## Reconsidering the Role of Food Prices in Inflation

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### Abstract — main findings
- Food prices are generally excluded from measures of inflation most closely watched by policymakers due either to their transitory nature or their higher volatility.
- In lower income countries:
  - Food price inflation is not only more volatile but also on average higher than nonfood inflation.
  - Food inflation is in many cases more persistent than nonfood inflation.
  - Shocks in many countries are propagated strongly into nonfood inflation.
- Under these conditions, and particularly given high global commodity price inflation in recent years, focusing on core measures that exclude food prices can:
  - Mis-specify inflation.
  - Lead to higher inflationary expectations.
  - Introduce a downward bias to forecasts of future inflation.
  - Produce lags in policy responses.
- Recommendation: In constructing measures of core inflation, policymakers should not assume that excluding food price inflation will provide a clearer picture of underlying inflation trends than headline inflation.

### I. Purpose, context, and literature implications
- Purpose: Assess the appropriateness of minimizing or excluding food inflation in measures of core inflation, particularly in developing countries.
- Context and motivation:
  - Core inflation indices in many advanced economies exclude volatile categories, often food and energy.
  - Statistical properties and relationships between food and nonfood prices differ substantially between advanced and developing economies.
  - The global commodity price runup during 2003-2007 pushed nonfood inflation higher in many countries; after the global financial crisis food prices resumed upward trends.
- Literature implications:
  - Findings in advanced-economy studies are mixed on food persistence and on the efficacy of excluding food from core measures (summarized conceptual findings from Bryan and Cecchetti (1993), Cutler (2001), Bilke and Stracca (2008), Cecchetti (2007), Rich and Steindel (2007), Alvarez et al. (2005), Cecchetti and Moessner (2008), Catão and Chang (2010), Anand and Prasad (2010)).
  - Implication: Core-inflation literature focused on rich countries does not automatically extend to developing economies.

### II. Data and measurement
- Sample: Consumer price indices (CPI) for 91 countries.
- Definitions:
  - Headline CPI index used.
  - Food index: the widest definition of food used in the country, excluding alcoholic beverages where this distinction is made; when subindex is “Food and Beverages” this is used.
  - Nonfood CPI calculated from total CPI, the weight of food in the CPI, and the food CPI.
- Frequency and calculations:
  - Inflation is calculated month-over-month.
  - Other macroeconomic data drawn from the IMF’s World Economic Outlook database.
- Special cases and exclusions:
  - For India, WPI data are used as the available monthly headline index.
  - Energy prices are excluded to preserve comparability across countries and avoid heterogeneity from government-administered energy prices.
- Seasonal adjustment:
  - Non-seasonally adjusted data are used.

### III. Relative levels, volatility, and skewness (empirical facts)
- Relative levels:
  - Food inflation runs higher than nonfood inflation in a majority of sample countries; countries with higher average food inflation comprise two thirds of the sample.
  - The difference between food and nonfood inflation is statistically significant at the 10 percent level in only 20 percent of the countries.
  - Differences are negligible among almost all high income countries; substantial differences appear mainly among poorer countries.
- Volatility and signal-to-noise:
  - Food price inflation is more volatile than nonfood price inflation in 65 of the 71 countries in the sample.
  - Higher volatility of food inflation lowers the signal-to-noise ratio, which supports excluding food when the objective is to maximize that ratio.
  - Countervailing risk: greater volatility implies that transmitted shocks from food to nonfood inflation can be large and not visible in a nonfood core measure until after transmission occurs.
- Skewness:
  - Both food and nonfood price inflation tend to be right-skewed.
  - For nonfood inflation, skewness becomes smaller at higher income levels; poorer countries are more likely to have large positive nonfood shocks.
  - For food inflation, skewness is relatively constant across income levels.
  - Implication: under trimmed-means methodologies (Bryan and Cecchetti 1993), greater skewness of food diminishes its weight, but among poorer countries skewness differences are less pronounced and trimmed-means may not drastically reduce food weight.

