## wpiea2020251-print-pdf

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### I. INTRODUCTION — adoption of FIT and inflation patterns
- Adoption of flexible inflation targeting (FIT) coincided with relatively low and more stable inflation in recent years compared to an earlier period of persistently high inflation.
- Food price inflation was remarkably subdued from late 2016 through mid-2019, creating a marked divergence between food and core inflation.
- The paper takes the adoption of inflation targeting to have been at the beginning of 2015 for analytical purposes; results are robust to adoption dates at the beginning of September 2016 and the beginning of 2014.
- The analysis focuses on domestic factors and whether inflation expectations have become more anchored since early 2015.

### Comparative and cross-country sample
- Cross-country sample: 33 emerging market economies — Albania, Bolivia, Botswana, Brazil, Bulgaria, Chile, China, Colombia, Croatia, Dominican Republic, Ecuador, El Salvador, Honduras, Hungary, India, Indonesia, Malaysia, Mexico, Mongolia, Nicaragua, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russia, Rwanda, Serbia, South Africa, Thailand, and Turkey.
- A simple event-study shows transition to FIT lowered EMDEs’ positive inflation differential vis-à-vis non-FIT peers, indicating a role for domestic monetary institutions.

### Key empirical findings — overview
- Headline inflation inertia in India (2004Q1–2019Q4) was stronger than in other emerging markets; the difference was driven entirely by the non-core component (most likely food).
- Measures of anchoring of inflation expectations:
  - Forecasters were less likely to revise medium-term inflation expectations after 2015—the distribution of medium-term inflation expectations revision became narrower.
  - Sensitivity of medium-term inflation expectations to inflation surprises declined after 2015.
- Second-round effects:
  - Since early 2015, headline inflation more likely converged to core inflation rather than core converging to headline, suggesting limited second-round effects.
- Caveat: persistent food-supply shocks—large and negative in recent years—may underlie structural changes; observed patterns do not necessarily imply permanent structural change.

### Evidence pointing to domestic drivers
- Import-price pass-through is positive and significant for core inflation and insignificant for headline inflation, implying segmentation between domestic and international food markets given food’s large consumption share.
- Food price inflation played a significant role in the pre/post-2015 change in convergence properties between headline and core inflation.

### Section focus: domestic vs international component of food-price convergence after 2015
- Headline inflation historically more backward-looking in India than in other EMDEs.
- Core and headline inflation are governed by different determinants in India; non-core (food) is prominent.
- Import-price pass-through to headline inflation historically weaker in India than in other EMDEs.
- Post-2015 structural break: core inflation no longer tends to revert to headline (no evidence of second-round effects), whereas headline tends to revert to core.
- The domestic component of relative food-price inflation, not the global component, contributed to the weaker second-round effects after 2015.

### Empirical analysis — Phillips-curve estimates and expectations
- Sample and period: 33 emerging market countries, 2004Q1 to 2019Q4.
- Inflation expectations measures:
  - Consensus Forecast: 2-year-ahead inflation expectations (for year t).
  - WEO forecasts: expected average change in the CPI index from t + 2 to t + 5 (annual WEO).
- Main specification: hybrid Phillips-curve (year-on-year changes) allowing parameters to differ for India vs other EMDEs; includes four-quarter-lagged inflation, real-time output gap (HP-filtered trend of real GDP), and import-price inflation in local currency.
- Empirical patterns (summary):
  - Headline inflation in India is more backward-looking; core inflation in India is more forward-looking relative to headline.
  - Both headline and core inflation are less responsive to the output gap in India than in other EMDEs.
  - Import-price inflation coefficient is smaller and less significant for India.

### Anchoring of inflation expectations — measures and evidence
- Constructed measures:
  - Inflation surprise: revision in the current survey for expected change in headline CPI from January to December in the current calendar year compared with the previous survey.
  - Revision in medium-term forecast: analogous revision for CPI change from January to December, 2 years ahead.
- Distributional evidence: narrowing of the distribution of two-year-ahead forecast revisions in India from 2015 onward, indicating tighter clustering around zero.
- Cross-country illustrative comparisons:
  - India’s sensitivity of two-year-ahead expectations to inflation surprises declined since 2015.
  - Chile exhibited well-anchored expectations both before and after 2015; Poland’s sensitivity was largely unchanged.

