## _wp11232

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---

### I. Introduction
- Current spike in commodity prices, owing to a combination of external and idiosyncratic shocks, has led to high inflation in the developing world.
- Upside pressure on food prices is likely to have a significant adverse impact on the purchasing power of the poor and on macroeconomic stability.
- Food price inflation in many low-income countries (LICs) is more volatile, on average higher, and in many cases more persistent than nonfood inflation (Walsh, 2011).
- Inflation pressures have started to rise in some CEMAC countries; noncore inflation (food, energy) has flared up in certain members (the Republic of Congo, Cameroon) though it remains low for the region as a whole.
- Objectives:
  - Investigate inflation dynamics in four CEMAC members (Cameroon, the Central African Republic, Gabon, the Republic of Congo) and in the CEMAC-4 region as a whole.
  - Disentangle dynamics and interactions between core and noncore components of inflation.
- Empirical approach:
  - Estimate a cointegrated vector autoregressive (VAR) model to investigate effects of energy prices (global crude oil prices) and food prices on the noncore component of inflation.
  - Estimate pass-through from noncore inflation into core inflation and impact of domestic activity on core inflation for each country and the region.
  - Robustness via panel cointegration tests following Pedroni (1999, 2001, 2004).
- Prior literature context (selected):
  - Portillo (2009): fiscal shocks important source of inflation volatility in CEMAC.
  - Coleman (2010): inflation persistence in the Franc zone; asymmetry across countries; persistence in Chad.
  - Kinda (2010): supply constraints, public spending, and external factors (rain patterns) drive inflation in Chad.
  - Fernandez, Gerling and Valdovinos (2011): keeping inflation low reduces inflation uncertainty and relative price variability in a currency union (WAEMU).
  - Baldini and Poplawski-Ribeiro (2011): CFA franc arrangement countries more successful in price stability and single-digit inflation in 1980–2005.
- Main findings highlighted:
  - Inflation in the region driven mainly by global commodity prices and government policies.
  - Government channels:
    - Controlled prices hinder pass-through from international market prices into domestic prices.
    - Predominant role of government expenditure in driving domestic activity, affecting prices in some members.
  - Monetary policy by the central bank of the currency union is weak in explaining inflation dynamics for individual countries and the region.
  - Money supply not found to have statistically significant effects on consumer price inflation and its main subcomponents in the CEMAC countries; M2 excluded from the models.

### II. Background
- A. The Economic and Monetary Community of Central Africa (CEMAC)
  - CEMAC established in 1994 by Cameroon (CMR), the Central African Republic (CAR), Chad (TCD), the Republic of Congo (COG), Equatorial Guinea (EQG), and Gabon (GAB).
  - Heterogeneity across members in size, economic structure, and price controls.
  - Cameroon accounts for almost 50 percent of the population and 31.5 percent of CEMAC GDP.
  - Food items dominate the CPI basket with weights ranging from 21.7 percent (Cameroon) to 70.1 percent (Central African Republic).
  - Energy subcomponent accounts for about 4 percent in all countries.
  - Congo and Equatorial Guinea are highly reliant on oil exports; Cameroon and Gabon more diversified; Central African Republic is the only oil importer.
  - High poverty coexists with high income per capita in some members (e.g., the Republic of Congo, Equatorial Guinea), increasing vulnerability to commodity price surges.
- B. The Central Bank (BEAC)
  - Monetary policy conducted by Banque des Etats d’Afrique Centrale (BEAC) in close cooperation with the French Treasury as part of the CFA franc zone arrangement.
  - BEAC main objectives: maintain price stability and an appropriate level of foreign reserves in the pooled foreign exchange reserves of the members.
  - The French Treasury holds 50 percent of CEMAC countries’ foreign reserves and guarantees convertibility of the CFAF into euros at a fixed exchange rate.
  - After a 50 percent devaluation in 1994, the CFAF is currently pegged against the euro at 655.96 CFAF per euro.
  - BEAC statutory rules:
    - Limits the stock of total advances to governments to 20 percent of the previous year’s fiscal revenues.
    - Designed to keep gross foreign reserves for each central bank above 20 percent of sight liabilities.
  - BEAC has used quantitative limits on credit to governments and private sector to limit monetization of deficits.
  - Given macroeconomic differences among members and weaknesses in the monetary transmission mechanism, conducting a single monetary policy suitable for each country’s dynamics is challenging for the BEAC.

