## wp17196

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### Channels and estimation method
- Headline CPI decomposition: P_t = P_t^N w_t^δ, where w_t = P_t^O / P_t^N and δ is the share of oil in the CPI basket.
- Log-difference identity: π_t = π_t^N + δ ∆log w_t.
- Two transmission channels:
  - Direct effect: δ ∆log w_t (changes in the log ratio of oil to non-oil prices scaled by oil share δ).
  - Indirect effect: via changes in non-oil (core) inflation π_t^N.
- Estimation approach:
  - Local projections (Jordà, 2005) estimated on annual data for horizons k = 0,..3:
    - π_{i,t+k} = α_i^k + ν_t^k + Σ_{j=1}^l γ_j^k π_{i,t−j} + β^k δ_{i,t−1} π_{t}^{oil} + Σ_{j=0}^{k} θ_j δ_{i,t+j−1} π_{t+j}^{oil} + ε_{i,t}^k
  - Key variables:
    - π = domestic CPI inflation; π_t^{oil} = global oil inflation; δ_{i,t−1} = oil share proxy (transport share in CPI); α_i^k = country fixed effects; ν_t^k = time fixed effects.
  - Forward leads of global oil inflation included to correct bias (Teulings and Zubanov, 2014).
- Identification and heterogeneity controls:
  - Interaction with lagged oil share δ_{i,t−1} identifies average effects while controlling for cross-country heterogeneity and time-fixed effects.
  - Lag structure: baseline lags l = 2; horizons k = 0, 1, 2, 3. Results robust to lag choice.
- IRF construction and inference:
  - IRFs = estimated β^k rescaled by sample average oil share δ̄ (multiply β^k by δ̄).
  - Confidence bands from standard deviations of estimated coefficients.
- Small-sample bias:
  - Nickell bias from lagged dependent variable with fixed effects noted; finite-sample bias of order 1/T with average T = 40 in baseline sample.

### Baseline results and robustness
- Main panel baseline (1970-2015, unbalanced panel of 72 countries):
  - A 10 percent increase in global oil price typically increases domestic inflation by 0.4 percentage point in the year of the shock.
  - Effect becomes statistically insignificant two years after the shock.
  - Many oil shock episodes involve increases of 50 percent or more; pass-through economically significant.
- Robustness checks:
  - Balanced sample (57 countries): results very similar to baseline.
  - Alternative oil-share proxy (fuel import share): medians —
    - Advanced economies: 12.6 percent (transport share) and 14.1 percent (fuel import share).
    - Developing economies: 13.6 percent (transport share) and 11.6 percent (fuel import share).
  - Fuel import share-based IRFs similar to baseline.
- Endogeneity and alternative estimators:
  - Addressed via: no country fixed-effects specification; two-step system GMM with up to four lags; dependent-variable re-specification πi,t − πt_oil; panel-VAR with Cholesky ordering (global oil inflation first) and lag length two.
  - Alternative specifications produce estimates similar to baseline.
- Role of core inflation:
  - Sample for core inflation: 45 countries.
  - Effect on core inflation considerably smaller and less persistent than on headline; core contributes about one-third of overall headline effect.
- Temporal change:
  - Subsample 1970-1992 vs 1993-2015: impact of oil prices on inflation declined.
  - Effect more than three times larger in 1970-1992 than in 1993-2015.
  - Drivers examined: (i) absence of significant oil shocks in the 1990s, (ii) declining oil share in consumption, (iii) structural changes (wage flexibility), (iv) increased credibility of monetary policy.
  - Kilian (2009) decomposition: sizes of supply, demand, and oil-specific demand shocks similar across subperiods — changes in shock sizes unlikely explanation.
  - Oil-price change squared term: negative but statistically insignificant coefficient.

### Structural determinants and asymmetries
- Extended specification (interactions of oil shocks with structural variables Xit):
  - Xit includes: inflation targeting dummy; energy intensity (EIA, available 1980-2011); labor market flexibility (EFW index, 1970-2012); central bank governance (governor turnover index, available 1980-1989 and 1995-2004).
  - Focus reported at horizon k = 0 since impacts vanish after two years.
- Key extended-specification findings:
  - Interaction terms have predicted signs; only inflation targeting regime and central bank governor turnover index are statistically significant.
  - Immediate impact at k = 0 declined from 0.07 (1970-1992) to 0.02 (1993-2015): decline of 0.05 percentage point.
  - Inflation targeting regimes explain a 0.015 percentage point decrease.
  - Central bank governance explains a 0.018 percentage point decrease.
  - Combined, these two variables explain about 60 percent of the observed decline.
- Asymmetry:
  - Positive shock: πt_oil,pos = πt_oil if πt_oil > 0, = 0 otherwise.
  - Negative shock: πt_oil,neg = πt_oil if πt_oil < 0, = 0 otherwise.
  - Response to positive shocks about twice as large as to negative shocks.
  - Both positive and negative responses decreased over time; asymmetry does not fully explain temporal decline.