### Second-round effects and the role of food inflation — methodology and results
- Monthly regression framework examines how deviations between headline and core inflation predict subsequent changes in headline, core, and food inflation.
- Key regression interpretations:
  - β1 in πHeadline_t − πHeadline_t−12 = α1 + β1(πHeadline_t−12 − πCore_t−12) + εHeadline captures degree to which headline reverts to core (β1 = −1 implies full reversion within a year).
  - β2 in πCore_t − πCore_t−12 = α2 + β2(πHeadline_t−12 − πCore_t−12) + εCore captures degree to which core reverts to headline (β2 = 1 implies full reversion within a year).
- Findings:
  - Pre-2015: core inflation tended to increase when headline was above core (evidence of second-round effects); headline was generally unresponsive to deviations.
  - Post-2015: core inflation has not tended towards headline (no evidence of second-round effects); headline has tended towards core.
- Food inflation regressions:
  - Post-2015, food inflation (and the relative price of food) tended to decline when there was a positive gap between headline and core—this was not the case before 2015.
- Domestic vs global components (nine Asian countries sample):
  - Global component of relative food price is not responsive to the headline-core gap and shows no structural break.
  - India’s domestic component of relative food price showed a significant negative response to the headline-core gap after 2015 and was a key driver of the post-2015 negative response of headline inflation.

### Policy implications — model-based simulations
- Model used: variant of the RBI’s benchmark macroeconomic model (Benes and others (2017)), semi-structural, calibrated to India.
- Phillips-curve for core inflation in model:
  - πC_t = σ1 πC_{t+4} + (1−σ1) πC_{t−1} + σ2(Y_t^GAP + σ3 Z_t^GAP) + σ4(πH_t − πC_t) + ε_t
  - σ4 captures passthrough from headline to core (second-round effects).
- Calibrations compared:
  - Baseline (“more anchored inflation” variant): σ1 = 0.33 and σ4 = 0.05.
  - Alternative (“less anchored inflation” variant): σ1 = 0.05 and σ4 = 0.25.
  - All other equations and calibrations unchanged between variants.
- Shock scenario:
  - Start: 2019Q2.
  - Core inflation assumed to rise gradually to 6.0 percent by 2020Q1.
  - Food and fuel inflation assumed to rise to 7.0 percent by 2020Q1.
- Simulation outcomes:
  - To bring down simulated high inflation, interest rates must be increased.
  - Interest-rate increases are larger under the “less anchored inflation” variant, producing a larger output loss.
  - When inflation expectations are more anchored, inflation can be returned to target with smaller interest-rate increases and lower output losses.
- Note on endogenous credibility:
  - Incorporating endogenous central-bank credibility that responds to target achievement would strengthen the simulation results: success (failure) in meeting targets would further anchor (de-anchor) expectations.

### Conclusion — interpretation and caveats
- Documented a shift in India’s inflation process broadly coinciding with FIT adoption.
- Evidence indicates improved anchoring of inflation expectations since early 2015 and a prominent role for domestic factors—especially the domestic component of food-price inflation—in shaping these changes.
- Improved anchoring reduces second-round effects and makes headline deviations from core less persistent, implying a smaller medium-term impact of short-term headline developments on inflation expectations.
- Caveat: results are suggestive but do not definitively attribute changes to FIT adoption because of endogeneity of regime choice/timing, joint domestic/global factors, and coincident negative food-price shocks; subdued food-price inflation from persistent supply shocks could also underlie observed patterns.

*Source: wpiea2020251-print-pdf*

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

### I. INTRODUCTION

### Adoption of flexible inflation targeting (FIT) and recent inflation patterns
- The adoption of flexible inflation targeting (FIT) was a landmark step forward for the conduct of monetary policy in India.
- FIT has coincided with relatively low and more stable inflation in recent years, compared to a period of persistently high inflation previously.
- There was remarkably subdued food price inflation for an extended period—from late 2016 through mid-2019—coinciding with a marked divergence between food and core inflation.
- The apparent shift in the inflation process around 2015 has been recognized by previous studies (Dholakia and Kadiyala (2018) and Chinoy (2019)), but there is currently no consensus regarding the factors which drove it.
- Global factors noted in the source that could have weighed on headline inflation include a sharp fall in global oil prices in the second half of 2014 and slower growth in China; weaker headline inflation is not unique to India (Bems and others (2018b)).

