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---

### I. Introduction
- Research question: Did inflation-targeting (IT) central banks manage the 2022 inflation surge, driven largely by global supply shocks, more effectively than non-IT counterparts (de jure AREAER classification)?
- Sample and approach:
  - Comparative analysis across a sample of 33 advanced and emerging market IT countries and 37 non-IT peers.
  - Focus on inflation outcomes, expectations, and output responses; robustness checks use alternative classifications.
- Key high-level finding:
  - Despite stronger institutional signaling and somewhat more proactive policy, IT central banks did not consistently achieve better inflation outcomes than non-IT peers during the 2022 supply-side shock.
  - Inflation expectations were not more firmly anchored and real economic costs were not unambiguously lower.

### II. Inflation Targeting Meets the “New Inflation”: A System Under Strain?
- Context and drivers:
  - Transition from predominantly demand-driven inflation to more frequent, persistent, and overlapping supply disruptions (pandemic, war, energy crises, fragmentation).
  - Drivers cited: geopolitical realignments, evolving trade patterns, climate-related disruptions, energy transition, reshoring and diversification, technological and cyber risks, demographic pressures.
- Characteristics of the new environment:
  - Supply shocks are often global, persistent, and systemic rather than localized and transitory.
  - Key parameters are less stable: the natural rate of interest is drifting, Phillips curve slopes are shifting, exchange rate pass-through is evolving.
  - Tightened policy margins due to high debt, large central bank balance sheets, climate and energy challenges, and sanctions.
- Implications for IT:
  - IT core elements: explicit medium-term target, primacy of price stability, forward-looking decisions, transparency, accountability.
  - Limitations in supply-driven shocks:
    - Rate adjustments cannot influence relative prices; disinflation typically comes at higher output costs.
    - Taylor-type rules may be ill-suited when the natural rate of interest varies across regimes (Nuño et al., 2024).
    - Optimal theoretical response may be to “let bygones be bygones,” tolerating permanent price-level shifts rather than reversing them.
  - Credibility concerns:
    - If households and firms become more backward-looking, announcements matter less than actual inflation outcomes (Coibion and Gorodnichenko, 2025; D’Acunto et al., 2025).
    - Risk of de-anchoring of long-run expectations if policymakers “look through” shocks (Nakamura et al., 2025).

### III. Stylized Facts: Inflation Dynamics in IT vs. Non-IT Countries
- Overview of 2022 episode:
  - The 2022 inflation surge was driven predominantly by supply shocks—energy and food price spikes and supply chain disruptions—with broad pass-through.
  - Cross-country studies attribute most of the rise and subsequent decline in inflation to war-related shocks; domestic macroeconomic conditions generally played a secondary role.
- Sample facts and descriptive results:
  - The shock affected both 33 IT and 37 non-IT countries.
  - Consumer price inflation (CPI) soared across the board, averaging around 9 percent globally.
  - Visual and preliminary comparisons indicate the difference in inflation trajectories between IT and non-IT countries was not statistically significant.
  - Many IT central banks pursued tightening even as supply-side pressures began to ease; these actions did not lead to clearly better outcomes compared to non-IT central banks in the observed period.
- Specific reported summary statistic:
  - 1.5 percentage points higher inflation than their non-IT counterparts throughout the sample period.
- Caveats:
  - Cross-country comparisons require accounting for structural differences (labor markets, fiscal responses, commodity exposures, institutional quality).
  - Rigorous econometric analysis is used to control for country-specific heterogeneity and time-varying shocks.

### IV. Event Study: Was Inflation Surge Less Pronounced in IT Countries?
- Empirical specification:
  - infl_it = α_t + β_t * IT_i + ε_it
    - infl is the y/y quarterly growth in headline CPI.
    - IT_i is a dummy equal 1 for IT countries (de jure AREAER).
    - α_t shows average inflation in non-IT countries in quarter t; α + β shows average inflation in IT countries in quarter t.
- Identification and sample considerations:
  - IT regimes in sample were adopted well before the sample period (2019:Q1–2024:Q4).
  - Most recent adopters (India and Kazakhstan) implemented IT in 2015.
  - Headline inflation is used because it is the inflation typically targeted by IT countries.
  - Sample exclusions: Finland and Spain (adopted Euro), Ghana, Guatemala, Moldova, Türkiye and Uganda (pre-shock inflation > 10 percent), Ukraine (wartime), Euro Area (incompatible arrangement).
  - Classification nuances tested in robustness checks: United States, Switzerland, Monetary Authority of Singapore.
- Empirical findings (summary):
  - Panel estimates indicate inflation surged across both IT and non-IT countries, peaking at roughly 9 percent on average.
  - The average inflation differential between IT and non-IT groups was constantly positive in the presented figures, indicating that IT countries, on average, experienced about (text cutoff in source).
  - The quantitative comparisons do not show a clear inflation advantage for IT regimes in the observed post-shock period.
- Short-run evolution:
  - While the inflation gap has remained broadly unchanged immediately after the shock, a divergence re-emerged after 2023. Inflation declined more sharply in non-IT countries in 2023, widening the difference once again.
- Interpretation:
  - The data do not support a decisive near-term inflation advantage for IT regimes during the 2022 supply-driven surge.
  - Possible interpretation: the global, supply-driven nature of the shock limited the practical effectiveness of IT’s theoretical advantages.
  - Policy implication: need to reassess whether monetary policy frameworks, including IT, should adapt to better manage credibility and real economic trade-offs as supply-side shocks become more frequent and persistent.