### Cross-country heterogeneity and monthly evidence
- Group comparison (1990 onward common sample):
  - Advanced vs developing: effects more precisely estimated for advanced economies; point estimates not statistically different across groups.
  - Transport share: advanced economies 14.2% vs developing 12.1%.
- Monthly dataset (2000M1–2015M12):
  - Countries: 34 advanced and 37 developing economies with >10 consecutive years of data.
  - Country-by-country monthly regressions: peak instantaneous effect typically at t = 0.
  - Pass-through less precisely estimated and more heterogeneous in developing economies; average pass-through similar across groups in recent period.
- Country-level regressions explaining instantaneous coefficients βi0:
  - Estimation: WLS with weights = inverse of standard error of βi0.
  - Candidate determinants: transport share in CPI; fuel import share; net energy imports; past inflation (1990s average); IT dummy; inflation anchoring (inverse of initial response of expectations); central bank autonomy index; energy subsidies as % of GDP.
  - Descriptive/bivariate evidence: strong correlations of βi0 with transport share, fuel import share, net energy imports, energy subsidy; monetary policy variables weak.
  - Multivariate (balanced sample of 47 countries): transport share in CPI is the most robust determinant; energy subsidies important; monetary policy conduct variables not major determinants in recent period (convergence in policy conduct).
  - Robustness: high correlations among oil-share proxies led to inclusion of transport weight only in multivariate regressions.

### Key quantitative statistics (selected exact figures from tables and summary)
- Main average pass-through:
  - 10 percent global oil price increase → 0.4 percentage point peak domestic inflation impact; effect insignificant two years after shock.
- Summary statistics (Table 1, world sample, selected):
  - Full Sample: CPI Inflation 14.33; Global Oil Inflation 0.72; Transport share in the Consumption Basket 15.21; Fuel Import Share 12.78; Obs: 2,820; 2,582; 837; 2,651 respectively.
  - Advanced: CPI Inflation 6.26; Global Oil Inflation 0.73; Transport share 17.17; Fuel Import Share 13.02; Obs: 1,247; 1,239; 515; 1,270.
  - Emerging: CPI Inflation 20.74; Global Oil Inflation 0.71; Transport share 12.08; Fuel Import Share 12.55; Obs: 1,573; 1,343; 322; 1,381.
  - 1970-1992: CPI Inflation 19.44; Global Oil Inflation 0.78; Transport share 12.89; Fuel Import Share 13.62; Obs: 1,209; 1,057; 47; 1,107.
  - 1993-2015: CPI Inflation 10.50; Global Oil Inflation 0.68; Transport share 15.35; Fuel Import Share 12.17; Obs: 1,611; 1,525; 790; 1,544.
- Baseline dynamic coefficients (Table 2, 훿_oil for k=0..3): 0.043, 0.023, -0.016, -0.029.
  - Sample sizes N: 2240, 2168, 2096, 2024; R-squared: 0.154, 0.251, 0.327, 0.359.
- Structural shocks (Table 3, selected means and s.d.):
  - Supply shock: 1975-1992 mean 0.028 sd 0.260; 1993-2007 mean -0.031 sd 0.228.
  - Demand shock: 1975-1992 mean -0.048 sd 0.210; 1993-2007 mean 0.046 sd 0.283.
  - Oil-specific demand shock: 1975-1992 mean 0.023 sd 0.246; 1993-2007 mean -0.023 sd 0.336.
- Extended specification (Table 4, selected):
  - Central Bank Governor Turnover coefficient examples: 29.721 (3.31) *** and 30.941 (3.34) ***.
  - Inflation Targeting example coefficient: -2.184 (-2.21) ** for η0 in one column.
  - Adjusted R-squared in Table 4 ranges from 0.169 to 0.201 across reported columns.
- Country-level pass-through examples (Table 5, 2000M1–2015M12):
  - United States: coefficient 0.025 s.e. 0.003 ***.
  - Sweden: 0.020 0.004 ***.
  - Spain: 0.020 0.003 ***.
  - Chile: 0.028 0.004 ***.
  - Brazil: -0.008 0.003 ***.
  - Mexico: -0.001 0.002.
- Cross-country correlates (Table 6, selected):
  - Transport Weight in CPI: coefficient 0.045 (2.94) ***.
  - Energy Subsidies: coefficient -0.071 (-1.91) * and -0.092 (-1.91) * in alternative specifications.
  - Fuel Import Share: coefficient 0.033 (2.19) **.
  - Net Energy Import: coefficient 0.002 (2.44) ***.
  - Adjusted R-squared up to 0.174 in some specifications.
- Country-characteristics examples (Table A.5, exact entries):
  - United States: Transport Weight in CPI (%) 15.67; Fuel Share in Merchandise Import (%) 16.27; Net Energy Imports 24.88; Level of Inflation in the 90s (% points) 3.00; IT Dummy 0; Inflation anchoring 2.27; Central Bank Independence Index (0-1) 0.18; Energy Subsidies (%) 1.93.
  - Venezuela: Transport Weight in CPI (%) 11.50; Fuel Share in Merchandise Import (%) 1.52; Net Energy Imports -234.15; Level of Inflation in the 90s (% points) 47.97; IT Dummy 0; Inflation anchoring 0.22; Central Bank Independence Index (0-1) 0.53; Energy Subsidies (%) 13.40.