### IV. Persistence: measurement framework and core results
- Estimation framework:
  - Baseline AR(q) estimated for each country and for food and nonfood inflation with q ranging between 1 and 9 lags; model chosen by highest AIC.
  - AR(q) models estimated via standard maximum likelihood ARIMA technique.
  - Three persistence measures used:
    1. SARC: Sum of Autoregressive Coefficients.
    2. LAR: Largest Autoregressive Root.
    3. HL: Half Life (impulse response half life in months).
- SARC results (Table 1 CPICPI-FCPI-N):
  - Mean: 0.194 0.182 0.021
  - Median: 0.274 0.243 0.000
  - Standard Deviation: 0.558 0.494 0.556
  - Skewness: -1.066 -1.668 -1.573
  - Kurtosis: 1.753 3.412 6.594
- SARC interpretation:
  - Persistence in nonfood prices is far less on average; more countries have negative SARC sums for nonfood prices, while positive SARC sums for food prices tend to be much higher.
  - Advanced economies have the least persistent nonfood shocks; persistence of nonfood shocks can be negative in advanced economies, consistent with central bank reversal.
  - Overall CPI is more persistent than either food or nonfood CPI, consistent with weighted averaging and transmission.
- LAR results (Table 2 CPICPI-FCPI-N):
  - Mean: 0.425 0.438 0.347
  - Median: 0.448 0.442 0.356
  - Standard Deviation: 0.304 0.239 0.288
  - Skewness: -0.585 -1.170 -0.527
  - Kurtosis: 0.240 2.801 0.729
- LAR interpretation:
  - Mean and median LARs for food inflation are higher than for nonfood inflation; mean LAR for overall CPI is higher than both subcomponents.
  - None of the series have unit roots, though some largest roots are relatively high.
  - Countries with LAR estimates for food inflation greater than 0.75: Ecuador, Mexico and South Africa.
  - Countries with LAR estimates for nonfood inflation above 0.75: Bulgaria, Colombia, Iran, Pakistan, Poland, Romania and Turkey.
  - Persistence of both food and nonfood inflation falls with income.
  - Correlations between SARC and LAR: CPI 83 percent, food inflation 70 percent, nonfood inflation 61 percent.
- HL results (Table 3 CPICPI-FCPI-N):
  - Mean: 3.967 3.033 0.915
  - Median: 2.000 1.000 0.000
  - Standard Deviation: 10.455 7.315 12.534
  - Skewness: 8.713 7.425 3.677
  - Kurtosis: 80.471 61.974 45.570
- HL interpretation:
  - Mean and median half-lives of shocks to food CPI exceed those of nonfood CPI.
  - In 53 countries (58 percent of the sample), the half life of a shock to nonfood inflation is only one month.
  - For food inflation, the half life is one month in 35 countries (38 percent of the sample).
  - For one third of countries, half the effect of a unit shock to food prices has not dissipated after two months; for nonfood prices this is true in only one fifth of the sample.
  - Half-lives are strongly related to income: higher income countries mostly have half-lives bunched below 6 months; middle-income countries show a much wider range, especially for food prices.
- Overall persistence conclusion:
  - Across SARC, LAR and HL, persistence of food price shocks is nontrivial in many countries.
  - Persistence of both food and nonfood inflation is generally higher in lower income countries and falls with income.