### III. Data and Estimation Methodology
- Data construction:
  - Monthly domestic CPI from IMF’s International Financial Statistics from 1996 to 2010.
  - Core and noncore price indices constructed using countries’ monthly data and weights for CPI subcomponents provided by the authorities.
  - Food weights in CPI basket: 21.7 percent (Cameroon) to 70.1 percent (Central African Republic).
  - Energy subcomponent: about 4 percent in all countries.
  - Quarterly dataset from 1996 to 2010 with a minimum of 55 observations per country.
  - Regional aggregation for CEMAC uses each country’s share of personal consumption expenditure (PCE) in total CEMAC PCE for each year.
- Imported price indices:
  - Data on total imports in US dollars by product label for each country between 2005 and 2009 obtained from Trade Map (International Trade Centre).
  - For each country, shares of each product in total value of imports computed for each year (2005–2009) and averaged across the sample period.
  - Imported products classified into energy, food, metals, or excluded.
  - Items rebased reflecting relative importance in respective category.
  - Quarterly import price indices (1996:1–2010:4) computed by multiplying relative average weights of each item within a category by the price index of a particular commodity or basket that best characterizes the price of the imported product.
  - Commodity price indices obtained from the IMF World Economic Outlook (WEO) database on a quarterly basis from 1996 to 2010.
- Data and variable construction specifics:
  - Imported product weights:
    - Average weights used for 1996:1–2004:4.
    - For each quarter between 2005 and 2009, use the weight relative to the imports in the corresponding year.
    - For 2010, use the imported product weights of 2009.
  - Commodity price indices:
    - Constructed indices for food (FCI), energy (ECI), and metals (metals index omitted from final model because not statistically significant).
    - Indices are euro-denominated.
    - Base year for indices: 2005 = 100.
  - Real GDP series:
    - Annual country real GDP interpolated to quarterly by allocating annual GDP/4 to a quarter and linearly interpolating remaining quarters; final series is weighted average of two interpolations to match annual totals.
    - Quarterly GDP series do not present seasonality by construction.
    - CEMAC aggregate GDP obtained by adding individual country series using a common base year.
  - Money aggregate:
    - M2 series aggregated for CEMAC as a whole; M2 not found statistically significant for main inflation components.
    - Money multipliers broadly stable over last decade in region.
- Econometric methods:
  - Cointegrated VAR (Johansen and Juselius, 1990, 1992) used for primary estimations.
  - Panel cointegration tests following Pedroni (1999, 2001, 2004) used for robustness.
- Models estimated:
  - Model (1): Noncore CPI dynamics with global food (FCI) and energy (ECI) import price indices.
    - Vector Xt = (log noncore CPI, log ECI, log FCI); p = 3.
    - Food and energy indices appear exogenous in first model.
    - Panel-VAR variant (Xt → Xit) and Pedroni panel cointegration tests applied.
  - Model (2): Pass-through from noncore to core CPI controlling for domestic demand.
    - Vector Yt = (log noncore CPI, log core CPI, log real GDP); p = 3.
    - Tests for cointegration between core, noncore, and RGDP; exogeneity tests for noncore inflation.
    - Panel-VAR variant and Pedroni dynamic panel cointegration robustness checks performed.
  - Estimation details:
    - When Π reduced rank r (r < p), Π = αβ′ and cointegrating vectors β estimated by maximum likelihood (Johansen, 1996).
    - Deterministic components and time dummies included when residuals exceed three standard deviations.

