## wpiea2019147

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**Canonical URL:** [wpiea2019147](https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019147.pdf)

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---

### Purpose and contributions
- Examines inflation co-movement in a set of 13 countries in Asia, focusing on emerging and developing economies.
- Offers a novel explanation for observed co-movement: common rainfall patterns (the “monsoon effect”).
- Innovations highlighted:
  - Considers determinants of co-movement in monthly inflation rates among 13 countries in Asia.
  - Links global inflation literature to agricultural-price literature by explicitly considering common rainfall shocks as a determinant of inflation co-movement.
  - Distinguishes food and core inflation using a continuous measure of co-movement (the instantaneous quasi correlation) not previously applied to this issue.

### Rationale and motivation
- Existing empirical studies mainly focus on advanced economies where inflation volatility is relatively low and food inflation is a small CPI share; explanations like common monetary policies and improved anchoring of inflation expectations are less relevant for emerging and developing economies.
- For many Asian countries (where food comprises a large part of the consumption basket), drivers of cross-country co-movement in food inflation are of specific interest.
- Not explicitly accounting for weather-related shocks could bias empirical results and lead to misattribution of inflation co-movement to other factors.
- Climate-change-related trends toward more extreme weather events imply weather-related factors will likely play an increasingly important role in shaping economic outcomes and driving inflation.

### Evidence linking rainfall to inflation
- Cites literature showing weather patterns play a key role in determining food inflation and headline inflation:
  - Brown and Kshirsagar (2015); Mitra and Chattopadhyay (2017) on food inflation.
  - Cashin, Mohaddes and Raissi (2017) on El Nino effects on headline inflation.
  - Baffes, Kshirsagar and Mitchell (2017) and Brey and Hertweck (2019) on the stronger role of natural rainfall where use of modern inputs is limited and irrigation shares are lower.
- If two countries face similar weather patterns, harvest yields (and thus food prices) may be more synchronized, especially in countries dependent on natural rainfall.

### Relation to existing literature on international inflation
- Seminal contributions summarized:
  - Auer and Fischer (2010): downward effect of imports from low-wage countries on U.S. prices (using manufacturing industry data).
  - Ciccarelli and Mojon (2010): a common factor explains nearly 70 percent of the variance in inflation rates among a set of 22 OECD countries.
  - Neely and Rapach (2011): world and regional factors account for about half of the inflation variation in a set of 64 countries (mostly high-income); role of these factors has increased over time.
  - Parker (2018): global factors play a smaller role in less developed countries across a broad set of 223 economies, and a greater role in driving energy and food-price inflation.
  - Mumtaz and Surico (2012): among 13 high-income countries, higher productivity growth and migration reduce importance of country factors; common movements in money growth are associated with greater inflation co-movement.
  - Auer, Levchenko and Sauré (2017): international input-output linkages play a major role in synchronizing producer-price inflation across countries.
  - Altansukh and others (2017): for a set of OECD countries, globalization of aggregate inflation is driven by convergence among core-inflation rates.

### Literature and contribution (paper-specific)
- Builds on Auer and Mehrotra (2014): finds producer price and headline consumer price inflation rates are more closely related among countries with higher trade integration in a set of 12 large countries in Asia.
- References Förster and Tillmann (2014): report that two thirds of overall inflation volatility can be explained by country-specific determinants, with an even higher ratio for CPI net of food and energy; only energy price inflation in advanced economies is driven to an important degree by common factors.
- Novel contribution: analyzes headline, core and food-price inflation co-movement for a panel of 13 Asian countries and introduces monthly rainfall data (total monthly rainfall, in millimeters) as a potential common shock (the “monsoon effect”).