### Comparative evidence from other EMDEs
- A simple event-study analysis shows that the transition to FIT has lowered emerging market and developing economy (EMDE) countries’ positive inflation differential, vis-à-vis a set of non-FIT peer countries.
- This pattern indicates a role played by domestic monetary institutions in affecting inflation dynamics (see also Bems and others (2018b)).

### Scope and methodological approach of the paper
- The paper studies how the inflation process in India has changed since the adoption of inflation targeting, which for analytical purposes is taken to have been at the beginning of 2015.
- Results are robust to choosing the adoption date as the beginning of September 2016; a robustness test also sets the FIT adoption date as the beginning of 2014 with broadly similar results.
- The analysis focuses specifically on the role played by domestic factors, and whether inflation expectations have become more anchored since early 2015.
- The paper examines headline and core inflation processes in India using standard Phillips curves (Gali and Gertler (1999)), and compares India with other emerging market and developing economies.
- The study sample for cross-country comparison includes 33 emerging market economies for which import price inflation can be constructed at quarterly frequency: Albania, Bolivia, Botswana, Brazil, Bulgaria, Chile, China, Colombia, Croatia, Dominican Republic, Ecuador, El Salvador, Honduras, Hungary, India, Indonesia, Malaysia, Mexico, Mongolia, Nicaragua, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russia, Rwanda, Serbia, South Africa, Thailand, and Turkey.

### Key empirical findings
- The inertia of headline inflation in India between 2004Q1 and 2019Q4 was stronger than in other emerging markets examined in the study, and the difference was entirely driven by the non-core inflation component.
- Measures of anchoring of inflation expectations:
  - Forecasters were less likely to revise medium-term inflation expectations in the period after 2015—the distribution of medium-term inflation expectations revision became narrower.
  - The sensitivity of medium-term inflation expectations to inflation surprises declined after 2015.
- Second-round effects analysis (based on Cecchetti and Moessner (2008) framework, extended to food price inflation):
  - It is more likely for headline inflation to have converged to core inflation rather than vice versa since early 2015, suggesting limited second-round effects.
- Overall interpretation:
  - Empirical results suggest more anchored inflation expectations and a shift in the inflation process, post-FIT adoption.
  - A vital caveat: persistent food-supply shocks—large and negative in recent years, causing food prices to remain low—may also underlie these structural changes; thus observed patterns do not necessarily imply a permanent structural change in the relationship between core and headline inflation.

### Evidence pointing to domestic factors driving the change
- The findings suggest that domestic factors rather than global forces stand behind changes in the inflation process, supported by:
  1. The import price pass-through is positive and significant for core inflation and insignificant for headline inflation. Given a large share of food in the consumption basket, this pattern implies segmentation between domestic and international food markets.
  2. Food price inflation played a significant role in the pre/post-2015 change of the convergence properties between headline and core inflation.

*Source: I. INTRODUCTION (excerpt) from wpiea2020251-print-pdf - References*

### 3. It is the domestic rather than international component of food price inflation that

### 3. It is the domestic rather than international component of food price inflation that became more likely to converge to core inflation after 2015

### Key findings
- Headline inflation in India has historically been substantially more backward-looking than in other emerging markets.
- Core and headline inflation processes are governed by different determinants in India, reflecting a prominent role for a non-core component (most likely food prices).
- Import-price pass-through to headline inflation has historically been weaker in India than in other emerging markets.
- Since early 2015 (the period corresponding to India’s FIT adoption), empirical patterns suggest inflation expectations became better anchored:
  - Medium-term inflation expectations became less likely to be revised.
  - Medium-term expectations became less sensitive to short-term inflation surprises.
- A structural break since 2015: core inflation no longer tends to revert to headline inflation (no evidence of second-round effects), whereas headline inflation tends to revert to core inflation.
- The domestic component of relative food-price inflation, rather than the global component, contributed to the weaker second-round effects after 2015.

### Empirical analysis — Phillips curve estimates
- Sample: 33 emerging market countries, period 2004Q1 to 2019Q4.
- Inflation expectations measures used:
  - Consensus Forecast: 2-year-ahead inflation expectations (for year t).
  - WEO forecasts: expected average change in the CPI index from t + 2 to t + 5 (annual WEO).
- Main empirical specification (hybrid Phillips-curve form, year-on-year changes) allows parameters to differ for India versus other EMDEs and includes:
  - Four-quarter-lagged inflation to capture backward-looking behavior.
  - Real-time output gap (HP-filtered trend of real GDP).
  - Import-price inflation in local currency.
- Empirical patterns from Table 1 (summary):
  - Headline inflation in India is more backward-looking; core inflation in India is more forward-looking relative to headline.
  - Both headline and core inflation are less responsive to the output gap in India than in other EMDEs.
  - Import-price inflation coefficient is smaller and less significant for India.