### IV. Empirical Results — Model (1): Effects of global food and energy prices on noncore inflation
- Cointegration tests:
  - Noncore inflation “weakly” cointegrates with food and energy prices in CEMAC-4 region.
  - Significant country-level cointegration found for Cameroon and Gabon (null rejected at 5 percent for these two countries).
  - Panel Pedroni ADF test rejects null of no cointegration for both pooled and group-mean estimations.
- Long-run pass-through (selected exact findings):
  - For every 1 percent increase in ECI, noncore inflation significantly increases by 0.2 percent in Cameroon (CMR) and the Central African Republic (CAR).
  - In the Republic of Congo, a 1 percent increase in FCI increases noncore inflation by 1.03 percent (weak significance at 10 percent).
  - For aggregated CEMAC-4 series, a 1 percent increase in FCI leads to a 0.16 percent increase in noncore inflation (weakly significant).
- Robustness (Pedroni FMOLS/DOLS):
  - ECI pass-through highly significant and positive for practically all individual countries.
  - FCI coefficients mostly not significant in long run for individual countries in panel estimations.
  - Group-FMOLS and Group-DOLS show a highly significant pass-through of ECI to noncore inflation for CEMAC-4.
- Speed of adjustment:
  - Error-correction coefficient for most countries (except the Congo) around 0.16 (i.e., 16 percent of deviation in noncore inflation corrected in first quarter).
- Impulse responses and dynamics:
  - CEMAC-4 impulse responses: increases in FCI and ECI raise noncore inflation; short-term effects peak after approximately 5 or 6 quarters and then decay but remain positive.
  - Country-specific:
    - Energy import prices do not have a long-run relationship with noncore CPI in the Republic of Congo (nonsignificant cointegration).
    - Food import price hikes show initially negative effects on noncore inflation for Cameroon and CAR, turning large and positive after a couple of quarters.
    - Peak effects for indices on noncore inflation occur around 4–6 quarters, then decay toward zero.
- Variance decomposition:
  - Degree of noncore inflation variability explained by ECI and FCI increases over horizons of 4, 20, and 40 quarters.
  - ECI explains a large share of noncore inflation variability in Cameroon and CAR.
  - FCI explains most variability in the Congo and Gabon.
  - For aggregate CEMAC-4, FCI mostly explains noncore inflation variability; for pooled sample both indices explain a small share.

### V. Empirical Results — Model (2): Impact of noncore inflation and economic activity on core inflation
- Cointegration tests:
  - Cointegration between core CPI, noncore CPI, and RGDP is highly significant only for Cameroon.
  - For other countries and CEMAC-4 aggregate, cointegration tests nonsignificant, indicating weak pass-through noncore→core and weak aggregate demand effects on long-run core inflation.
- Quantified pass-through and dynamics:
  - For Cameroon: peak effects of 1 percent increases in ECI and FCI on core inflation estimated at 0.32 percent after 10 quarters (ECI) and 0.14 percent after 14 quarters (FCI).
  - Pooled OLS CEMAC-4 sample (Table 7): coefficient on noncore CPI suggests that a 1 percent increase in noncore CPI raises core inflation by 0.85 percent.
  - Heterogeneity across countries and estimation methods: coefficients differ by country and estimator.
  - Noted unexpected result: significant long-run positive relationship between core inflation and RGDP for the Congo in both Tables 7 and 8.
- Variance decomposition of core inflation:
  - Noncore inflation explains up to 27 percent (Cameroon) and 69 percent (CAR) of core inflation variability at given horizons.
  - RGDP explains significant core-inflation variability in the Congo.
  - For CEMAC-4 region, noncore inflation accounts for up to 28 percent (aggregate) or 35 percent (pooled-OLS) of core inflation variability depending on sample.

### VI. Policy Implications
- Main messages:
  - Inflation in CEMAC largely driven by supply shocks, notably imported commodity price shocks.
  - Economic structure and government interventions (controlled prices, dominant government spending) shape pass-through dynamics.
- Country and regional recommendations:
  - Vulnerability and buffers:
    - CEMAC countries vulnerable to commodity price shocks; global food and energy prices important drivers of noncore inflation.
    - In absence of country-tailored monetary policy, countries should build fiscal space and reserve buffers by:
      - Increasing revenue mobilization,
      - Boosting expenditure efficiency while protecting social sectors,
      - Advancing structural reforms to increase economic diversification.
    - Oil exporters vulnerable to food price shocks should build fiscal buffers and save a substantial part of oil windfalls while increasing non-oil revenues.
  - Infrastructure:
    - Improvements in infrastructure bottlenecks could ease inflationary pressures over time.
    - Planned scaling up in infrastructure projects could add to near-term inflationary pressures; a gradual approach to capital spending recommended to avoid large short-term inflation increases.
  - Price liberalization and targeted assistance:
    - Manage price volatility via further liberalization of domestic prices while tailoring transfers to the poor.
    - Well-targeted subsidies and compensatory measures preferable to broad petroleum subsidies.
    - Avoid generalized subsidies, general public sector wage increases, ad-hoc tax reductions, and export restrictions.