### Stylized facts and data
- Sample period for empirical analysis: 2010m1-2016m12.
- Baseline inflation measure: the 3-month moving-average inflation rate derived from the log values of CPI indexes.
- Smoothing parameter for HP filter (monthly data): 130,000.
- Average cross-country correlations (in-text table):
  - Headline CPI Inflation0.31
  - Food CPI Inflation0.20
  - Core CPI Inflation0.05
- Main empirical regularities:
  - Food inflation is more correlated across the 13 Asian countries than core inflation.
  - Food inflation correlations across countries are associated with greater trade integration and with correlation in rainfall patterns (common surplus/deficit deviations from monthly norms).
  - For core inflation there is little discernable relationship between cross-country correlations and trade integration.

### Measurement and methodology
- Co-movement measure: instantaneous quasi-correlation (period-by-period), commonly used in business-cycle synchronicity literature.
- Quasi-correlation formula used:
  - Q C R i,j,t x = (x i,t − x i,t * )(x j,t − x j,t * ) / (σ i x σ j x)
- Equilibrium notion in baseline: country- and variable-specific trend from HP filter (smoothing parameter 130,000). Robustness checks use sample-average equilibrium.
- Panel regression specification (for π ∈ {food; core}):
  - Q C R i j t π = α + β1 (TTRADE i,j,t ) + β2 (Q C R i j t M2) + β2 (Q C R i j t NEER) + β4 (Q C R i j t RAIN_DEV) + γ t + δ i + ε i j t
- Trade integration measure:
  - TTRADE i,j,t = (IMPORT share i,j,t / IMPORTs i,t) + (IMPORT share j,i,t / IMPORTs j,t)
- Independent variables mostly entered as quasi-correlations: money-supply growth (M2), nominal effective exchange rate (NEER) changes, and rainfall deviations from month-specific averages.

### Empirical results — Food inflation co-movement
- Baseline regressions (Table 1, column (1)-(3)) — key coefficients and significance:
  - QC RAIN DEV: 0.118*** (0.042); 0.106** (0.042); 0.096* (0.048)
  - TRADE: 0.249*** (0.075); 0.256*** (0.072); -0.825 (0.971)
  - QC NEER: 0.050*** (0.014); 0.055*** (0.017); 0.046*** (0.017)
  - QC M2: 0.002 (0.015); 0.003 (0.014); 0.006 (0.014)
  - Observations: 3,914 across specifications
  - Number of country pairs: 59
  - R-squared: 0.01, 0.03, 0.03 (columns 1–3 respectively)
- Interpretation highlights:
  - Trade integration is positively and statistically significantly associated with food inflation co-movement in random-effects and time-fixed-effects specifications; the trade coefficient becomes subsumed when country-pair fixed effects are included.
  - NEER co-movement is positively associated with food inflation co-movement across all specifications.
  - Co-movement in rainfall deviations (monsoon effect) is positively associated with food inflation co-movement across specifications; magnitude implication in source:
    - An increase in trade integration of 1 standard deviation (1/10 of the magnitude considered in the table) would increase food-inflation co-movement by about 0.03.
    - A one-unit change in the co-movement of the NEER (approximately a one-standard-deviation shock) implies about a 0.05 change in food-inflation co-movement.
    - A one-unit shock to rainfall co-movement is large (about 4 standard deviations); a one-standard-deviation shock would contribute to about a 0.025 unit change in the average food-inflation quasi-correlation.
  - QC M2 co-movement is not statistically significant for food inflation.

### Empirical results — Core inflation co-movement
- Baseline regressions (Table 2, column (1)-(3)) — key coefficients and significance:
  - QC RAIN DEV: 0.031 (0.042); 0.022 (0.041); 0.027 (0.043)
  - TRADE: -0.059 (0.065); -0.050 (0.069); -0.376 (0.520)
  - QC NEER: 0.036** (0.016); 0.068*** (0.022); 0.070*** (0.022)
  - QC M2: -0.000 (0.014); -0.003 (0.016); -0.004 (0.016)
  - Observations: 1,565 across specifications
  - Number of country pairs: 25
  - R-squared: 0.01, 0.11, 0.11 (columns 1–3 respectively)
- Interpretation highlights:
  - The consistent and lone robust finding: QC NEER is positively associated with core inflation co-movement, indicating exchange-rate passthrough commonality.
  - Rainfall co-movement and trade integration do not show statistically significant relationships with core inflation co-movement.
  - Overall, core inflation co-movement is weaker and largely unexplained by the standard determinants used — suggesting dominance of country-specific (idiosyncratic) factors.