### Policy implications and suggested research avenues
- Policy implications:
  - Improvements in monetary policy frameworks and better-anchored inflation expectations are associated with lower aggregate pass-through from oil to inflation.
  - Energy subsidy reform could increase transmission of oil price signals to domestic prices, with implications for inflation dynamics that policymakers need to consider.
  - The transport share in the consumption basket is a key structural determinant of exposure to global oil inflation; policymakers should account for this channel when assessing vulnerability to oil shocks.
- Suggested future research:
  - Test how energy price changes affect inflation expectations in advanced and developing economies.
  - Further investigate supply and demand effects for the pass-through to inform appropriate monetary policy frameworks to keep inflation expectations well anchored.

*Source: IMF Working Paper — wp17196 (sections 4.1, 4.2, and 6).*

### 4.1 Channels and estimation method ...............................................................................9

### 4.1 Channels and estimation method

### Channels through which global oil prices affect headline CPI
- Headline CPI (P_t) is decomposed as P_t = P_t^N w_t^δ, where w_t = P_t^O / P_t^N and δ is the share of oil in the CPI basket (O = oil, N = non-oil).
- Taking logs and first differences yields:
  - π_t = π_t^N + δ ∆log w_t.
- Two distinct channels:
  - Direct effect: through ∆log w_t (changes in the log ratio of oil to non-oil prices), scaled by the oil share δ.
  - Indirect effect: via changes in non-oil (core) inflation π_t^N.

### Estimation approach: local projections (Jordà, 2005)
- Use local projections to estimate impulse response functions (IRFs) directly; a flexible alternative to VAR/ARDL that does not impose dynamic restrictions.
- For each horizon k, the reduced-form equation estimated on annual data is:
  - π_{i,t+k} = α_i^k + ν_t^k + Σ_{j=1}^l γ_j^k π_{i,t−j} + β^k δ_{i,t−1} π_{t}^{oil} + Σ_{j=0}^{k} θ_j δ_{i,t+j−1} π_{t+j}^{oil} + ε_{i,t}^k
  - where k = 0,..3.
- Definitions and roles of variables and terms:
  - π = domestic CPI inflation.
  - π_t^{oil} = global oil inflation in year t.
  - δ_{i,t−1} = share of oil in the domestic consumption basket for country i in year t−1, proxied by the share of transport in the CPI basket.
  - α_i^k = country-fixed effects.
  - ν_t^k = time-fixed effects.
  - β^k = impact coefficient of global oil inflation on domestic inflation at horizon k.
  - γ_j^k captures persistence of domestic CPI inflation.
  - Forward leads of global oil inflation (between time 0 and horizon k) are included to correct bias in local projections (Teulings and Zubanov, 2014).

### Identification and control for cross-country heterogeneity
- Inclusion of δ_{i,t−1} (lagged oil share) allows identification of average effect while controlling for cross-country heterogeneity and time-fixed effects.
- A lagged term is used because changes in global oil prices can directly affect a country's oil share in consumption baskets.

### Lag structure, horizons, and robustness to specification
- Baseline number of lags (l) is chosen to be two; results are reported as robust to the choice of lag length.
- Forecast horizons considered: k = 0, 1, 2, 3 (i.e., contemporaneous up to three years ahead).

### Construction and inference for IRFs
- IRFs of the average effect are obtained by plotting estimated β^k rescaled by the sample average oil share δ̄ (i.e., multiply β^k by δ̄).
- Confidence bands are computed using the standard deviations associated with the estimated coefficients.

### Small-sample bias considerations
- Presence of lagged dependent variable and country fixed effects may bias parameter estimates in small samples (Nickell, 1981).
- Finite sample bias is of order 1/T, where the average T in the baseline sample is 40. The time dimension length mitigates this concern.

