Question 1

Why is food so dominant in Mexican inflation compared to advanced economies?

Accepted Answer

Food dominates Mexican inflation because it combines a large CPI weight with high sensitivity to both domestic demand cycles and global supply shocks. Colunga-Ramos, Chen, and Perales (2026) find food ranks first for both demand (importance 0.591) and supply (importance 0.533) in Mexico — a pattern distinct from Shapiro's (2024) U.S. benchmark where services dominate demand-driven inflation. The food-dominance pattern reflects Mexico's exposure to global commodity shocks, higher food expenditure shares in household budgets, and procyclical food demand that amplifies the demand-side contribution.

Question 2

What explains Mexico's slow disinflation since 2023 despite 725 basis points of tightening?

Accepted Answer

The services floor. Colunga-Ramos, Chen, and Perales (2026) show Mexican services contribute an average 0.555 percentage points to demand-driven inflation but correlate only 0.463 with aggregate demand inflation — high persistence, low cyclical amplitude. During 2023-2024, goods inflation fell from 8.25% to 3.19% driven by external supply normalization, but services inflation only fell from 5.01% to 4.71%, and the services demand component actually rose from 2.55% to 2.67%. The mechanism is sticky services prices combined with Mexico's tight labor market — minimum wages rose 88% in real terms from 2019-2023.

Question 3

Why does housing contribute so little to Mexican inflation despite being 18% of the CPI basket?

Accepted Answer

Housing prices in Mexico barely move. Colunga-Ramos, Chen, and Perales (2026) find housing importance scores of 0.054 (demand) and 0.018 (supply) — lowest across all five categories despite an 18.05% CPI basket weight. The correlation of housing with supply-driven inflation is slightly negative (-0.082), meaning supply shocks that contract real incomes actually dampen housing prices. The structural reasons are owner-occupied rent imputation based on slow-moving construction-cost surveys, a thin informal rental market, and weak mortgage-cost and housing-wealth channels. This housing non-response means the monetary transmission channels documented for the U.S. operate weakly in Mexico.

Question 4

How should an emerging-market central bank decompose inflation into supply and demand components?

Accepted Answer

Apply sign-restriction identification at the sectoral level following Shapiro (2024), then aggregate. Colunga-Ramos, Chen, and Perales (2026) operationalize this with rolling bivariate VARs (42 months, 12 lags) on log prices and log quantities for each of 31 Mexican CPI sectors, classifying monthly shocks: same-sign residuals = demand-driven; opposite-sign = supply-driven. Aggregate sectoral contributions using CPI weights into five economically meaningful groups (food, energy, services, manufacturing, housing). Construct an importance score as |correlation with aggregate inflation| x average contribution. Validate with a structural VAR using the Benigno et al. (2022) Global Supply Chain Pressure Index for supply shocks. The rankings are robust across window lengths of 36-60 months, lag choices of 6-18, and Bayesian estimation with Normal-Wishart priors.

Question 5

What SVAR ordering correctly identifies monetary policy shocks in an emerging market like Mexico?

Accepted Answer

Order external variables first (GSCPI, oil, U.S. CPI and IP, U.S. Divisia M2), then domestic inflation components, domestic real activity, domestic Divisia M2, and exchange rate — with a block-recursive impact matrix preventing contemporaneous feedback from domestic to external variables. Colunga-Ramos, Chen, and Perales (2026) use this structure following Kim and Roubini (2000) and Cushman and Zha (1997). Two implementation points matter more than ordering: use Divisia monetary aggregates — Colunga-Ramos and Valcarcel (2024) produce Mexico's first Divisia M4 and show it avoids the price puzzle without commodity-price controls — and include COVID-19 dummies for months with IGAE growth beyond three standard deviations.

Question 6

What historical episodes in Mexico validate the supply-demand inflation decomposition?

Accepted Answer

Three episodes show the decomposition provided policy-relevant guidance aggregate inflation missed. (1) May 2020: headline inflation at 2.56% looked neutral, but Colunga-Ramos, Chen, and Perales (2026) show 93.4% of it was supply-driven (2.39% vs 0.17% demand), validating Banco de Mexico's rate cuts from 7.00% to 4.25%. (2) September 2008 - March 2010 Global Financial Crisis: the demand component fell from 3.12% to 1.84% while supply fell less, meaning the decline was cyclical. (3) June-July 2024: headline inflation at 4.70% in June masked a demand component at 2.53% (above its 2.06% long-run average); next month headline jumped to 5.22% with demand at 3.32%, and Banco de Mexico correctly held at 11.00%.

Question 7

How do I replicate the rolling-window bivariate VAR sectoral decomposition step by step?

Accepted Answer

Replicating the Colunga-Ramos, Chen, and Perales (2026) decomposition requires three stages: bivariate VAR estimation on each sector, residual-sign classification, and aggregation to economically meaningful groups. For each of Mexico's 31 CPI sectors, estimate a rolling 12-lag bivariate VAR on log prices and log quantities using a 42-month window following Shapiro (2024, JMCB). When contemporaneous residuals from both equations share a sign, classify the shock as demand-driven; when they differ in sign, classify it as supply-driven. Multiply each residual by its CPI weight, then sum sector-level contributions into food, energy, services, manufacturing, and housing groups — aggregating after decomposition, not before, to preserve sign-based identification. Construct an importance score as the product of each category's absolute correlation with aggregate inflation and its average contribution. Validate with a structural VAR: demand-driven inflation should respond to domestic monetary expansions and supply-driven inflation to global supply-chain proxies such as the NY Fed's Global Supply Chain Pressure Index.

Question 8

Where do I get sectoral CPI and quantity proxies for the Mexico decomposition?