### V. Transmission of food shocks to nonfood inflation (VAR/IRF evidence)
- Method: Two-equation VAR for food and nonfood prices; impulse response functions (IRFs) derived from jointly estimated system.
- Heterogeneity: IRFs are very heterogeneous across countries.
- Average impulse-response magnitudes:
  - Average standard deviation of impulse responses:
    - Poorer half of the sample: 0.043
    - Richer half of the sample: 0.038
- Own-shock long-run responses:
  - Nonfood price shocks:
    - Rich countries: a one percent shock → increases nonfood price level by one percent in the long run.
    - Poor countries: a one percent nonfood shock → increases nonfood price level by almost 2.5 percent in the long run.
  - Food price shocks:
    - Rich countries: a one percent food price shock → long run effect ≈ 0.8 percent.
    - Poor countries: a one percent food price shock → long run effect ≈ 0.9 percent.
- Cross effects:
  - Shocks to nonfood prices affect food prices; these effects are stronger among rich countries than among poor countries.
  - Shocks from food to nonfood:
    - Quantified average effects:
      - Rich countries: a one percent shock to food prices → results in a 0.15 percent increase in nonfood prices on average.
      - Poor countries: the average effect is around 0.3 percent.
  - Likely mechanism: greater capital content of food in rich countries can amplify nonfood→food transmission; conversely, in poorer countries food shocks more strongly propagate into nonfood.

### VI. Policy-relevant conclusions and recommendations
- Core-measure validity:
  - A core measure must have the same medium-term mean as the headline measure. Food inflation is in many countries higher than nonfood inflation, so excluding food is likely to understate medium-term inflation—especially in poorer countries.
- Persistence and transmittal:
  - Excluding food because it is perceived as transitory is often unjustified; food inflation is in many cases quite persistent—often more so than nonfood inflation—in poorer countries where food is a large consumption share.
  - Food inflation is transmitted into nonfood inflation significantly, particularly in developing economies.
- Risks of exclusion:
  - High volatility and right skew of food prices increase the risk of underestimating medium-term inflation if food is excluded and transmission is strong.
  - Excluding faster-moving components can create a downward bias in current-inflation estimates if non-core inflation rises faster than core inflation over a sustained period.
- Practical guidance for policymakers:
  - Do not automatically minimize or exclude food inflation from core measures, especially in developing economies where food inflation can be higher and more persistent and where food has a larger weight in the consumption basket.
  - When constructing core inflation measures, policy authorities should:
    - Evaluate whether food inflation’s long-run mean differs from nonfood inflation.
    - Assess the volatility and persistence of food shocks and their transmission to nonfood prices.
    - Consider broader CPI targeting or alternative core measures that account for persistence and mean-level differences, particularly where household welfare and credit constraints make food-price changes economically salient.
  - Central-bank actions:
    - Where transmission of food shocks into nonfood prices is strong, central banks may need to act earlier in a tightening cycle.
    - Given food’s volatility, central banks—especially with weak monetary transmission—should avoid overreacting to transitory shocks.
    - Monitoring the speed of transmission from food to nonfood inflation helps identify potential incipient rises in nonfood inflation originating from food-price shocks.
- Research note: Analyzing how these dynamics vary with exchange rate and monetary policy regimes is an important area for further research.

*Prepared by James P. Walsh. WP/11/71. April 2011.*

### Section 1

### Reconsidering the Role of Food Prices in Inflation

### Abstract — main findings
- Food prices are generally excluded from measures of inflation most closely watched by policymakers due either to their transitory nature or their higher volatility.
- In lower income countries:
  - Food price inflation is not only more volatile but also on average higher than nonfood inflation.
  - Food inflation is in many cases more persistent than nonfood inflation.
  - Shocks in many countries are propagated strongly into nonfood inflation.
- Under these conditions, and particularly given high global commodity price inflation in recent years, focusing on core measures that exclude food prices can:
  - Mis-specify inflation.
  - Lead to higher inflationary expectations.
  - Introduce a downward bias to forecasts of future inflation.
  - Produce lags in policy responses.
- Recommendation: In constructing measures of core inflation, policymakers should not assume that excluding food price inflation will provide a clearer picture of underlying inflation trends than headline inflation.

### I. Introduction and background — purpose and context
- Purpose: Assess the appropriateness of minimizing or excluding food inflation in measures of core inflation, particularly in developing countries.
- Context and motivation:
  - Core inflation indices in many advanced economies minimize or eliminate volatile categories, often excluding food and energy.
  - The statistical properties and relationships between food and nonfood prices may differ substantially between advanced and developing economies.
  - The global commodity price runup during 2003-2007 pushed nonfood inflation higher in many countries, highlighting the potential for food price shocks to affect broader inflation.
  - After the global financial crisis, food prices resumed upward trends, renewing the policy question of how to treat food price inflation.