### V. Event Study Controlling for Country-Specific Unobserved Heterogeneity
- Regression specification:
  - infl_it = α_t + β_t * IT_i + λ_i + ε_it (within regressions by demeaning to avoid multicollinearity).
- Findings:
  - Before the 2022 shock, demeaned inflation in IT and non-IT countries was broadly comparable.
  - After the shock, demeaned inflation in non-IT countries declined faster relative to IT counterparts in 2023.
  - Robustness checks: regressions run separately for advanced and emerging economies, strong and lite IT countries, and other sample modifications produced qualitatively comparable results.
- Note:
  - “Inflation targeting lite” described as transitional frameworks where an inflation objective is announced but does not yet serve as the dominant nominal anchor.

### VI. Central Bank Independence (CBI) and Macroprudential Policy
- CBI:
  - Regression: infl_it = α_t + β_t * CBI_i + λ_i + ε_it, using de jure and de facto CBI measures.
  - De jure CBI (Romelli 2022, 2024): countries with higher legal independence experienced a sharper inflation surge after the 2022 shock; by Q4 2022 inflation in these countries was 2.2 percentage points above historical norms.
  - De facto CBI (KOF governor turnover): countries with no irregular leadership changes also experienced a modestly stronger inflation rise post-shock, with weaker statistical significance.
  - Pre-shock (2019–early 2022) pattern: countries with higher CBI delivered lower and more stable inflation; the shock reversed this advantage temporarily.
  - Interpretation: greater legal independence did not insulate economies from the early phase of the supply shock; credibility may have led to delayed aggressive intervention.
- Macroprudential policy:
  - Regression: infl_it = α_t + β_t * MP_i + λ_i + ε_it, where MP_i = 1 if cumulative macroprudential regulation > sample average as of 2022:Q1.
  - Sample coverage: out of 89 countries, all 89 AE and EM countries have data on Macroprudential Regulation; 40 had level of macroprudential regulation exceeding sample average, while 49 had level below sample average.
  - Result: tighter macroprudential regulation did not produce statistically significant differences in inflation dynamics; inflation trajectories were statistically indistinguishable across tighter vs. weaker macroprudential regimes.
  - Conclusion: macroprudential tools are vital for systemic financial risk management but were not effective at directly dampening the 2022 supply-side inflation surge.

### VII. Inflation Expectations: Anchoring in IT vs Non-IT Countries
- Regression: E[infl_it] = α * POST_t + λ_i + ε_it, with POST = 1 for 2022:Q1–2024:Q4; country fixed effects included.
- Sample: 32 IT countries and 17 non-IT countries using Consensus Forecast inflation expectations.
- Long-horizon expectations (2, 5, 10 years): minimal impact from the 2022 supply shock; long-term expectations remained broadly anchored in both groups.
- Short-horizon responses and magnitudes:
  - In IT countries actual inflation rose by an average of 3.1 percentage points.
  - In IT countries current-year expectations rose by 3.9 points; one-year-ahead expectations rose by 1.2 percentage points.
  - In non-IT countries actual inflation rose by 2.8 percentage points.
  - In non-IT countries current-year expectations rose by 3.4 points; one-year-ahead expectations rose by 1.1 percentage points.
- Main takeaway: IT did not deliver a decisive advantage in stabilizing short-term expectations; the gap between IT and non-IT was narrow, and long-term anchoring was similar.

### VIII. Interest Rate Reactions
- Regression: pr_it = α_t + β_t * IT_i + λ_i + ε_it, where pr = nominal policy rate or money market rate.
- Sample: 45 countries with policy rate data from LSEG and 33 with money market rate data from IFS; of the 45 with policy rate data, 23 are IT countries and 22 are not.
- Finding:
  - IT central banks raised policy rates more aggressively relative to their historical averages than non-IT central banks after the 2022 shock; pattern holds for policy rates and money market rates and persisted through end-2023.
  - Pre-shock context: between 2019 and early 2022 IT central banks ran lower interest rates than non-IT peers and entered the shock with more policy space.
- Puzzle:
  - IT central banks tightened more despite no clear edge in inflation outcomes or expectation management; raises questions about whether tightening was necessary to defend credibility or represented an overreaction to supply-driven inflation.