### Anchoring of inflation expectations
- Constructed measures:
  - Inflation surprise: revision in the current survey for expected change in headline CPI from January to December in the current calendar year compared with the previous survey.
  - Revision in medium-term forecast: analogous revision for CPI change from January to December, 2 years ahead.
- Distributional evidence (Figure 1): a narrowing of the distribution of two-year-ahead forecast revisions in India from 2015 onward, indicating tighter clustering around zero.
- Cross-country illustrative comparisons (Figure 2): India’s sensitivity of two-year-ahead expectations to inflation surprises declined since 2015; Chile exhibited well-anchored expectations both before and after 2015; Poland’s sensitivity was largely unchanged.

### Second-round effects and the role of food inflation
- Method: Monthly regressions examining how deviations between headline and core inflation predict subsequent changes in headline, core, and food inflation. Key equations:
  - Headline reversion: πHeadline_t − πHeadline_t−12 = α1 + β1(πHeadline_t−12 − πCore_t−12) + εHeadline
  - Core reversion: πCore_t − πCore_t−12 = α2 + β2(πHeadline_t−12 − πCore_t−12) + εCore
- Interpretation:
  - β1 captures degree to which headline reverts to core (β1 = −1 implies full reversion within a year).
  - β2 captures degree to which core reverts to headline (β2 = 1 implies full reversion within a year).
- Results (Table 2 summary):
  - Pre-2015: core inflation tended to increase when headline was above core (evidence of second-round effects); headline was generally unresponsive to deviations vis-à-vis core.
  - Post-2015: core inflation has not tended towards headline (no evidence of second-round effects); headline has tended towards core.
- Food inflation regressions (Table 3 summary):
  - Post-2015, food inflation (and the relative price of food) tended to decline when there was a positive gap between headline and core—this was not the case before 2015.
- Domestic vs global components:
  - For 9 Asian countries, residuals from country and year fixed-effect regressions of relative food price provide a domestic component (global trend filtered out).
  - Table 3 (columns 5–8) shows:
    - Global component of relative food price is not responsive to the headline-core gap and shows no structural break.
    - India’s domestic component of relative food price showed a significant negative response to the headline-core gap after 2015 and was a key driver of the post-2015 negative response of headline inflation.

### Policy implications — model-based simulations
- Model: Variant of the RBI’s benchmark macroeconomic model (Benes and others (2017)), semi-structural, calibrated to India.
- Phillips-curve in model for core inflation:
  - πC_t = σ1 πC_{t+4} + (1−σ1) πC_{t−1} + σ2(Y_t^GAP + σ3 Z_t^GAP) + σ4(πH_t − πC_t) + ε_t
  - σ4 captures passthrough from headline to core (second-round effects).
- Calibrations:
  - Baseline (termed “more anchored inflation” variant): σ1 = 0.33 and σ4 = 0.05.
  - Alternative (termed “less anchored inflation” variant): σ1 = 0.05 and σ4 = 0.25.
  - All other equations and calibrations unchanged between the two variants.
- Shock scenario (simulation details):
  - Start: 2019Q2.
  - Core inflation assumed to rise gradually to 6.0 percent by 2020Q1.
  - Food and fuel inflation assumed to rise to 7.0 percent by 2020Q1.
- Simulation outcomes (Figure 3 summary):
  - To bring down the simulated high inflation, interest rates must be increased.
  - Interest-rate increases are larger under the “less anchored inflation” variant, producing a larger output loss.
  - When inflation expectations are more anchored, inflation can be returned to target with smaller interest-rate increases and lower output losses.
- Note on endogenous credibility:
  - Incorporating endogenous central-bank credibility that responds to target achievement would strengthen the simulation results: success (failure) in meeting targets would further anchor (de-anchor) expectations.