### Robustness checks
- Frequencies tested: month-on-month (mm), 3mma (baseline), 4mma, 6mma.
- Alternative equilibrium notion: sample-average equilibrium (AVG) vs HP trend.
- Food inflation robustness (Table 3) — selected estimates:
  - QC FOOD 4mma (HP): QC RAIN DEV 0.133** (0.061); TRADE 0.314*** (0.081); QC NEER 0.044*** (0.017)
  - QC FOOD 6mma (HP): QC RAIN DEV 0.240 (0.205); TRADE 0.145 (0.091); QC NEER 0.099*** (0.022)
  - QC FOOD mm (HP): QC RAIN DEV 0.003 (0.012); TRADE 0.221*** (0.071); QC NEER 0.016 (0.015)
  - QC FOOD 3mma (AVG): QC RAIN DEV 0.116** (0.054); TRADE 0.306*** (0.079); QC NEER 0.058*** (0.021)
  - Observations vary by frequency (e.g., 3,849; 3,743; 4,082; 3,914)
  - R-squared range for food specifications: 0.03–0.07
- Core inflation robustness (Table 4) — selected estimates:
  - QC CORE 4mma (HP): QC NEER 0.066*** (0.022)
  - QC CORE mm (HP): QC NEER 0.044* (0.024)
  - QC CORE 3mma (AVG): QC NEER 0.068*** (0.023)
  - In the 6mma (HP) specification no variable shows a discernable role.
  - Observations for core specifications vary (e.g., 1,532; 1,476; 1,648; 1,565)
  - R-squared range for core specifications: 0.06–0.14
- Robustness takeaways:
  - The monsoon (rainfall co-movement) effect for food inflation is generally preserved across frequencies but is sensitive to smoothing (loses significance with 6mma and is muted at month-on-month frequency).
  - NEER co-movement remains a robust determinant for both food and core inflation co-movement in many specifications.
  - Trade integration is robust for food inflation at several frequencies but can be subsumed by country-pair fixed effects.

### Conclusions and policy implications
- Empirical conclusions:
  - Common rainfall patterns are associated with higher food-inflation co-movement across emerging and developing Asian economies.
  - Food inflation co-movement is partly related to trade integration and common exchange-rate movements.
  - Core inflation co-movement is weak and not attributable to standard determinants except for exchange-rate co-movement.
- Policy implications:
  - Domestic monetary policy is primarily effective in controlling core—and not food—inflation.
  - Domestic monetary policy needs to be calibrated to domestic inflationary pressures; countries that are highly economically integrated cannot necessarily rely on stable inflation in neighbors to achieve domestic inflation objectives.
  - Recognition that a common external factor (monsoon/rainfall co-movement) can drive food-price spillovers across borders suggests value in regional monitoring and in agricultural/weather-related risk mitigation policies.

### Caveats and avenues for future research
- Limited sample period (2010m1-2016m12, spanning seven years) due to data constraints may leave results sensitive to abnormal events in the sample.
- Lack of data on industrial production or other demand metrics limits control for co-movement in economic circumstances, though time fixed effects and co-movement in M2 growth partly proxy for business-cycle influences.
- Strong co-movement in food prices may be driven by specific sub-components (particular crops); future work could examine crop-level commonality relative to sensitivity to rainfall.
- Weak core inflation co-movement does not imply that price levels do not move together over the medium term, especially between highly integrated countries.

*Source: wpiea2019147 - introduction   of   inflation   targeting. (IMF staff paper content provided in the supplied PDF excerpt).*

### References..............................................................................................................