### IX. Output Losses and Disinflation
- Regression: gr_it = α_t + β_t * IT_i + λ_i + ε_it, where gr = y/y real GDP growth rate.
- Sample: 48 countries with quarterly GDP data; 31 are IT countries and 17 are not.
- Finding:
  - No meaningful difference in output losses between IT and non-IT countries during the 2023 disinflation phase; growth trajectories broadly similar once country fixed effects are controlled.
  - Conclusion: no evidence that IT frameworks delivered lower sacrifice ratios or smoother landings in this supply-shock episode.

### X. Exchange Rate Flexibility: REER and NEER Dynamics
- Regression: ER_it = α_t + β_t * IT_i + λ_i + ε_it, where ER = REER or NEER.
- Data coverage: out of 89 countries, 58 have REER data and 61 have NEER data from IFS. Out of 36 countries, 26 are IT and 10 are non-IT countries.
- REER result:
  - Both IT and non-IT countries experienced relative appreciation after the 2022 shock with no statistically significant difference in trajectories.
- NEER result:
  - Similar direction with more variation and wider confidence intervals among IT countries, indicating heterogeneity in nominal exchange rate responses.
- Interpretation:
  - IT regimes did not consistently exhibit greater exchange rate flexibility; exchange rate movements reflected global capital flows, terms of trade, and investor sentiment as much as domestic policy frameworks.

### XI. Descriptive Statistics and Sample Lists (selected)
- Annex I — Table 3. Descriptive statistics (Variable / Mean / Std. dev. / Min / Max):
  - Actual inflation — 3.90 / 3.57 / -3.26 / 22.64
  - Inflation forecast - current year — 3.95 / 3.24 / -1.29 / 21.78
  - Inflation forecast - next year — 3.12 / 1.74 / 0.04 / 13.57
  - Inflation forecast - 2 years — 2.85 / 1.25 / 0.54 / 9.19
  - Inflation forecast - 5 years — 2.79 / 1.01 / 0.77 / 6.95
  - Inflation forecast - 10 years — 2.73 / 0.93 / 0.77 / 6.95
  - Policy rate — 4.52 / 3.89 / -0.10 / 25.00
  - Money market date — 4.01 / 3.40 / -0.68 / 15.81
  - Output growth — 2.26 / 5.74 / -29.70 / 42.70
  - NEER — 0.70 / 6.46 / -45.92 / 33.81
  - REER — 0.28 / 6.57 / -47.15 / 39.08
  - IT dummy — 0.47 / 0.50 / 0 / 1
  - Central Bank Independence dummy (de jure) — 0.50 / 0.50 / 0 / 1
  - Central Bank Independence dummy (de facto) — 0.76 / 0.43 / 0 / 1
  - Macroprudential regulation tightness dummy — 0.46 / 0.50 / 0 / 1
- Annex I — Table 1. List of IT and non-IT countries (presented as in source) includes 33 IT entries and 37 non-IT entries (selected entries shown in the source).

### XII. Conclusions and Implications for Policy and Research
- Overall assessment:
  - IT remains valuable for transparency, discipline, and long-term price stability, particularly in demand-driven inflation environments.
  - In the 2022 global supply-shock environment, IT central banks did not consistently outperform non-IT peers on inflation outcomes, expectation anchoring, output costs of disinflation, or exchange rate flexibility.
  - Credibility and effective outcomes appear to depend more on real-time policy responses, communication, and adaptability than on framework labels alone.
- Policy implications:
  - Rigid, single-rule frameworks may be insufficient when multiple large supply shocks overlap; judgment-based, flexible frameworks are indispensable.
  - Central banks may need contingent strategies robust to regime switches, persistent supply disturbances, and unanchored expectations.
- Three questions for future research:
  - What anchors inflation expectations when there is no formal target?
  - How should monetary policy respond to inflation driven by supply shocks rather than demand excess?
  - What frameworks are best suited for a world shaped by persistent supply shocks (climate change, geopolitical fragmentation, trade realignments)?