### Literature on core inflation and relevance to food prices
- Core-measure concepts and findings:
  - Bryan and Cecchetti (1993): Advocate truncation of the distribution of component price changes to maximize signal-to-noise; food and energy tend to assume less importance ex post due to higher volatility and skewness.
  - Cutler (2001): Emphasizes persistence; weights components by relative persistence. Finds low weight on energy and seasonal food, but high weight on nonseasonal food in the UK because those food prices are relatively persistent.
  - Bilke and Stracca (2008): For the Euro Area, find food prices are relatively persistent, producing a higher weight on food than in the headline CPI.
  - Cecchetti (2007): Warns that excluding food and energy can produce a less effective focus than headline inflation if non-core inflation rises faster than core inflation over a sustained period, creating a downward bias.
  - Rich and Steindel (2007): Assess U.S. measures and find the aggregate excluding food and energy performs weakly as a predictive core series.
  - Alvarez et al. (2005): Find food prices in both the Eurozone and U.S. are less persistent than nonfood prices, with European prices more persistent overall.
  - Cecchetti and Moessner (2008): Find that since 2003, headline inflation in many countries is not reverting to core to the same degree, suggesting secular increases in commodity prices may affect nonfood prices.
  - Catão and Chang (2010): Argue that food’s role in household utility and high food-price volatility strengthen the case for targeting a broad CPI rather than an ad hoc core index; offsetting some food-price changes can be welfare-enhancing.
  - Anand and Prasad (2010): Conclude that with credit-constrained consumers, a narrow focus on nonfood inflation can lead to suboptimal outcomes.
- Implication: Much of the core-inflation literature is focused on rich countries; the conclusions do not automatically extend to developing economies.

### II. Data — sample and measurement choices
- Sample: Consumer price indices (CPI) for 91 countries.
- Definitions:
  - Headline CPI index used.
  - Food index: the widest definition of food used in the country, excluding alcoholic beverages where this distinction is made; when subindex is “Food and Beverages” this is used.
  - Nonfood CPI calculated from total CPI, the weight of food in the CPI, and the food CPI.
- Frequency and calculations:
  - Inflation is calculated month-over-month.
  - Other macroeconomic data drawn from the IMF’s World Economic Outlook database.
- Special cases and exclusions:
  - For India, WPI data are used as this is the only national price index with a monthly frequency available and it is the headline index most commonly cited by the central bank and in the press.
  - Energy prices are excluded from the analysis for these reasons:
    - Constructing comparable energy price indices across countries is less straightforward than for food.
    - Not every country publishes an energy category; classification differences (e.g., gasoline) exist.
    - Government administration of energy prices is more widespread than that of food prices, especially in developing economies, producing heterogeneity in persistence.
    - Including energy would complicate cross-country comparisons and potentially bias inclusion criteria if persistence alone were used.
- Seasonal adjustment:
  - Non-seasonally adjusted data are used.
  - Not all countries publish seasonally adjusted CPI component data; mixing SA and NSA would reduce volatility for one group relative to another.
  - Blanket seasonal adjustments (e.g., X12) are inappropriate given heterogeneous seasonal patterns (e.g., Ramadan, Lunar New Year) across countries.

### III. Food inflation versus nonfood inflation — conceptual tests and statistical concerns
- Two critical considerations when evaluating exclusion of food from core inflation:
  1. Long-run mean comparison:
     - Excluding food is justified only if the long-run mean of food inflation equals the long-run mean of nonfood inflation.
     - If food’s long-run mean differs, excluding it will systematically bias core measures and underestimate headline inflation (Cecchetti 2007).
  2. Volatility of shocks:
     - If food price shocks are more volatile than nonfood shocks, they raise the noise-to-signal ratio, complicating identification of underlying trends.
     - Larger and more frequent food shocks increase the likelihood of misdiagnosing underlying inflation and increase the chance of transmission to nonfood prices.
- Transmission mechanism:
  - In some economies, food-price shocks propagate strongly into nonfood inflation; this propagation is not universal and must be empirically assessed.
  - If food shocks are small, long-run effects will likely be minimal; if large and volatile, they can materially affect nonfood inflation and the overall price level.