### wpiea2019147 - References

### Purpose and contributions
- Examines inflation co-movement in a set of 13 countries in Asia, focusing on emerging and developing economies.
- Offers a novel explanation for observed co-movement: common rainfall patterns (the “monsoon effect”).
- Innovations highlighted:
  - Considers determinants of co-movement in monthly inflation rates among 13 countries in Asia.
  - Links global inflation literature to agricultural-price literature by explicitly considering common rainfall shocks as a determinant of inflation co-movement.
  - Distinguishes food and core inflation using a continuous measure of co-movement (the instantaneous quasi correlation) not previously applied to this issue.

### Rationale and motivation
- Existing empirical studies mainly focus on advanced economies where inflation volatility is relatively low and food inflation is a small CPI share; explanations like common monetary policies and improved anchoring of inflation expectations are less relevant for emerging and developing economies.
- For many Asian countries (where food comprises a large part of the consumption basket), drivers of cross-country co-movement in food inflation are of specific interest.
- Not explicitly accounting for weather-related shocks could bias empirical results and lead to misattribution of inflation co-movement to other factors.
- Climate-change-related trends toward more extreme weather events imply weather-related factors will likely play an increasingly important role in shaping economic outcomes and driving inflation.

### Evidence linking rainfall to inflation
- Cites literature showing weather patterns play a key role in determining food inflation and headline inflation:
  - Brown and Kshirsagar (2015); Mitra and Chattopadhyay (2017) on food inflation.
  - Cashin, Mohaddes and Raissi (2017) on El Nino effects on headline inflation.
  - Baffes, Kshirsagar and Mitchell (2017) and Brey and Hertweck (2019) on the stronger role of natural rainfall where use of modern inputs is limited and irrigation shares are lower.
- If two countries face similar weather patterns, harvest yields (and thus food prices) may be more synchronized, especially in countries dependent on natural rainfall.

### Relation to existing literature on international inflation
- Seminal contributions:
  - Auer and Fischer (2010): downward effect of imports from low-wage countries on U.S. prices (using manufacturing industry data).
  - Ciccarelli and Mojon (2010): a common factor explains nearly 70 percent of the variance in inflation rates among a set of 22 OECD countries.
- Neely and Rapach (2011): world and regional factors account for about half of the inflation variation in a set of 64 countries (mostly high-income); role of these factors has increased over time.
- Parker (2018): global factors play a smaller role in less developed countries across a broad set of 223 economies, and a greater role in driving energy and food-price inflation.
- Mumtaz and Surico (2012): among 13 high-income countries, higher productivity growth and migration reduce importance of country factors; common movements in money growth are associated with greater inflation co-movement.
- Auer, Levchenko and Sauré (2017): international input-output linkages play a major role in synchronizing producer-price inflation across countries.
- Altansukh and others (2017): for a set of OECD countries, globalization of aggregate inflation is driven by convergence among core-inflation rates.

*Source: wpiea2019147 - References (excerpt).*

### introduction   of   inflation   targeting.

### introduction   of   inflation   targeting.

### Literature and contribution
- Builds on Auer and Mehrotra (2014): finds producer price and headline consumer price inflation rates are more closely related among countries with higher trade integration in a set of 12 large countries in Asia.
- References Förster and Tillmann (2014): report that two thirds of overall inflation volatility can be explained by country-specific determinants, with an even higher ratio for CPI net of food and energy; only energy price inflation in advanced economies is driven to an important degree by common factors.
- Novel contribution: analyzes headline, core and food-price inflation co-movement for a panel of 13 Asian countries and introduces monthly rainfall data (total monthly rainfall, in millimeters) as a potential common shock (the “monsoon effect”).