*Source: wpiea2025212-source-pdf*

### REFERENCES ________________________________________________________________________________________________ 28

### wpiea2025212-source-pdf - REFERENCES ________________________________________________________________________________________________ 28

### FIGURES
- 1. CPI inflation in IT and non-IT countries ____________________________________________________________________ 9
- 2. Event study analysis: Evolution of average inflation in IT and non-IT countries ___________________________ 12
- 3. Event study analysis: Evolution of average inflation in IT and non-IT countries after  
    controlling for country specific unobserved heterogeneity _______________________________________________ 14
- 4. Event study analysis: Evolution of differences between inflation rates in more independent CBs and  
    compared to less independence CBs after controlling for country specific unobserved heterogeneity __ 16
- 5. Event study analysis: Evolution of differences between inflation rates in countries with tighter 
    macroprudential regulation compared to countries with weaker macroprudential regulation  
    after controlling for country specific unobserved heterogeneity _________________________________________ 17
- 6. Change in inflation expectations following the 2022 inflation surge ______________________________________ 19
- 7. Event study analysis: Evolution of average policy rates in IT and non-IT countries after  
    controlling for country specific unobserved heterogeneity _______________________________________________ 21
- 8. Event study analysis: Evolution of average real GDP growth rates in IT and non-IT  
    countries after controlling for country specific unobserved heterogeneity _______________________________ 23
- 9. Event study analysis: Evolution of real and nominal effective exchange rates in IT and  
    non-IT countries after controlling for country specific unobserved heterogeneity _______________________ 25

### ANNEXES
- ANNEXES

*Source: wpiea2025212-source-pdf - REFERENCES ________________________________________________________________________________________________ 28*

### Annex I ______________________________________________________________________________________________________ 31

### Annex I

### I. Introduction
- Research question: Did inflation-targeting (IT) central banks manage the 2022 inflation surge, driven largely by global supply shocks, more effectively than non-IT counterparts (de jure AREAER classification)?
- Theoretical background:
  - IT frameworks aim to serve as a nominal anchor, stabilizing expectations and enhancing central bank credibility.
  - Empirical literature links IT adoption to improved inflation control, reduced macroeconomic volatility, and strengthened policy discipline in demand-driven inflationary episodes (Bernanke et al., 1999; Mishkin and Schmidt-Hebbel, 2007).
- Motivation:
  - The resilience of IT regimes to large, persistent, global supply shocks is relatively untested; the 1970s oil shocks predate modern IT.
  - The 2022 surge following the War in Ukraine provides an empirical test.
- Sample and approach:
  - Comparative analysis across a sample of 33 advanced and emerging market IT countries and 37 non-IT peers.
  - Focus on inflation outcomes, expectations, and output responses; robustness checks use alternative classifications.
- Key high-level finding:
  - Despite stronger institutional signaling and somewhat more proactive policy, IT central banks did not consistently achieve better inflation outcomes than non-IT peers during the 2022 supply-side shock. Inflation expectations were not more firmly anchored and real economic costs were not unambiguously lower.

### II. Inflation Targeting Meets the “New Inflation”: A System Under Strain?
- Context: Structural change in the nature of inflation
  - Transition from predominantly demand-driven inflation over the past four decades to more frequent, persistent, and overlapping supply disruptions (pandemic, war, energy crises, fragmentation).
  - Drivers cited: geopolitical realignments, evolving trade patterns, climate-related disruptions, energy transition, reshoring and diversification, technological and cyber risks, demographic pressures.
- Characteristics of the new environment:
  - Supply shocks are often global, persistent, and systemic rather than localized and transitory.
  - Key parameters are less stable: the natural rate of interest is drifting, Phillips curve slopes are shifting, exchange rate pass-through is evolving.
  - Tightened policy margins due to high debt, large central bank balance sheets, climate and energy challenges, and sanctions.
- Implications for IT:
  - IT was developed with core elements: explicit medium-term target, primacy of price stability, forward-looking decisions, transparency, accountability.
  - IT’s historical success rested on environments where inflation was mostly demand-driven and monetary transmission functioned predictably.
  - In supply-driven shocks:
    - Rate adjustments cannot influence relative prices; disinflation typically comes at higher output costs.
    - Taylor-type rules may be ill-suited when the natural rate of interest varies across regimes (Nuño et al., 2024).
    - Optimal theoretical response may be to “let bygones be bygones,” tolerating permanent price-level shifts rather than reversing them.
  - Credibility concerns:
    - If households and firms become more backward-looking, announcements matter less than actual inflation outcomes (Coibion and Gorodnichenko, 2025; D’Acunto et al., 2025).
    - The main risk in “looking through” shocks is potential loss of inflation-fighting reputation and de-anchoring of long-run expectations (Nakamura et al., 2025).