### VII. Conclusions and Avenues for Future Research
- Conclusions:
  - Imported commodity price shocks significantly explain inflation dynamics in CEMAC countries.
  - National government policies (controlled prices and fiscal dominance) are important drivers of inflation dynamics.
  - Magnitude and duration of core inflation responses to domestic demand (e.g., from public investment scaling up) can be nonnegligible; reducing infrastructure bottlenecks helps medium-term inflation.
  - Heterogeneous country responses imply different policy mixes: build buffers for those vulnerable to energy shocks; increase non-oil growth, price flexibility, and targeted subsidies for those vulnerable to food shocks.
- Suggestions for future research:
  - Analyze how food and energy price changes affect inflation expectations in the CEMAC region.
  - Analyze the inflationary impact of fiscal policy to determine optimal policy mix, especially within a monetary union.

*Source — IMF Staff calculations and analysis from the cited working paper content*

### REFERENCES .............................................................................................................

### _wp11232 - REFERENCES

### I. INTRODUCTION
- Current spike in commodity prices, owing to a combination of external and idiosyncratic shocks, has led to high inflation in the developing world.
- Upside pressure on food prices is likely to have a significant adverse impact on the purchasing power of the poor and on macroeconomic stability.
- Food price inflation in many low-income countries (LICs) is more volatile, on average higher, and in many cases more persistent than nonfood inflation (Walsh, 2011).
- Inflation pressures have started to rise in some CEMAC countries; noncore inflation (food, energy) has flared up in certain members (the Republic of Congo, Cameroon) though it remains low for the region as a whole.
- Objectives of the paper:
  - Investigate inflation dynamics in four CEMAC members (Cameroon, the Central African Republic, Gabon, the Republic of Congo) and in the CEMAC-4 region as a whole.
  - Disentangle dynamics and interactions between core and noncore components of inflation.
- Empirical approach:
  - Estimate a cointegrated vector autoregressive (VAR) model investigating the effect of energy prices (in particular, global crude oil prices) and food prices on the noncore component of inflation.
  - Estimate the pass-through from noncore inflation into core inflation and the impact of domestic activity on core inflation for each country and the region as a whole.
  - Robustness checks via panel cointegration tests following Pedroni (1999, 2001, 2004).
- Summary of prior literature and context:
  - Portillo (2009): fiscal shocks important source of inflation volatility in CEMAC.
  - Coleman (2010): inflation persistence in the Franc zone; asymmetry across countries; persistence in Chad.
  - Kinda (2010): supply constraints, public spending, and external factors (rain patterns) drive inflation in Chad.
  - Fernandez, Gerling and Valdovinos (2011): keeping inflation low reduces inflation uncertainty and relative price variability in a currency union (WAEMU).
  - Baldini and Poplawski-Ribeiro (2011): CFA franc arrangement countries were more successful in achieving price stability and single-digit inflation in 1980–2005.
- Main findings highlighted:
  - Inflation in the region is driven mainly by global commodity prices and government policies.
  - Government channels:
    - Controlled prices hinder pass-through from international market prices into domestic prices.
    - Predominant role of government expenditure in driving domestic activity, affecting prices in some members.
  - Monetary policy conducted by the central bank of the currency union is weak in explaining inflation dynamics for individual countries and for the region as a whole.
  - Money supply is not found to have statistically significant effects on consumer price inflation (and its main subcomponents) in the CEMAC countries and is excluded from the models.