Accepted Answer

Three sources cover the data requirements. Sectoral CPI comes from INEGI (inegi.org.mx), which publishes the National Consumer Price Index (INPC) with 299 generic items organized into 31 special-aggregate sectors at monthly frequency back to 1969. Quantity proxies come from INEGI's Indicador Global de la Actividad Económica (IGAE) — Mexico's monthly GDP equivalent, available at the sector level — or from sector-level industrial and services production indexes in INEGI's Banco de Información Económica. Monetary and financial data come from Banco de México's Sistema de Información Económica: Divisia M2, policy rate, exchange rate, and inflation expectations. External data include the NY Fed Global Supply Chain Pressure Index, U.S. macro variables from FRED, and global oil prices. Note: April–June 2020 and April–May 2021 had IGAE growth exceeding three standard deviations; Colunga-Ramos, Chen, and Perales (2026) treat these with dummy variables in the validation SVAR to avoid distorted impulse responses.

Question 9

Can this sectoral decomposition be applied to other emerging markets like Brazil, India, or Turkey?

Accepted Answer

The methodology is, in principle, portable to any economy with sectoral CPI and monthly quantity proxies spanning at least eight to ten years, though cross-country application is outside the scope of this paper. Brazil is the most data-ready candidate: IBGE publishes the IPCA with detailed sectoral breakdowns and sector-level industrial production (PIM-PF); given Brazil's larger formal services sector, services may dominate demand-driven inflation more strongly than in Mexico. India's MoSPI CPI and Central Statistics Office IIP data support the same approach; India's higher food expenditure share would likely amplify the food-dominance pattern. Turkey's TÜİK CPI is available, though sectoral quantity proxies are sparser, and the 2018–2024 currency-crisis episode would test whether the decomposition can separate demand inflation from supply-side passthrough under exchange-rate stress. South Africa, Indonesia, Chile, and Colombia have the necessary statistical infrastructure; a cross-country panel would test whether the food-dominance pattern holds generally in emerging markets.

Question 10

What does the food-services-housing decomposition imply for Banco de México's monetary policy strategy?

Accepted Answer

Three concrete implications follow for any inflation-targeting central bank facing food-dominant and services-floor inflation. First, traditional interest-rate tightening is a blunt tool for food-driven inflation because food responds substantially to global supply disruptions beyond the reach of domestic monetary policy. Second, the services floor means disinflation will be slow even after demand-driven goods inflation normalizes; Colunga-Ramos, Chen, and Perales (2026) show services demand barely responded to twelve months of policy rates at 11.25%, so central banks should plan for extended hold periods rather than expecting symmetric easing when headline falls. Third, the near-zero housing contribution implies that housing-wealth and mortgage-cost transmission channels operating in advanced economies work weakly in Mexico, so rate cuts should not be expected to stimulate demand through housing as in the U.S. or Eurozone. For forward guidance design, when the demand-driven component is above its historical average — as in June 2024 ahead of the July reacceleration documented in the paper — the decomposition supports holding rates even as headline declines, providing a clear communication: rates are elevated because demand inflation remains above trend, not because the central bank is indifferent to food prices.

Category	CPI weight (MX)	Demand importance (MX)	Supply importance (MX)	Role in the U.S. benchmark	Mexican pattern
Food	Large	0.591 (rank 1)	0.533 (rank 1)	Primarily a supply-driven category in Shapiro (2024) .	Dominates both channels — the food-dominance pattern. Creates inflation swings only partially controllable through interest rates.
Energy	Medium	0.311 (rank 2)	0.267 (rank 2)	Primarily supply-driven in advanced economies.	Symmetric: Mexico produces oil for global markets and consumes it domestically, so energy amplifies both cyclical demand and supply pressures.
Services	Medium-large	0.257 (rank 3)	0.098 (rank 4)	Dominates demand-driven inflation in Shapiro (2024) .	Large average contribution (0.555 pp) but low correlation (0.463) — the services floor. Slow-moving; explains persistent disinflation resistance since 2023.
Manufacturing	Medium	0.209 (rank 4)	0.100 (rank 3)	Procyclical in most economies.	High demand-side correlation (0.691) but modest magnitude. Global value chain integration absorbs supply disruptions.
Housing	18.05%	0.054 (rank 5)	0.018 (rank 5)	Largest component of core CPI in the U.S.; strong monetary-policy response channel.	Housing non-response. Prices barely move; correlation with supply-driven inflation is even slightly negative (-0.082).

Luis Fernando Colunga-Ramos | Robin Chen

Decomposing supply and demand driven inflation in Mexico: Evidence from sectoral analysis

Why Mexican Inflation Behaves Differently: Food Dominates, Services Persist, Housing Barely Moves

Key Concepts

Where Mexican Inflation Differs from the United States

Q1. Why is food so dominant in Mexican inflation compared to advanced economies?

Q2. What explains Mexico’s slow disinflation since 2023 despite 725 basis points of tightening?

Q3. Why does housing contribute so little to Mexican inflation despite being 18% of the CPI basket?

Q4. How should an emerging-market central bank decompose inflation into supply and demand components?

Q5. What SVAR ordering correctly identifies monetary policy shocks in an emerging market like Mexico?

Q6. What historical episodes in Mexico validate the supply-demand inflation decomposition?

Q7. How do I replicate the rolling-window bivariate VAR sectoral decomposition step by step?

Q8. Where do I get sectoral CPI and quantity proxies for the Mexico decomposition?

Q9. Can this sectoral decomposition be applied to other emerging markets like Brazil, India, or Turkey?

Q10. What does the food-services-housing decomposition imply for Banco de México’s monetary policy strategy?

Data and Code

Citation