### III. Stylized Facts: Inflation Dynamics in IT vs. Non-IT Countries
- Overview of the 2022 episode:
  - The 2022 inflation surge was driven predominantly by supply shocks—energy and food price spikes and supply chain disruptions—with broad pass-through across advanced and emerging economies.
  - Cross-country studies attribute most of the rise and subsequent decline in inflation to war-related shocks; domestic macroeconomic conditions generally played a secondary role (Dao et al., 2024; BIS 2024).
  - Demand-side forces were material in a few economies (notably the United States).
- Sample facts and descriptive results:
  - The shock affected both 33 IT and 37 non-IT countries.
  - Consumer price inflation (CPI) soared across the board, averaging around 9 percent globally.
  - Visual and preliminary comparisons indicate the difference in inflation trajectories between IT and non-IT countries was not statistically significant.
  - Many IT central banks pursued tightening even as supply-side pressures began to ease; these actions did not lead to clearly better outcomes compared to non-IT central banks in the observed period.
- Caveats:
  - Cross-country comparisons require accounting for structural differences (labor markets, fiscal responses, commodity exposures, institutional quality).
  - Rigorous econometric analysis is needed to control for country-specific heterogeneity and time-varying shocks.

### IV. Event Study: Was Inflation Surge Less Pronounced in IT Countries?
- Empirical specification (event-study regression):
  - infl_it = α_t + β_t * IT_i + ε_it
    - infl is the y/y quarterly growth in headline CPI.
    - IT_i is a dummy equal 1 for IT countries (de jure AREAER).
    - α_t shows average inflation in non-IT countries in quarter t; α + β shows average inflation in IT countries in quarter t.
- Identification and sample considerations:
  - IT regimes in sample were adopted well before the sample period (2019:Q1–2024:Q4).
  - Most recent adopters (India and Kazakhstan) implemented IT in 2015, providing sufficient transition time.
  - Predetermined IT indicator reduces endogeneity concerns and the need for IV or dynamic panel estimators.
  - Headline inflation is used because it is the inflation typically targeted by IT countries; a consistent cross-country core inflation dataset covering all sample countries is unavailable.
- Sample exclusions and classification notes:
  - From initial 40 IT countries in AREAER, Finland and Spain were excluded given they adopted the Euro since then.
  - Ghana, Guatemala, Moldova, Türkiye and Uganda were excluded because they experienced very high annual inflation rates (more than 10 percent) before the global supply shock, casting doubt on de facto IT implementation.
  - Classification nuances:
    - The United States is not classified as IT in AREAER, but was operated under a flexible inflation-targeting framework until August 2020 and is included in the IT group for robustness checks.
    - The Swiss National Bank’s medium-term definition of price stability (0–2 percent inflation) and reliance on conditional forecasts align with IT practices and are tested as part of the IT group for robustness.
    - The Monetary Authority of Singapore is centered on managing the exchange rate and is therefore classified as non-IT, but tested as IT for robustness checks.
- Empirical findings (summary of reported results up to the presented content):
  - Panel estimates indicate inflation surged across both IT and non-IT countries, peaking at roughly 9 percent on average.
  - The average inflation differential between IT and non-IT groups was constantly positive in the presented figures, indicating that IT countries, on average, experienced about (text cutoff in source) — overall, the quantitative comparisons do not show a clear inflation advantage for IT regimes in the observed post-shock period.
- Interpretation:
  - The data do not support a decisive near-term inflation advantage for IT regimes during the 2022 supply-driven surge.
  - Possible interpretation: the global, supply-driven nature of the shock constrained all monetary policy frameworks, limiting the practical effectiveness of IT’s theoretical advantages.
  - Policy implication: as supply-side shocks become more frequent and persistent, there is a need to reassess whether monetary policy frameworks, including IT, should adapt to better manage credibility and real economic trade-offs.

*Source: Annex I of the IMF Working Paper "Navigating the 2022 Inflation Surge" (content reproduced from the supplied PDF excerpt).*

### 1.5 percentage points higher inflation than their non-IT counterparts throughout the sample period. While the

### 1.5 percentage points higher inflation than their non-IT counterparts throughout the sample period. While the

### Findings
- 1.5 percentage points higher inflation than their non-IT counterparts throughout the sample period.

### Short-run evolution
- While the inflation gap has remained broadly unchanged immediately after the shock, a divergence re-emerged after

*Source: wpiea2025212-source-pdf*

### 2023. Inflation declined more sharply in non-IT countries, widening the difference once again.

### wpiea2025212-source-pdf - 2023. Inflation declined more sharply in non-IT countries, widening the difference once again.

### Key findings: inflation dynamics and the role of IT
- Inflation outcomes during the 2022 global supply shock were not consistently better in inflation-targeting (IT) countries than in non-IT countries.
- Non-IT countries experienced a faster decline in demeaned inflation after the initial 2022 spike, producing a widening difference in 2023.
- The presumed advantage of IT in buffering economies from global price pressures is not systematically supported by the evidence presented.

