wages. When tested against 18 years of food-at-home data, this theoretical prediction accounts for 70 percent of the observed variance in inflation inequality. Notably, the sources find that the "cheapflation" observed during the 2021–2023 surge was actually slightly less than expected given the massive rise in input costs, suggesting that pass-through in levels is a highly conservative and powerful explanation for inflation inequality.