*Source: IMF Working Paper (section 4.1, "Channels and estimation method").*

### 4.2 Baseline results

### wp17196 - 4.2 Baseline results

### Baseline estimates of oil-price pass-through to inflation
- Sample and period: panel estimates over 1970-2015.
- Main finding: global oil price shocks have a positive and statistically significant effect on domestic inflation.
- Quantitative effect:
  - A 10 percent increase in global oil price typically increases domestic inflation by 0.4 percentage point in the short term (i.e. in the year of the oil price shock).
  - The effect becomes statistically insignificant two years after the shock.
  - Many episodes of oil price shocks involve increases of 50 percent or more; therefore the estimated pass-through is economically significant.

### Robustness checks and alternative measures of oil share
- Main sample: an unbalanced panel of 72 countries.
- Balanced-sample check: re-estimated for a balanced panel of 57 countries with CPI data for the whole period; results very similar to baseline.
- Alternative oil-share proxy: share of fuel in total merchandise imports (wider coverage).
  - Comparative medians (transport share in CPI basket vs fuel import share):
    - Advanced economies: 12.6 percent (transport share) and 14.1 percent (fuel import share).
    - Developing economies: 13.6 percent (transport share) and 11.6 percent (fuel import share).
  - Results using fuel import shares produce impulse responses similar to the baseline, supporting robustness to the alternative proxy.

### Endogeneity concerns and alternative estimators
- Potential endogeneity sources:
  - Nickell bias from country-fixed effects with a lagged dependent variable.
  - Reverse causality or omitted common factors affecting global oil prices and domestic inflation.
- Methods to address endogeneity:
  - Re-estimate Equation (3) without country-fixed effects.
  - Two-step generalized-method-of-moments (system GMM) estimator using up to four lags of domestic and global oil inflation as instruments for global oil inflation.
  - Re-estimate using the difference (πi,t − πt_oil) as the dependent variable to purge common factors.
  - Panel-VAR approach (Cholesky identification ordering: global oil inflation first, then domestic inflation; lag length chosen equal to two) to control for lagged feedback effects.
- Result: estimates from these alternative specifications are similar to the baseline, confirming the validity of the baseline results.

### Role of core (non-oil) inflation
- Sample for core inflation: restricted to 45 countries with both headline and core inflation data.
- Finding: the effect of global oil price shocks on core inflation is considerably smaller and less persistent than on headline inflation.
  - Core inflation contributes by about one-third to the overall effect of oil price shocks on domestic headline inflation.

### Changes in the effects over time
- Subsample analysis: Equation (3) re-estimated for 1970-1992 and 1993-2015.
  - Finding: the impact of oil prices on inflation has declined over time.
  - Magnitude: the effect of oil price shocks is more than three times larger in 1970-1992 than in 1993-2015.
- Country examples: United States and United Kingdom show more muted impacts in the latter period.
- Proposed drivers for the decline (discussed and assessed):
  - (i) absence of significant oil shocks in the 1990s,
  - (ii) declining share of oil in the consumption basket,
  - (iii) structural changes such as greater wage flexibility,
  - (iv) increased credibility of monetary policy (better-anchored inflation expectations).
- Investigation of shock sizes:
  - Using Kilian (2009) decomposition into three structural shocks (supply, demand, oil-specific demand): sizes of the three structural shocks are similar between the two subperiods, implying changes in shock sizes unlikely to account for the reduced response.
  - Including a square term of oil price changes yields a negative but statistically insignificant coefficient.

### Structural factors shaping pass-through (extended specification, Equation (4))
- Extended specification includes interactions of oil-price shocks with structural variables Xit:
  - Xit includes: (i) inflation targeting regime (dummy = 1 if adopted), (ii) energy intensity (total primary energy consumption (British Thermal units) per dollar of real GDP, EIA, available 1980-2011), (iii) labor market flexibility (EFW-based index, available 1970-2012), (iv) central bank governance (central bank governor turnover index, available 1980-1989 and 1995-2004).
- Estimation focus: report coefficients ρk at horizon k=0 since impacts vanish two years after shocks.
- Results (Table 4 summary):
  - All interaction terms have predicted signs.
  - Only inflation targeting regime and central bank governor turnover index are statistically significant.
- Quantification of contributions to the temporal decline:
  - Immediate impact on CPI inflation at k=0 declined by 0.05 percentage point from 0.07 (1970-1992) to 0.02 (1993-2015).
  - Inflation targeting regimes account for a 0.015 percentage point decrease.
  - Central bank governance accounts for a 0.018 percentage point decrease.
  - Combined, these two variables explain about 60 percent of the observed decline in the effect of oil price shocks.

### Asymmetry in responses to positive vs negative oil shocks
- Positive and negative oil-price shocks defined as:
  - πt_oil,pos = πt_oil if πt_oil > 0, = 0 otherwise.
  - πt_oil,neg = πt_oil if πt_oil < 0, = 0 otherwise.
- Finding: the response to positive oil price shocks is twice as large as the response to negative oil price shocks (consistent with Mork (1989) and U.S. evidence).
- Note: both positive and negative shock responses decreased over time; asymmetry alone does not explain the temporal decline.