### Event study controlling for country-specific unobserved heterogeneity
- Regression specification: infl_it = α_t + β_t * IT_i + λ_i + ε_it (within regressions by demeaning to avoid multicollinearity).
- Before the 2022 shock, demeaned inflation in IT and non-IT countries was broadly comparable.
- After the shock, demeaned inflation in non-IT countries declined faster relative to IT counterparts in 2023.
- Robustness checks: regressions run separately for advanced and emerging economies, strong and lite IT countries, and other sample modifications produced qualitatively comparable results.
- Note: “Inflation targeting lite” is described as transitional frameworks where an inflation objective is announced but does not yet serve as the dominant nominal anchor.

### Central bank independence (CBI) versus IT
- Regression specification: infl_it = α_t + β_t * CBI_i + λ_i + ε_it, using de jure and de facto CBI measures.
- De jure CBI (Romelli 2022, 2024): countries with higher legal independence experienced a sharper inflation surge after the 2022 shock; by Q4 2022 inflation in these countries was 2.2 percentage points above historical norms.
- De facto CBI (KOF governor turnover): countries with no irregular leadership changes also experienced a modestly stronger inflation rise post-shock, with weaker statistical significance.
- Pre-shock (2019–early 2022) pattern: countries with higher CBI delivered lower and more stable inflation; the shock reversed this advantage temporarily.
- Interpretation: greater legal independence did not insulate economies from the early phase of the supply shock; credibility may have led to delayed aggressive intervention.

### Macroprudential policy
- Regression specification: infl_it = α_t + β_t * MP_i + λ_i + ε_it, where MP_i = 1 if cumulative macroprudential regulation > sample average as of 2022:Q1.
- Sample coverage: out of 89 countries, all 89 AE and EM countries have data on Macroprudential Regulation (IMF database); 40 had level of macroprudential regulation exceeding sample average, while 49 had the level of macroprudential regulation below sample average.
- Result: tighter macroprudential regulation did not produce statistically significant differences in inflation dynamics; inflation trajectories were statistically indistinguishable across tighter vs. weaker macroprudential regimes.
- Conclusion: macroprudential tools are vital for systemic financial risk management but were not effective at directly dampening the 2022 supply-side inflation surge.

### Inflation expectations: anchoring in IT vs non-IT countries
- Regression specification: E[infl_it] = α * POST_t + λ_i + ε_it, with POST = 1 for 2022:Q1–2024:Q4; country fixed effects included.
- Sample: analysis covers 32 IT countries and 17 non-IT countries using Consensus Forecast inflation expectations.
- Long-horizon expectations (2, 5, 10 years): minimal impact from the 2022 supply shock; long-term expectations remained broadly anchored in both groups.
- Short-horizon responses and magnitudes:
  - In IT countries actual inflation rose by an average of 3.1 percentage points.
  - In IT countries current-year expectations rose by 3.9 points; one-year-ahead expectations rose by 1.2 percentage points.
  - In non-IT countries actual inflation rose by 2.8 percentage points.
  - In non-IT countries current-year expectations rose by 3.4 points; one-year-ahead expectations rose by 1.1 percentage points.
- Main takeaway: IT did not deliver a decisive advantage in stabilizing short-term expectations; the gap between IT and non-IT was narrow, and long-term anchoring was similar.

### Interest rate reactions: did IT central banks act more aggressively?
- Regression specification: pr_it = α_t + β_t * IT_i + λ_i + ε_it, where pr = nominal policy rate or money market rate.
- Sample: 45 countries with policy rate data from LSEG and 33 with money market rate data from IFS; of the 45 with policy rate data, 23 are IT countries and 22 are not.
- Finding: IT central banks raised policy rates more aggressively relative to their historical averages than non-IT central banks after the 2022 shock; pattern holds for policy rates and money market rates and persisted through end-2023.
- Pre-shock context: between 2019 and early 2022 IT central banks ran lower interest rates than non-IT peers and entered the shock with more policy space.
- Puzzle: IT central banks tightened more despite no clear edge in inflation outcomes or expectation management; raises questions about whether tightening was necessary to defend credibility or represented an overreaction to supply-driven inflation.

### Output losses and disinflation: sacrifice ratios and soft landings
- Regression specification: gr_it = α_t + β_t * IT_i + λ_i + ε_it, where gr = y/y real GDP growth rate.
- Sample: 48 countries with quarterly GDP data; 31 are IT countries and 17 are not.
- Finding: no meaningful difference in output losses between IT and non-IT countries during the 2023 disinflation phase; growth trajectories broadly similar once country fixed effects are controlled.
- Conclusion: no evidence that IT frameworks delivered lower sacrifice ratios or smoother landings in this supply-shock episode.