### Policy implications and recommendations (from the analysis and cited literature)
- Policymakers should not automatically minimize or exclude food inflation from core measures, especially in developing economies where:
  - Food inflation can be higher and more persistent than nonfood inflation.
  - Food has a larger weight in the consumption basket.
  - Transmission from food to nonfood inflation is significant.
- When constructing core inflation measures, policy authorities should:
  - Evaluate whether food inflation’s long-run mean differs from nonfood inflation.
  - Assess the volatility and persistence of food shocks and their transmission to nonfood prices.
  - Recognize that excluding faster-moving components can create a downward bias in current-inflation estimates if non-core inflation rises faster than core inflation over a sustained period.
  - Consider broader CPI targeting or alternative core measures that account for persistence and mean-level differences, particularly where household welfare and credit constraints make food-price changes economically salient.

*Prepared by James P. Walsh. WP/11/71. April 2011.*

### Section 2

### _wp1171 - Section 2

### Relative levels of food and nonfood inflation
- Food inflation runs higher than nonfood inflation in a majority of sample countries; countries with higher average food inflation comprise two thirds of the sample.
- The difference between food and nonfood inflation is statistically significant at the 10 percent level in only 20 percent of the countries.
- Differences are negligible among almost all high income countries; substantial differences appear mainly among poorer countries.
- Interpretation points from the text:
  - A higher mean of food inflation implies food prices will rise relative to nonfood prices over time.
  - In rapidly growing low income countries, rising incomes can push up food prices relative to other goods.
  - As countries become richer, the differential should narrow as tastes shift away from food and retail food-price composition shifts toward labor and other costs.
  - Other plausible causes include the relatively low tradability of non-staple foods, the labor intensity of agriculture, and the Balassa-Samuelson effect.

### Volatility and signal-to-noise considerations
- Food price inflation is more volatile than nonfood price inflation in 65 of the 71 countries in the sample.
- Higher volatility of food inflation lowers the signal-to-noise ratio, supporting exclusion of food prices from core indices aimed at maximizing that ratio.
- However, greater volatility implies transmitted shocks from food to nonfood inflation can in some cases be quite large; if propagation mechanisms are strong, large food shocks can yield large upward shifts in nonfood prices not visible in a nonfood core measure until transmission has occurred.
- Figure/statistics references in text:
  - Standard deviations and histograms (Figures 2 and 4) show food inflation and innovations to food inflation are more volatile than their nonfood counterparts.

### Skewness of inflation distributions
- Both food and nonfood price inflation tend to be right-skewed (unusually large positive shocks more common than unusually small ones).
- For nonfood inflation, skewness becomes smaller at higher income levels; poorer countries are more likely to have large positive nonfood shocks.
- For food inflation, skewness is relatively constant across income levels; food prices tend to have larger positive shocks than negative ones in both rich and poor countries.
- Implication for trimmed-means core measures: under the Bryan and Cechetti (1993) methodology, greater skewness of food and energy reduces their weights in trimmed-means measures. Among poorer countries, the skewness difference is less pronounced, so trimmed-means could produce very different results and may not drastically reduce the weight of food prices.

### Innovations to inflation (second derivative)
- The standard deviations of changes in inflation (innovations) show that innovations to food inflation are more volatile than innovations to nonfood inflation.
- Consequence: if transmission to nonfood prices is strong, volatile and right-skewed food shocks in low income countries can feed strongly into nonfood inflation.

### Summary comparative statement
- On average and especially in lower income countries, food inflation tends to be:
  - higher,
  - more volatile,
  - skewed to the right,
  - and the changes to food inflation over time are themselves more volatile than changes to nonfood inflation.