### Advanced vs. developing economies
- Separate estimation of Equation (3) for advanced and developing country groups using a common sample starting from 1990 to avoid differences in time-series coverage.
- Finding: effect of oil price shocks is more precisely estimated for advanced economies, but point estimates are not statistically different across groups.
  - Transport share in CPI basket: advanced economies 14.2% and developing economies 12.1%, consistent with similar average pass-through.

### Evidence from monthly data for the 2000s
- Monthly sample: 2000M1 to 2015M12.
  - Countries: 34 advanced economies and 37 developing economies with more than ten consecutive years of data.
- Country-by-country specification (Equation (6)): monthly domestic headline inflation regressed on monthly global oil inflation and lags/leads.
- Key findings:
  - Peak instantaneous effect typically at t=0; table ranks instantaneous coefficients β0 for each country.
  - Pass-through is less precisely estimated and more heterogeneous among developing economies than advanced economies.
  - On average, pass-through in developing economies is similar to advanced economies in the recent period.

### Explaining cross-country heterogeneity (country-level regressions, Equation (7))
- Dependent variable: estimated instantaneous country coefficients βi0 from monthly regressions.
- Estimation method: Weighted Least Squares (WLS) with weights = inverse of standard error of βi0.
- Potential explanatory factors considered:
  - Transport share in CPI basket.
  - Fuel share of merchandise imports or ratio of net energy imports to total energy use.
  - Past inflation proxied by average inflation in the 1990s.
  - Inflation targeting regime (binary).
  - Anchoring of inflation expectations (inverse of initial response of inflation expectations to inflation surprises, private survey data 1990-2014).
  - Central bank autonomy (average index from Dince and Eichengreen (2014), 1998-2010, range 0–1).
  - Energy subsidies as a share of GDP (post-tax petroleum subsidies as a share of GDP, Coady et al. (2015)).
- Descriptive and bivariate evidence:
  - Scatter plots indicate strong correlations of βi0 with transport share in CPI basket, fuel import share, net energy imports, and energy subsidy.
  - Monetary policy variables show weak associations and generally not statistically significant.
  - Transport share explains a meaningful portion of cross-country variation (R2 = 0.11 in bivariate).
- Multivariate regressions (balanced sample of 47 countries):
  - Transport share in the CPI basket emerges as the most robust determinant of cross-country response.
  - Monetary policy conduct variables do not appear as major determinants of cross-country differences in pass-through in the recent period—likely due to convergence in monetary policy conduct over the last 20 years.
  - Energy subsidies are an important determinant of the degree of pass-through.
- Robustness notes:
  - High correlations among three proxies for oil shares lead to inclusion of transport weight only in multivariate regressions.
  - To retain sample size, the degree of inflation anchoring variable was dropped from the multivariate specification in the balanced sample.

*Source: wp17196 - 4.2 Baseline results (IMF working paper content provided).*

### 6. Conclusions

### 6. Conclusions

### Main findings on oil price pass-through to domestic inflation
- A 10 percent increase in global oil inflation, on average, increases domestic inflation at the peak impact by about 0.4 percentage point, with the effect becoming statistically insignificant two years after the shock.
- The pass-through has declined over time, mostly due to the improvement in the conduct of monetary policy.
- Using a monthly CPI dataset for 34 advanced and 37 developing economies, a one percent increase in global oil inflation has similar instantaneous effects of around 0.01 percentage points on both emerging and advanced economies.
- The transport share in the CPI basket is the most robust determinant of cross-country variation in the inflation response to oil shocks.
- Energy subsidies tend to reduce the pass-through from global oil price shocks to domestic inflation by distorting the price signal from oil price shocks.
- Variables regarding the conduct of monetary policy do not seem to be a major factor in explaining cross-country differences in pass-through magnitude in the cross-country analysis presented.