### Exchange rate flexibility: REER and NEER dynamics
- Regression specification: ER_it = α_t + β_t * IT_i + λ_i + ε_it, where ER = REER or NEER.
- Data coverage: out of 89 countries, 58 have REER data and 61 have NEER data from IFS. Out of 36 countries, 26 are IT and 10 are non-IT countries.
- REER result: both IT and non-IT countries experienced relative appreciation after the 2022 shock with no statistically significant difference in trajectories.
- NEER result: similar direction with more variation and wider confidence intervals among IT countries, indicating heterogeneity in nominal exchange rate responses.
- Interpretation: IT regimes did not consistently exhibit greater exchange rate flexibility; exchange rate movements reflected global capital flows, terms of trade, and investor sentiment as much as domestic policy frameworks.

### Conclusions and implications for policy and research
- Overall assessment:
  - IT remains valuable for transparency, discipline, and long-term price stability, particularly in demand-driven inflation environments.
  - In the 2022 global supply-shock environment, IT central banks did not consistently outperform non-IT peers on inflation outcomes, expectation anchoring, output costs of disinflation, or exchange rate flexibility.
  - Credibility and effective outcomes appear to depend more on real-time policy responses, communication, and adaptability than on framework labels alone.
- Policy implications:
  - Rigid, single-rule frameworks may be insufficient when multiple large supply shocks overlap; judgment-based, flexible frameworks are indispensable.
  - Central banks may need contingent strategies robust to regime switches, persistent supply disturbances, and unanchored expectations.
- Three questions for future research:
  - What anchors inflation expectations when there is no formal target?
  - How should monetary policy respond to inflation driven by supply shocks rather than demand excess?
  - What frameworks are best suited for a world shaped by persistent supply shocks (climate change, geopolitical fragmentation, trade realignments)?

*Source: IMF Working Paper excerpt, "Navigating the 2022 Inflation Surge."*

### References

### References

### Cited works (selected, as listed)
- Avalos, Fernando; Ryan Banerjee; Matthias Burgert; Boris Hofmann; Cristina Manea; and Matthias Rottner. 2025. Commodity Prices and Monetary Policy: Old and New Challenges. BIS Bulletin No. 96 (January 8, 2025). Bank for International Settlements. https://www.bis.org/publ/bisbull96.pdf  
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- Bank for International Settlements (BIS). 2024. Annual Economic Report 2024. Basel: Bank for International Settlements, June. https://www.bis.org/publ/arpdf/ar2024e.pdf?utm  
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### Annex I — Table 1. List of IT and non-IT countries
- Source: IMF AREAER, 2023 vintage, which is based on the de jure classification of monetary policy regimes. From the initial sample of 40 IT countries in the AREAER we have excluded Finland and Spain given that they have adopted Euro since then. In addition, we have excluded Ghana, Guatemala, Moldova, Turkiye and Uganda, since they have experienced very high annual inflation rates (more than 10 percent) before the global supply shock which casts doubt on their de facto IT implementation. Ukraine is removed from the IT sample given the exceptional wartime circumstances and Euro Area is removed from the sample since it is a special arrangement consisting of a group of countries making it incompatible with the rest of the sample.
- IT countries / Non-IT countries (presented as in source):
  1. Albania / 1. Algeria
  2. Armenia (Strong) / 2. Azerbaijan
  3. Australia / 3. Bahamas
  4. Brazil / 4. Bahrain
  5. Canada (Strong) / 5. Belarus
  6. Chile / 6. BosniaandHerzegovina
  7. Colombia / 7. Botswana
  8. Czechia (Strong) / 8. BruneiDarussalam
  9. DominicanRepublic / 9. Bulgaria
  10. Georgia (Strong) / 10. CapeVerde
  11. Hungary / 11. China
  12. Iceland (Strong) / 12. CostaRica
  13. India / 13. Denmark
  14. Indonesia (Strong) / 14. Ecuador
  15. Israel (Strong) / 15. ElSalvador
  16. Japan (Strong) / 16. Fiji
  17. Kazakhstan / 17. HongKongSAR
  18. Korea (Strong) / 18. Jamaica
  19. Mexico (Strong) / 19. Jordan
  20. NewZealand (Strong) / 20. Kuwait
  21. Norway / 21. Macedonia
  22. Paraguay (Strong) / 22. Malaysia
  23. Peru (Strong) / 23. Mauritius
  24. Philippines / 24. Mongolia
  25. Poland / 25. Montenegro
  26. Romania (Strong) / 26. Morocco
  27. Russia (Strong) / 27. Oman
  28. Serbia (Strong) / 28. SaintKittsAndNevis
  29. SouthAfrica / 29. SaudiArabia
  30. Sweden / 30. Singapore
  31. Thailand / 31. Switzerland
  32. UnitedKingdom / 32. Tonga
  33. Uruguay (Strong) / 33. TrinidadandTobago
  34. Tunisia
  35. UnitedArabEmirates
  36. UnitedStates
  37. Vietnam