### II. BACKGROUND
- A. The Economic and Monetary Community of Central Africa (CEMAC)
  - CEMAC established in 1994 by Cameroon (CMR), the Central African Republic (CAR), Chad (TCD), the Republic of Congo (COG), Equatorial Guinea (EQG), and Gabon (GAB).
  - Heterogeneity across members in size, economic structure, and price controls.
  - Cameroon accounts for almost 50 percent of the population and 31.5 percent of CEMAC GDP.
  - Food items dominate the CPI basket with weights ranging from 21.7 percent in Cameroon to 70.1 percent in the Central African Republic.
  - Energy subcomponent accounts for about 4 percent in all countries.
  - Congo and Equatorial Guinea are highly reliant on oil exports; Cameroon and Gabon more diversified; Central African Republic is the only oil importer.
  - High poverty coexists with high income per capita in some members (e.g., the Republic of Congo, Equatorial Guinea), increasing vulnerability to commodity price surges.
- B. The Central Bank (BEAC)
  - Monetary policy conducted by Banque des Etats d’Afrique Centrale (BEAC) in close cooperation with the French Treasury as part of the CFA franc zone arrangement.
  - BEAC main objectives: maintain price stability and an appropriate level of foreign reserves in the pooled foreign exchange reserves of the members.
  - The French Treasury holds 50 percent of CEMAC countries’ foreign reserves and guarantees convertibility of the CFAF into euros at a fixed exchange rate.
  - After a 50 percent devaluation in 1994, the CFAF is currently pegged against the euro at 655.96 CFAF per euro.
  - BEAC monetary policy rules embedded in statutes:
    - BEAC limits the stock of total advances to governments to 20 percent of the previous year’s fiscal revenues.
    - BEAC is designed to keep gross foreign reserves for each central bank above 20 percent of sight liabilities.
  - BEAC has used quantitative limits on credit to governments and private sector to limit monetization of deficits.
  - Given macroeconomic differences among members and weaknesses in the monetary transmission mechanism, conducting a single monetary policy suitable for each country’s dynamics is challenging for the BEAC.

### III. DATA AND ESTIMATION METHODOLOGY
- A. Construction of the dataset
  - Consumer price indices: monthly domestic CPI from IMF’s International Financial Statistics from 1996 to 2010.
  - Core and noncore price indices constructed using countries’ monthly data and weights for CPI subcomponents provided by the authorities.
  - Food weights in CPI basket: range from 21.7 percent (Cameroon) to 70.1 percent (Central African Republic).
  - Energy subcomponent: about 4 percent in all countries.
  - Quarterly dataset constructed from 1996 to 2010 with a minimum of 55 observations per country in the sample.
  - Regional aggregation for CEMAC uses each country’s share of personal consumption expenditure (PCE) in the total value of CEMAC’s PCE for each year.
  - Import price-based indices constructed for energy, food, and metals:
    - Data on total imports in US dollars by product label for each country between 2005 and 2009 obtained from Trade Map (International Trade Centre).
    - For each country, shares of each product in total value of imports computed for each year (2005–2009) and averaged across the sample period.
    - Imported products classified into energy, food, metals, or excluded.
    - Items rebased reflecting relative importance in respective category.
    - Quarterly import price indices (1996:1–2010:4) computed by multiplying relative average weights of each item within a category by the price index of a particular commodity or basket that best characterizes the price of the imported product.
    - Commodity price indices obtained from the IMF World Economic Outlook (WEO) database (IMF, 2011a) on a quarterly basis from 1996 to 2010.
- Econometric methods referenced:
  - Cointegrated VAR (Johansen and Juselius, 1990, 1992) to investigate effects of energy and food prices on noncore inflation.
  - Pass-through estimation from noncore to core inflation and domestic activity impacts on core inflation.
  - Panel cointegration tests following Pedroni (1999, 2001, 2004) for robustness.