### Quantitative highlights and robustness evidence
- Table 1 (summary statistics, world sample):
  - Full Sample: CPI Inflation 14.33, Global Oil Inflation 0.72, Transport share in the Consumption Basket 15.21, Fuel Import Share 12.78 (Obs: 2,820; 2,582; 837; 2,651 respectively).
  - Advanced: CPI Inflation 6.26, Global Oil Inflation 0.73, Transport share 17.17, Fuel Import Share 13.02 (Obs: 1,247; 1,239; 515; 1,270).
  - Emerging: CPI Inflation 20.74, Global Oil Inflation 0.71, Transport share 12.08, Fuel Import Share 12.55 (Obs: 1,573; 1,343; 322; 1,381).
  - 1970-1992: CPI Inflation 19.44, Global Oil Inflation 0.78, Transport share 12.89, Fuel Import Share 13.62 (Obs: 1,209; 1,057; 47; 1,107).
  - 1993-2015: CPI Inflation 10.50, Global Oil Inflation 0.68, Transport share 15.35, Fuel Import Share 12.17 (Obs: 1,611; 1,525; 790; 1,544).
- Baseline dynamic coefficients (Table 2, selected):
  - 훿_oil (k=0..3): 0.043, 0.023, -0.016, -0.029 (t-stats shown in table).
  - Lag coefficients for domestic inflation (휋_i,t−1 and 휋_i,t−2) reported across k specifications with significance as in Table 2.
  - Sample sizes N: 2240, 2168, 2096, 2024; R-squared: 0.154, 0.251, 0.327, 0.359.
- Structural shock summary (Table 3):
  - Supply shock mean and sd: 1975-1992 mean 0.028 sd 0.260; 1993-2007 mean -0.031 sd 0.228.
  - Demand shock mean and sd: 1975-1992 mean -0.048 sd 0.210; 1993-2007 mean 0.046 sd 0.283.
  - Oil-specific demand shock mean and sd: 1975-1992 mean 0.023 sd 0.246; 1993-2007 mean -0.023 sd 0.336.
- Extended specification (Table 4, selected coefficients):
  - Central Bank Governor Turnover enters strongly: coefficient 29.721 (3.31) *** and 30.941 (3.34) *** in columns reported.
  - Inflation Targeting shows negative coefficients in some specifications, e.g., -2.184 (-2.21) ** for 휂0 in one column.
  - Adjusted R-squared in Table 4 ranges from 0.169 to 0.201 across reported columns; sample Ns reported per column (e.g., 2234; 1746; 2167; 1548; 1407).
- Country-level pass-through estimates (Table 5, examples, 2000M1 to 2015M12):
  - United States: coefficient 0.025 s.e. 0.003 ***.
  - Sweden: 0.020 0.004 ***.
  - Spain: 0.020 0.003 ***.
  - Chile: 0.028 0.004 ***.
  - Brazil: -0.008 0.003 ***.
  - Mexico: -0.001 0.002.
  - Numerous country-specific coefficients and standard errors are reported in Table 5 (advanced and developing economies).
- Cross-country correlates of instantaneous coefficients (Table 6, selected):
  - Transport Weight in CPI: coefficient 0.045 (2.94) ***.
  - Energy Subsidies: coefficient -0.071 (-1.91) * and -0.092 (-1.91) * in alternative specifications.
  - Fuel Import Share: coefficient 0.033 (2.19) **.
  - Net Energy Import: coefficient 0.002 (2.44) ***.
  - Sample sizes and adjusted R-squareds vary by specification (e.g., N from 33 to 68; adjusted R-squared up to 0.174).

### Heterogeneity, timing, and robustness
- Timing:
  - Peak average response: a 10 percent global oil inflation increase → ~0.4 percentage point peak domestic inflation impact; effect fades to insignificance by two years.
  - Monthly 2000M1–2015M12 instantaneous distributions show a clustering around 0.01 percentage points per one percent world oil inflation for both advanced and emerging economies.
- Heterogeneity:
  - Transport share in CPI and fuel import metrics explain notable cross-country variation in pass-through.
  - Energy subsidies reduce pass-through magnitude.
  - Measures of monetary policy conduct (e.g., inflation targeting dummy, central bank independence index) are not strong cross-country predictors of pass-through in the reported cross-sectional analyses.
- Robustness:
  - Results are robust across alternative estimation setups and dependent variables as reported in Figures (panel VAR, GMM, no country fixed effects, alternative dependent variables) and associated tables.

### Policy implications and suggested research avenues
- Policy implications:
  - Improvements in monetary policy frameworks and inflation-anchoring appear associated with lower aggregate pass-through from oil to inflation.
  - Energy subsidy reform could increase the transmission of oil price signals to domestic prices, with implications for inflation dynamics that policymakers need to consider.
  - The transport share of consumption is a key structural determinant of exposure to global oil inflation; policymakers should account for this channel when assessing vulnerability to oil shocks.
- Suggested future research directions (as noted in the chapter):
  - Test how energy price changes affect inflation expectations in advanced and developing economies.
  - Further investigate supply and demand effects for the pass-through to inform the appropriate monetary policy framework to keep inflation expectations well anchored.