### Persistence: rationale and implications
- Persistence of shocks matters because longer-lived shocks keep inflation elevated longer and extend the window for transmission from food to nonfood prices.
- If food price shocks are short-lived, policymakers may discount them; if persistent, they can feed into inflationary expectations and require earlier policy action.

### Persistence measurement framework
- Baseline autoregressive equation estimated for each country and for food and nonfood inflation with q ranging between 1 and 9 lags; the model chosen is the one with the highest Akaike Information Criterion (AIC).
- AR(q) models estimated via standard maximum likelihood ARIMA technique.
- Three persistence measures are used:
  1. SARC: Sum of Autoregressive Coefficients.
  2. LAR: Largest Autoregressive Root.
  3. HL: Half Life (impulse response half life in months).

### SARC (Sum of Autoregressive Coefficients) findings
- SARC results (Table 1 CPICPI-FCPI-N):
  - Mean: 0.194 0.182 0.021
  - Median: 0.274 0.243 0.000
  - Standard Deviation: 0.558 0.494 0.556
  - Skewness: -1.066 -1.668 -1.573
  - Kurtosis: 1.753 3.412 6.594
- Findings and interpretation:
  - Persistence in nonfood prices is far less on average: more countries have negative SARC sums for nonfood prices, and positive SARC sums for food prices tend to be much higher.
  - SARC estimates have high standard deviations and negative outliers.
  - Advanced economies have the least persistent nonfood shocks; persistence of nonfood shocks can be negative in advanced economies, consistent with central banks acting to reverse nonfood shocks.
  - Policy implication: in middle- and low-income countries, improving monetary outcomes might involve reacting more forcefully to nonfood shocks and possibly to food price shocks given their greater risk to nonfood inflation.
  - Overall CPI is more persistent than either food or nonfood CPI, consistent with CPI being a weighted average and with transmission between subindices.

### LAR (Largest Autoregressive Root) findings
- LAR results (Table 2 CPICPI-FCPI-N):
  - Mean: 0.425 0.438 0.347
  - Median: 0.448 0.442 0.356
  - Standard Deviation: 0.304 0.239 0.288
  - Skewness: -0.585 -1.170 -0.527
  - Kurtosis: 0.240 2.801 0.729
- Findings:
  - Mean and median LARs for food inflation are higher than for nonfood inflation; mean LAR for overall CPI is higher than both subcomponents.
  - None of the series have unit roots, though some largest roots are relatively high.
  - Three countries have LAR estimates for food inflation greater than 0.75: Ecuador, Mexico and South Africa.
  - Seven countries have LAR estimates for nonfood inflation above 0.75: Bulgaria, Colombia, Iran, Pakistan, Poland, Romania and Turkey.
  - Persistence of both food and nonfood inflation falls with income (Figure 8).
  - Correlations between SARC and LAR: CPI 83 percent, food inflation 70 percent, nonfood inflation 61 percent.

### HL (Half Life) findings
- HL results (Table 3 CPICPI-FCPI-N):
  - Mean: 3.967 3.033 0.915
  - Median: 2.000 1.000 0.000
  - Standard Deviation: 10.455 7.315 12.534
  - Skewness: 8.713 7.425 3.677
  - Kurtosis: 80.471 61.974 45.570
- Findings:
  - Mean and median half-lives of shocks to food CPI exceed those of nonfood CPI.
  - In most countries both food and nonfood inflation revert quickly; half lives are often small.
  - In 53 countries (58 percent of the sample), the half life of a shock to nonfood inflation is only one month.
  - For food inflation, the half life is one month in 35 countries (38 percent of the sample).
  - For one third of countries, half the effect of a unit shock to food prices has not dissipated after two months; for nonfood prices this is true in only one fifth of the sample.
  - Half-lives are strongly related to income: higher income countries mostly have half-lives bunched below 6 months; middle-income countries show a much wider range, especially for food prices.