*Source: _wp11232 - REFERENCES*

### 2010. These average weights are used for 1996:1–2004:4. Then for each quarter between 2005 and

### _wp11232 - 2010. These average weights are used for 1996:1–2004:4. Then for each quarter between 2005 and

### Data and variable construction
- Imported product weights:
  - Average weights used for 1996:1–2004:4.
  - For each quarter between 2005 and 2009, use the weight relative to the imports in the corresponding year.
  - For 2010, use the imported product weights of 2009.
- Commodity price indices:
  - Constructed indices for food (FCI), energy (ECI), and metals (metals index was considered but omitted from final model because it was not statistically significant).
  - Indices are euro-denominated (observed variations embed exchange rate and US dollar denominated commodity price movements).
  - Base year for indices: 2005 = 100.
- Real GDP series:
  - Annual country real GDP interpolated to quarterly frequency by allocating annual GDP/4 to a quarter and linearly interpolating remaining quarters, repeated for second and third quarter allocations, then final series is weighted average of the two series; weights chosen to minimize distance between annual GDP figures and sum of four quarters so quarterly sums match annual data.
  - By construction, quarterly GDP series do not present seasonality.
  - CEMAC aggregate GDP series obtained by adding individual country series (using a common base year).
- Money aggregate:
  - M2 series aggregated for CEMAC as a whole; M2 not found statistically significant for main inflation components.
  - Money multipliers broadly stable over last decade in region.

### Methodology and estimation models
- Two dynamic models estimated using cointegrated vector autoregressive (VAR) methodology (Johansen and Juselius, 1990, 1992):
  - Model (1): Noncore CPI dynamics with global food (FCI) and energy (ECI) commodity import price indices.
    - Vector Xt = (log noncore CPI, log ECI, log FCI); p = 3.
    - Tests for cointegration and exogeneity; food and energy indices appear exogenous in first model.
    - Panel-VAR variant: replace Xt with Xit to pool across countries (Pedroni panel cointegration tests used).
  - Model (2): Pass-through from noncore to core CPI controlling for domestic demand.
    - Vector Yt = (log noncore CPI, log core CPI, log real GDP); p = 3.
    - Tests for cointegration between core, noncore, and RGDP; tests exogeneity of noncore inflation.
    - Panel-VAR variant and Pedroni dynamic panel cointegration robustness checks performed.
- Estimation details:
  - When Π is reduced rank r (r < p), Π = αβ′ and cointegrating vectors β estimated by maximum likelihood (Johansen, 1996).
  - Deterministic components and time dummies included when residuals exceed three standard deviations.

### Empirical results — Model (1): Effects of global food and energy prices on noncore inflation
- Cointegration tests:
  - Noncore inflation “weakly” cointegrates with food and energy prices in CEMAC-4 region.
  - Significant country-level cointegration found for Cameroon and Gabon (null rejected at 5 percent for these two countries).
  - Panel Pedroni ADF test rejects null of no cointegration for both pooled and group-mean estimations.
- Long-run pass-through estimates (selected exact findings from cointegration estimates):
  - For every 1 percent increase in ECI, noncore inflation significantly increases by 0.2 percent in Cameroon (CMR) and the Central African Republic (CAR).
  - In the Republic of Congo, a 1 percent increase in FCI increases noncore inflation by 1.03 percent (weak significance at 10 percent).
  - For aggregated CEMAC-4 series (Aggregate column, Table 3), a 1 percent increase in FCI leads to a 0.16 percent increase in noncore inflation (weakly significant).
- Robustness (Pedroni FMOLS/DOLS, Table 4):
  - ECI pass-through is highly significant and positive for practically all individual countries.
  - FCI coefficients mostly not significant in long run for individual countries in panel estimations.
  - Group-FMOLS and Group-DOLS show a highly significant pass-through of ECI to noncore inflation for CEMAC-4.
- Speed of adjustment:
  - Error-correction coefficient for most countries (except the Congo) around 0.16 (i.e., 16 percent of deviation in noncore inflation corrected in first quarter).
- Impulse responses and dynamics:
  - CEMAC-4 impulse responses: increases in FCI and ECI raise noncore inflation; short-term effects peak after approximately 5 or 6 quarters and then decay but remain positive.
  - Country-specific impulse responses:
    - Energy import prices do not have a long-run relationship with noncore CPI in the Republic of Congo (missing impulse response due to nonsignificant cointegration).
    - Food import price hikes show initially negative effects on noncore inflation for Cameroon and CAR, turning large and positive after a couple of quarters.
    - Peak effects for indices on noncore inflation occur around 4–6 quarters, then decay toward zero.
- Variance decomposition (Table 5):
  - Degree of noncore inflation variability explained by ECI and FCI increases over horizons of 4, 20, and 40 quarters.
  - ECI explains a large share of noncore inflation variability in Cameroon and CAR.
  - FCI explains most variability in the Congo and Gabon.
  - For aggregate CEMAC-4, FCI mostly explains noncore inflation variability; for pooled sample both indices explain a small share.