### Stylized facts and data
- Sample period for empirical analysis: 2010m1-2016m12.
- Baseline inflation measure: the 3-month moving-average inflation rate derived from the log values of CPI indexes.
- Smoothing parameter for HP filter (monthly data): 130,000.
- Average cross-country correlations (in-text table):
  - Headline CPI Inflation0.31
  - Food CPI Inflation0.20
  - Core CPI Inflation0.05
- Main empirical regularities:
  - Food inflation is more correlated across the 13 Asian countries than core inflation.
  - Food inflation correlations across countries are associated with greater trade integration and with correlation in rainfall patterns (common surplus/deficit deviations from monthly norms).
  - For core inflation there is little discernable relationship between cross-country correlations and trade integration.

### Measurement and methodology
- Co-movement measure: instantaneous quasi-correlation (period-by-period), commonly used in business-cycle synchronicity literature.
- Quasi-correlation formula used:
  - Q C R i,j,t x = (x i,t − x i,t * )(x j,t − x j,t * ) / (σ i x σ j x)
  - Equilibrium notion in baseline: country- and variable-specific trend from HP filter (smoothing parameter 130,000). Robustness checks use sample-average equilibrium.
- Panel regression specification (for π ∈ {food; core}):
  - Q C R i j t π = α + β1 (TTRADE i,j,t ) + β2 (Q C R i j t M2) + β2 (Q C R i j t NEER) + β4 (Q C R i j t RAIN_DEV) + γ t + δ i + ε i j t
  - Trade integration measure:
    - TTRADE i,j,t = (IMPORT share i,j,t / IMPORTs i,t) + (IMPORT share j,i,t / IMPORTs j,t)  (as defined in the source)
- Independent variables mostly entered as quasi-correlations: money-supply growth (M2), nominal effective exchange rate (NEER) changes, and rainfall deviations from month-specific averages.

### Empirical results — Food inflation co-movement
- Baseline regressions (Table 1, column (1)-(3)) — key coefficients and significance:
  - QC RAIN DEV: 0.118*** (0.042); 0.106** (0.042); 0.096* (0.048)
  - TRADE: 0.249*** (0.075); 0.256*** (0.072); -0.825 (0.971)
  - QC NEER: 0.050*** (0.014); 0.055*** (0.017); 0.046*** (0.017)
  - QC M2: 0.002 (0.015); 0.003 (0.014); 0.006 (0.014)
  - Observations: 3,914 across specifications
  - Number of country pairs: 59
  - R-squared: 0.01, 0.03, 0.03 (columns 1–3 respectively)
- Interpretation highlights:
  - Trade integration is positively and statistically significantly associated with food inflation co-movement in random-effects and time-fixed-effects specifications; the trade coefficient becomes subsumed when country-pair fixed effects are included.
  - NEER co-movement is positively associated with food inflation co-movement across all specifications.
  - Co-movement in rainfall deviations (monsoon effect) is positively associated with food inflation co-movement across specifications; magnitude implication in source:
    - An increase in trade integration of 1 standard deviation (1/10 of the magnitude considered in the table) would increase food-inflation co-movement by about 0.03.
    - A one-unit change in the co-movement of the NEER (approximately a one-standard-deviation shock) implies about a 0.05 change in food-inflation co-movement.
    - A one-unit shock to rainfall co-movement is large (about 4 standard deviations); a one-standard-deviation shock would contribute to about a 0.025 unit change in the average food-inflation quasi-correlation.
  - QC M2 co-movement is not statistically significant for food inflation.

### Empirical results — Core inflation co-movement
- Baseline regressions (Table 2, column (1)-(3)) — key coefficients and significance:
  - QC RAIN DEV: 0.031 (0.042); 0.022 (0.041); 0.027 (0.043)
  - TRADE: -0.059 (0.065); -0.050 (0.069); -0.376 (0.520)
  - QC NEER: 0.036** (0.016); 0.068*** (0.022); 0.070*** (0.022)
  - QC M2: -0.000 (0.014); -0.003 (0.016); -0.004 (0.016)
  - Observations: 1,565 across specifications
  - Number of country pairs: 25
  - R-squared: 0.01, 0.11, 0.11 (columns 1–3 respectively)
- Interpretation highlights:
  - The consistent and lone robust finding: QC NEER is positively associated with core inflation co-movement, indicating exchange-rate passthrough commonality.
  - Rainfall co-movement and trade integration do not show statistically significant relationships with core inflation co-movement.
  - Overall, core inflation co-movement is weaker and largely unexplained by the standard determinants used — suggesting dominance of country-specific (idiosyncratic) factors.