*Source: IMF working paper — wp17196, "6. Conclusions" (figures and tables referenced within the source).*

### References

### References (wp17196 - References)

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### Appendix — sources, definitions, and dataset coverage (selected)
- Table A.1: Sources and definitions of variables
  - Consumer Price Index: Haver Analytics (Including data for the core CPI)
  - World Oil Price: IMF Primary Commodity Prices West Texas Intermediate Crude Oil Prices
  - CPI Transport Basket Share: Country statistics sources and Haver Analytics (A broad concept of oil share)
  - Fuel Import Share: World Bank (World Bank staff estimates from the Comtrade database maintained by the United Nations Statistics Division)
  - Net Energy Imports: International Energy Agency and United Nations, Energy Statistics Yearbook (As a % of the total energy use)
  - Nominal Exchange Rate: IMF/GDS Database (Local currency units/USD)
  - Inflation Expectation: Consensus Economic Forecasts
  - Global Energy Subsidies: Coady et al. (2015) (As a % of the GDP)
  - Inflation Targeting Dummy: IMF's World Economic Outlook (1 if inflation targeting, 0 otherwise)
  - Advanced/Emerging Dummy: IMF's World Economic Outlook (1 if advanced, 0 if emerging)
  - Central Bank Governor Turnover Index: Crowe and Meade (2007) (A lower value indicates more independence)
  - Energy Intensity: U.S. Energy Information Administration (As a % of the GDP, normalized the value in 1980 to one)
  - Labor Market Flexibility: Fraser Institute’s Economic Freedom of the World database (A higher value indicates more flexibility (0 to 10))

### Appendix — selected summary statistics and empirical results (exact reported figures)
- Table A.2: Summary statistics on the annual dataset, 1970 to 2015 (examples)
  - Australia: Mean Headline Inflation 5.44; Global Oil Inflation 0.52; Core Inflation 5.40; Fuel Share in Merchandise Import 8.97; Transport Share in CPI Basket 13.87; Obs 44; Standard Deviations: Headline 3.74; Global Oil Inflation 2.22; Core Inflation 4.12; Fuel Share 4.06; Transport Share 1.29.
  - United States: Mean Headline Inflation 4.20; Global Oil Inflation 1.13; Core Inflation 4.10; Fuel Share in Merchandise Import 16.00; Transport Share in CPI Basket 17.15; Obs 44; Standard Deviations: Headline 2.75; Global Oil Inflation 4.37; Core Inflation 2.52; Fuel Share 7.56; Transport Share 0.48.
  - India: Mean Headline Inflation 7.70; Global Oil Inflation 1.73; Core Inflation 7.18; Fuel Share in Merchandise Import 27.71; Transport Share in CPI Basket 7.57; Obs 44; Standard Deviations: Headline 4.66; Global Oil Inflation 7.05; Core Inflation 2.33; Fuel Share 10.41; Transport Share 0.00.
  - Russia: Mean Headline Inflation 47.28; Global Oil Inflation 0.14; Core Inflation 8.31; Fuel Share in Merchandise Import 2.14; Transport Share in CPI Basket 3.13; Obs 25; Standard Deviations: Headline 74.29; Global Oil Inflation 0.58; Core Inflation 2.62; Fuel Share 0.80; Transport Share 0.21.
  - (Table A.2 contains analogous entries for 100+ countries with Obs counts and exact means and standard deviations.)

- Table A.3: Summary statistics on the monthly dataset, 2000M1 to 2015M12 (examples)
  - United States: Mean Headline Inflation 0.20; S.D. 1.12; Oil Inflation in LCU Mean 0.54; S.D. 8.33; Obs 167.
  - Japan: Mean Headline Inflation -0.02; S.D. 1.21; Oil Inflation in LCU Mean 0.81; S.D. 8.51; Obs 138.
  - Turkey: Mean Headline Inflation 1.26; S.D. 2.11; Oil Inflation in LCU Mean 0.29; S.D. 9.18; Obs 167.
  - Venezuela: Mean Headline Inflation 1.82; S.D. 2.72; Oil Inflation in LCU Mean 1.08; S.D. 11.03; Obs 167.
  - (Table A.3 lists monthly means, standard deviations, and Obs for numerous countries.)

- Table A.4: Pass-through coefficients from world oil inflation (measured in USD) to domestic headline inflation (2000M1 to 2015M12)
  - United States: Coefficient 0.025; s.e. 0.003 ***
  - Israel: Coefficient 0.022; s.e. 0.004 ***
  - Spain: Coefficient 0.019; s.e. 0.003 ***
  - Sweden: Coefficient 0.018; s.e. 0.003 ***
  - Greece: Coefficient 0.017; s.e. 0.004 ***
  - Canada: Coefficient 0.017; s.e. 0.003 ***
  - Ireland: Coefficient 0.016; s.e. 0.004 ***
  - Belgium: Coefficient 0.014; s.e. 0.003 ***
  - France: Coefficient 0.013; s.e. 0.003 ***
  - Norway: Coefficient 0.011; s.e. 0.005 ***
  - Germany: Coefficient 0.011; s.e. 0.003 ***
  - United Kingdom: Coefficient 0.010; s.e. 0.003 ***
  - Japan: Coefficient 0.008; s.e. 0.003 ***
  - Italy: Coefficient 0.006; s.e. 0.002 ***
  - Taiwan: Coefficient 0.000; s.e. 0.002
  - Kazakhstan: Coefficient -0.001; s.e. 0.004
  - Turkey: Coefficient -0.002; s.e. 0.013
  - Singapore: Coefficient -0.003; s.e. 0.003
  - Iran: Coefficient -0.006; s.e. 0.009
  - Note: T-statics based on robust standard errors are reported in parentheses. ***, **, and * denote significance at 1, 5, and 10 percent level.