### Overall persistence conclusion
- Across all three measures (SARC, LAR, HL), persistence of food price shocks is nontrivial in many countries.
- Persistence of both food and nonfood inflation is generally higher in lower income countries and falls with income.

*Source: _wp1171 - Section 2*

### Section 3

### _wp1171 - Section 3

### Transmission of Food Price Shocks to Nonfood Inflation
- The transmission of shocks between food and nonfood inflation may differ between poor and rich countries; policy efficiency depends on whether food supply shocks propagate into nonfood prices.
- To assess transmission, a two-equation vector autoregression (VAR) for food and nonfood prices is estimated and impulse response functions (IRFs) are derived.
- The equations are estimated jointly, using standard maximum likelihood techniques for seemingly unrelated equations.

### Impulse Response Findings
- The derived IRFs are very heterogeneous across countries.
- Effect of food price shocks on nonfood prices:
  - The first-round effect is much larger in the poorer countries than in the richer ones.
  - The declining cumulative IRF shows that while some difference is eliminated over time in rich countries, a large difference persists.
  - Average standard deviation of impulse responses:
    - Poorer half of the sample: 0.043
    - Richer half of the sample: 0.038
- Response of each index to its own shock:
  - Nonfood price shocks:
    - In the long run, a one percent shock to nonfood prices increases the nonfood price level by one percent in the rich countries.
    - In the poorer half of the sample, a one percent nonfood shock increases the nonfood price level by almost 2.5 percent.
  - Food price shocks:
    - Long run effect of a one percent food price shock in rich countries: around 0.8 percent.
    - Long run effect of a one percent food price shock in poor countries: around 0.9 percent.
- Cross effects (nonfood → food):
  - Shocks to nonfood prices affect food prices; these effects are stronger among rich countries than among poor countries.
  - Likely explanation: greater capital content of food in rich countries (e.g., electricity, construction costs affecting grocery-store goods more than market goods).

### Policy-Relevant Conclusions and Implications
- Eliminating food prices from core inflation may provide an incorrect picture of underlying inflation trends, especially in low income countries, for three primary reasons:
  - First: A core measure must have the same medium-term mean as the headline measure. Food inflation is in many countries higher than nonfood inflation, so excluding food is likely to show lower inflation even in the long run; this is of particular concern among poorer countries.
  - Second: Excluding food prices due to perceived transience is often unjustified. Food inflation is in many cases quite persistent—often more so than nonfood prices—and this is particularly pronounced in poorer countries where food is a large share of the consumption basket. Slow dissipation of food shocks can raise expectations for overall inflation even if core measures are unaffected.
  - Third: Food inflation is transmitted into nonfood inflation significantly, particularly in developing economies. Quantified example:
    - In rich countries, a one percent shock to food prices on average results in a 0.15 percent increase in nonfood prices.
    - In poor countries, the average effect is around 0.3 percent.
- The high volatility and right skew of food prices aggravate underestimation risks: large food price shocks are more likely than large nonfood shocks, so discounting food developments where transmission to nonfood is strong or quick can underestimate medium-term effects.
- Implications for central banks:
  - Core inflation indices that minimize or eliminate food prices are likely to be misspecified in many developing economies.
  - Policymakers should return to first principles when constructing core price indices for assessing medium-term developments; it is not clear that reducing weights on volatile or transient components will necessarily underweight food inflation.
  - Where transmission of food shocks into nonfood prices is strong, central banks should be aware of the broader impact of food prices and may need to act earlier in a tightening cycle. However:
    - Food inflation is quite volatile; central banks, particularly in countries with weak mechanisms for monetary transmission, should avoid overreacting to transitory shocks by tightening prematurely.
    - The appropriate response depends on a central bank’s broader exchange rate and monetary policy regimes; analyzing how the described dynamics vary with policy regimes is an important area for further research.
  - Monitoring how rapidly food price shocks impact nonfood inflation can help central banks be vigilant about stemming an incipient rise in nonfood inflation that could be spilling over from food prices.

*Source: _wp1171 - Section 3*

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