### Robustness checks
- Frequencies tested: month-on-month (mm), 3mma (baseline), 4mma, 6mma.
- Alternative equilibrium notion: sample-average equilibrium (AVG) vs HP trend.
- Food inflation robustness (Table 3) — selected estimates:
  - QC FOOD 4mma (HP): QC RAIN DEV 0.133** (0.061); TRADE 0.314*** (0.081); QC NEER 0.044*** (0.017)
  - QC FOOD 6mma (HP): QC RAIN DEV 0.240 (0.205); TRADE 0.145 (0.091); QC NEER 0.099*** (0.022)
  - QC FOOD mm (HP): QC RAIN DEV 0.003 (0.012); TRADE 0.221*** (0.071); QC NEER 0.016 (0.015)
  - QC FOOD 3mma (AVG): QC RAIN DEV 0.116** (0.054); TRADE 0.306*** (0.079); QC NEER 0.058*** (0.021)
  - Observations vary by frequency (e.g., 3,849; 3,743; 4,082; 3,914)
  - R-squared range for food specifications: 0.03–0.07
- Core inflation robustness (Table 4) — selected estimates:
  - QC CORE 4mma (HP): QC NEER 0.066*** (0.022)
  - QC CORE mm (HP): QC NEER 0.044* (0.024)
  - QC CORE 3mma (AVG): QC NEER 0.068*** (0.023)
  - In the 6mma (HP) specification no variable shows a discernable role.
  - Observations for core specifications vary (e.g., 1,532; 1,476; 1,648; 1,565)
  - R-squared range for core specifications: 0.06–0.14
- Robustness takeaways:
  - The monsoon (rainfall co-movement) effect for food inflation is generally preserved across frequencies but is sensitive to smoothing (loses significance with 6mma and is muted at month-on-month frequency).
  - NEER co-movement remains a robust determinant for both food and core inflation co-movement in many specifications.
  - Trade integration is robust for food inflation at several frequencies but can be subsumed by country-pair fixed effects.

### Conclusions and policy implications
- Empirical conclusions:
  - Common rainfall patterns are associated with higher food-inflation co-movement across emerging and developing Asian economies.
  - Food inflation co-movement is partly related to trade integration and common exchange-rate movements.
  - Core inflation co-movement is weak and not attributable to standard determinants except for exchange-rate co-movement.
- Policy implications:
  - Domestic monetary policy is primarily effective in controlling core—and not food—inflation.
  - Domestic monetary policy needs to be calibrated to domestic inflationary pressures; countries that are highly economically integrated cannot necessarily rely on stable inflation in neighbors to achieve domestic inflation objectives.
  - Recognition that a common external factor (monsoon/rainfall co-movement) can drive food-price spillovers across borders suggests value in regional monitoring and in agricultural/weather-related risk mitigation policies.

### Caveats and avenues for future research
- Limited sample period (2010m1-2016m12, spanning seven years) due to data constraints may leave results sensitive to abnormal events in the sample.
- Lack of data on industrial production or other demand metrics limits control for co-movement in economic circumstances, though time fixed effects and co-movement in M2 growth partly proxy for business-cycle influences.
- Strong co-movement in food prices may be driven by specific sub-components (particular crops); future work could examine crop-level commonality relative to sensitivity to rainfall.
- Weak core inflation co-movement does not imply that price levels do not move together over the medium term, especially between highly integrated countries.

*Source: wpiea2019147 - introduction   of   inflation   targeting. (IMF staff paper content provided in the supplied PDF excerpt)*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019147.pdf_