- Table A.5: Summary statistics on country characteristics (selected exact entries)
  - Australia: Transport Weight in CPI (%) 14.03; Fuel Share in Merchandise Import (%) 12.54; Net Energy Imports -133.68; Level of Inflation in the 90s (% points) 2.51; IT Dummy 1; Inflation anchoring 1.33; Central Bank Independence Index (0-1) 0.18; Energy Subsidies (%) 0.83.
  - United States: Transport Weight in CPI (%) 15.67; Fuel Share in Merchandise Import (%) 16.27; Net Energy Imports 24.88; Level of Inflation in the 90s (% points) 3.00; IT Dummy 0; Inflation anchoring 2.27; Central Bank Independence Index (0-1) 0.18; Energy Subsidies (%) 1.93.
  - Norway: Transport Weight in CPI (%) 17.45; Fuel Share in Merchandise Import (%) 4.97; Net Energy Imports -688.67; Level of Inflation in the 90s (% points) 2.45; IT Dummy 1; Inflation anchoring 2.15; Central Bank Independence Index (0-1) 0.33; Energy Subsidies (%) 0.53.
  - Russia: Transport Weight in CPI (%) 3.13; Fuel Share in Merchandise Import (%) 1.93; Net Energy Imports -76.95; Level of Inflation in the 90s (% points) 8.55; IT Dummy 0; Inflation anchoring 0.76; Central Bank Independence Index (0-1) 0.61; Energy Subsidies (%) 7.71.
  - Venezuela: Transport Weight in CPI (%) 11.50; Fuel Share in Merchandise Import (%) 1.52; Net Energy Imports -234.15; Level of Inflation in the 90s (% points) 47.97; IT Dummy 0; Inflation anchoring 0.22; Central Bank Independence Index (0-1) 0.53; Energy Subsidies (%) 13.40.

- Table A.6: Country-specific factors and the size of pass-through coefficients (balanced sample) — selected estimated coefficients (WLS)
  - Transport Weight in CPI: 0.049 (2.28)*** (Column 1)
  - Fuel Import: 0.051 (2.31)*** (Column 6)
  - Level of Inflation in the 90s: -0.019 (-1.40) (Column 2)
  - IT Dummy: -0.002 (-0.49) (Column 3)
  - Central Bank Independence Index: 0.001 (0.10) (Column 4)
  - Energy Subsidies: -0.043 (-0.48) (Column 5)
  - Net Energy Import: 0.001 (1.20) (Column 7)
  - Inflation Anchoring: 0.001 (0.51) (Column 8)
  - Constants reported per column (examples): 0.003 (0.92); 0.012 (6.57)***; 0.012 (3.41)***; 0.011 (2.94)***; 0.0117 (5.66)***; 0.004 (1.34); 0.011 (7.15)***; 0.009 (2.83)***.
  - N 24 in each reported regression; Adjusted R-squared vary by column (e.g., 0.1554, 0.0405, -0.0341, -0.0449, -0.0346, 0.1588, 0.0191, -0.0332).
  - Note: T-statics based on WLS are reported in parentheses. ***, **, and * denote significance at 1, 5, and 10 percent level.

### Methodology — Estimating Inflation Anchoring
- Objective: Estimate the extent to which inflation expectations are anchored by measuring the response of medium-term inflation expectations to an unexpected increase in current-period inflation.
- Procedure:
  - Estimate the response of expectations of future inflation to an unexpected 1 standard deviation increase in inflation in the current year.
  - Inflation expectation data: surveys of professional forecasters conducted in 20 advanced and 18 emerging and developing economies over the past two decades, based on Consensus Economics.
  - Statistical approach: based on Levin, Natalucci, and Piger (2004).
- Estimation equation (separately for each country):
  - ∆E_it π_i,t+N = α + β ∆E_it π_it + μ_i + λ_t + v_i,t , where N = 1...5.
  - Definitions:
    - ∆E_it π_i,t+N denotes the revision of expectations for inflation in year t+N.
    - The explanatory variable is the unexpected change in current-year inflation, defined as the revision of expectations for inflation in year t made between spring and fall of year t.
  - Data frequency: surveys published twice yearly in the spring (March/April) and fall (September/October) from 1990 to 2015.
  - Table A.4 lists the countries included in this part of the analysis.

*Source: wp17196 - References (IMF working paper content provided).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17196.pdf_