### Empirical results — Model (2): Impact of noncore inflation and economic activity on core inflation
- Cointegration tests:
  - Cointegration between core CPI, noncore CPI, and RGDP is highly significant only for Cameroon.
  - For other countries and CEMAC-4 aggregate, cointegration tests nonsignificant, indicating weak pass-through noncore→core and weak aggregate demand effects on long-run core inflation.
- Quantified pass-through and dynamics:
  - For Cameroon: peak effects of 1 percent increases in ECI and FCI on core inflation estimated at 0.32 percent after 10 quarters (ECI) and 0.14 percent after 14 quarters (FCI).
  - Table 7 estimates:
    - For pooled OLS CEMAC-4 sample, coefficient on noncore CPI suggests that a 1 percent increase in noncore CPI raises core inflation by 0.85 percent (Pooled OLS column).
  - Heterogeneity across countries and estimation methods: coefficients differ by country and estimator.
  - Unexpected result: significant long-run positive relationship between core inflation and RGDP for the Congo in both Tables 7 and 8 (possible relation to controlled prices and economic structure).
- Variance decomposition of core inflation (Table 9):
  - Noncore inflation explains up to 27 percent (Cameroon) and 69 percent (CAR) of core inflation variability at given horizons.
  - RGDP explains significant core-inflation variability in the Congo.
  - For CEMAC-4 region, noncore inflation accounts for up to 28 percent (aggregate) or 35 percent (pooled-OLS) of core inflation variability depending on sample.

### Policy implications
- Main messages:
  - Inflation in CEMAC is largely driven by supply shocks, notably imported commodity price shocks.
  - Economic structure and government interventions (controlled prices, dominant government spending) shape pass-through dynamics.
- Country and regional policy recommendations:
  - Vulnerability and buffers:
    - CEMAC countries are vulnerable to commodity price shocks; global food and energy prices are important drivers of noncore inflation.
    - In absence of country-tailored monetary policy, countries should build fiscal space and reserve buffers by:
      - Increasing revenue mobilization,
      - Boosting expenditure efficiency while protecting social sectors,
      - Advancing structural reforms to increase economic diversification.
    - Oil exporters vulnerable to food price shocks should build fiscal buffers and save a substantial part of oil windfalls while increasing non-oil revenues.
  - Infrastructure:
    - Improvements in infrastructure bottlenecks could ease inflationary pressures over time.
    - Planned scaling up in infrastructure projects could add to near-term inflationary pressures; a gradual approach to capital spending recommended to avoid large short-term inflation increases.
  - Price liberalization and targeted assistance:
    - Managing price volatility requires further liberalization of domestic prices while tailoring transfers to the poor.
    - Well-targeted subsidies and compensatory measures are preferable to broad petroleum subsidies.
    - Avoid generalized subsidies, general public sector wage increases, ad-hoc tax reductions, and export restrictions.

### Conclusions and avenues for future research
- Conclusions:
  - Imported commodity price shocks significantly explain inflation dynamics in CEMAC countries.
  - National government policies (controlled prices and fiscal dominance) are important drivers of inflation dynamics.
  - The magnitude and duration of core inflation responses to domestic demand (e.g., from public investment scaling up) can be nonnegligible; reducing infrastructure bottlenecks helps medium-term inflation.
  - Heterogeneous country responses imply different policy mixes: build buffers for those vulnerable to energy shocks; increase non-oil growth, price flexibility, and targeted subsidies for those vulnerable to food shocks.
- Suggestions for future research:
  - Analyze how food and energy price changes affect inflation expectations in the CEMAC region.
  - Analyze the inflationary impact of fiscal policy to determine optimal policy mix, especially within a monetary union.

*Italic: Source — IMF Staff calculations and analysis from the cited working paper content*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp11232.pdf_