### Conclusion
- Documented a shift in India’s inflation process that broadly coincided with FIT adoption.
- Evidence suggests improved anchoring of inflation expectations since early 2015 and a prominent role for domestic factors—especially the domestic component of food-price inflation—in shaping these changes.
- The improved anchoring reduces second-round effects and makes headline deviations from core less persistent, implying a smaller medium-term impact of short-term headline developments on inflation expectations.
- Model simulations quantify that better-anchored expectations materially reduce the output cost of restoring inflation to target after food/fuel-driven shocks.
- Caveat: results are suggestive but do not definitively attribute the changes to FIT adoption because of endogeneity of regime choice/timing, joint domestic/global factors, and coincident negative food-price shocks; subdued food-price inflation from persistent supply shocks could also underlie the observed patterns.

*Source: wpiea2020251-print-pdf*

### REFERENCES

### wpiea2020251-print-pdf - REFERENCES

### Works on India’s inflation dynamics and monetary policy
- Anand, R., D. Ding, and V. Tulin, (2014), “Food Inflation in India: The Role for Monetary Policy,” IMF Working Paper 14/178.
- Behera, H., G. Wahi, M. Kapur, (2018), “Phillips curve relationship in an emerging economy: Evidence from India,” Economic Analysis and Policy, Vol 59, Sept, pp 116-126.
- Benes, J., K. Clinton, A. George, P. Gupta, J. Joice, O. Kamenik, D. Laxton, P. Mitra, G. V. Nadhanael, R. Portillo, W Hou, and F. Zhang, (2017), “Quarterly Projection Model for India: Key Elements and Properties,” IMF Working Paper 17/33.
- Chinoy, S., (2019), “Parsing India’s inflation dynamics,” JP Morgan Chase Bank Economic Research Note, April 12.
- Dholakia, R. H., and Kadiyala, V. S., (2018) “Changing Dynamics of Inflation in India,” Economic & Political Weekly, Vol 53, Issue 9, March 3rd.
- Patra, M., J.K. Khundrakpam, and J. Joice, (2018), “Non-Linear, Asymmetric and Time-Varying Exchange Rate Pass-Through: Recent Evidence from India,” Reserve Bank of India Working Paper Series WPS 02/2018.
- Pattanaik, S., S. Muduli, and S. Ray, (2019), “Inflation Expectations of Households: Do They Influence Wage-Price Dynamics in India?” Reserve Bank of India Working Paper Series, WPS 01/2019.
- Raj, J., Misra, S., George, A.T., and John, J. (2020) “Inflation Measures in India – An Empirical Evaluation using CPI Data”, RBI Working Paper No. 05/2020
- Sonna, T., H. Joshi, A. Sebastian, and U. Sharma, (2014), “Analytics of Food Inflation in India,” RBI Working Paper Series 10/2014.
- Eichengreen, B., P. Gupta, R. Choudhary, (2020) “Inflation Targeting in India: An Interim Assessment” draft manuscript, 25 September.

### Research on inflation targeting, persistence, and international influences
- Bems, R., F. Caselli, F. Grigoli, B. Gruss, and W. Lian, (2018a), “Expectations Anchoring and Inflation Persistence,” IMF Working Paper 18/280.
- Bems, R., F. Caselli, F. Grigoli, B. Gruss, and W. Lian, (2018b), “Is Inflation Domestic or Global? Evidence from Emerging Markets,” IMF Working Paper 18/241.
- De Mendonca, H. F., and G.J. de Guimaraes e Souza, (2012), “Is inflation targeting a good remedy to control inflation?” Journal of Development Economics, 98, 178-91.
- Gali, J., and M. Gertler, (1999),"Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, 44, no. 2: 195-222.
- Gerlach, S., and P. Tillmann, (2012), “Inflation targeting and inflation persistence in Asia-Pacific,” Journal of Asian Economics, 23, 4: 360-373.
- Goncalves, C.E., and J.M. Salles, (2008), “Inflation targeting in emerging economies: what do the data say?” Journal of Development Economics, 85, 312-18.
- Lin, S. and H. Ye, (2009), “Does inflation targeting make a difference in developing countries?” Journal of Development Economics, 89, 118-23.
- Samarina, A., M. Terpstra, and J De Haan, (2014), “Inflation targeting and inflation performance: a comparative analysis,” Applied Economics Vol 46, Issue 1.
- IMF, (2018), “Regional Economic Outlook: Asia Pacific, Chapter 2 – Low Inflation in Asia: How Long Will it Last?” May.
- Cecchetti, S., and R. Moessner, (2008), “Commodity prices and inflation dynamics,” BIS Quarterly Review, December.

*Referenced in wpiea2020251-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020251-print-pdf.pdf_