### Annex I — Table 2. Variable definition and sources
- Source: Authors.
- Variable definitions and sources (as listed):
  - infl: Growth in CPI index, y/y, percent (measured as log difference) — IMF IFS, OECD, Worldbank
  - IT: Dummy: =1 for IT countries — IMF AREAER
  - CBI de jure: Dummy: =1 if country's Central Bank Independence score was above average in 2022 — Romelli (2022, 2024)
  - CBI de facto: Dummy: =1 if CB Governor turnover happened before the official end of term at least once during 2017-2022 — Dreher, Sturm, and De Haan (2010), updated
  - MP: Dummy: = 1 if country had a higher than average number of cumulative macropru measures for 17 indicators implemented in 2022 — IMF iMaPP Database, Alam and others (2019)
  - POST: Dummy: =1 for the post supply shock period (2022:Q1 - 2024:Q4) — Authors
  - E[infl]: Inflation expectations (current year, 1-, 2-, 5-, and 10-years ahead), percent — Consensus Forecast
  - pr: CB policy rates, percent — LSEG
  - mmr: Money market rates, percent — IMF IFS
  - gr: GDP growth, y/y, percent — IMF IFS
  - NEER: Nominal effective exchange rate growth, y/y, percent — IMF IFS
  - REER: Real effective exchange rate growth, y/y, percent — IMF IFS

### Annex I — Table 3. Descriptive statistics
- Source: Authors, based on the sources mentioned in Table 2.
- Descriptive statistics (Variable / Mean / Std. dev. / Min / Max):
  - Actual inflation — 3.90 / 3.57 / -3.26 / 22.64
  - Inflation forecast - current year — 3.95 / 3.24 / -1.29 / 21.78
  - Inflation forecast - next year — 3.12 / 1.74 / 0.04 / 13.57
  - Inflation forecast - 2 years — 2.85 / 1.25 / 0.54 / 9.19
  - Inflation forecast - 5 years — 2.79 / 1.01 / 0.77 / 6.95
  - Inflation forecast - 10 years — 2.73 / 0.93 / 0.77 / 6.95
  - Policy rate — 4.52 / 3.89 / -0.10 / 25.00
  - Money market date — 4.01 / 3.40 / -0.68 / 15.81
  - Output growth — 2.26 / 5.74 / -29.70 / 42.70
  - Nominal effective exchange rate (NEER) — 0.70 / 6.46 / -45.92 / 33.81
  - Real effective exchange rate (REER) — 0.28 / 6.57 / -47.15 / 39.08
  - IT dummy — 0.47 / 0.50 / 0 / 1
  - Central Bank Independence dummy (de jure) — 0.50 / 0.50 / 0 / 1
  - Central Bank Independence dummy (de facto) — 0.76 / 0.43 / 0 / 1
  - Macroprudential regulation tightness dummy — 0.46 / 0.50 / 0 / 1

### Annex I — Figures A.1 to A.8 (titles and notes)
- Figure A.1. Advanced economies — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A: Average inflation in IT and non-IT countries
  - Note: Reported are the coefficients α (IT countries, D_IT=1) and α + β (non-IT countries, D_IT=0) from the regression and their 95% confidence intervals based on robust standard errors. Dotted line denotes the supply shock.
  - Panel B: Difference between average inflation in IT and non-IT countries
  - Note: Reported are the coefficients β from the regression and their 95% confidence intervals based on robust standard errors. The dotted line denotes the supply shock.
- Figure A.2. Emerging economies — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.3. Strong IT countries — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.4. Lite IT countries — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.5. Using only 19 non-IT countries with currency pegs — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.6. Using only 18 IT countries up to 3-years inflation target horizon — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.7. Using inflation differentials from explicit (IT) and implicit (non-IT) inflation targets — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.
- Figure A.8. Revised sample: Treating U.S., Switzerland, and Singapore as IT countries — Event study analysis: Evolution of average inflation in IT and non-IT countries
  - Panel A & Panel B with same note structure as Figure A.1.

*IMF WORKING PAPERS    Navigating the 2022 Inflation Surge — References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025212-source-pdf.pdf_