Regional Inflation Dynamics In India*
Regional Inflation Dynamics in IndiaRegional Inflation Dynamics inIndia*An analysis of the regional inflation dynamics in Indiareveals the presence of wide dispersion in inflation acrossstates, largely driven by food price inflation. State levelinflation tends to converge to the national average overtime, however, validating the choice of national levelconsumer price inflation as the nominal anchor formonetary policy in India.IntroductionWith the adoption of a flexible inflation targeting(FIT) framework in India with consumer price inflation(all-India combined) as the numerical target, a path ofdisinflation has brought down inflation from 11.5 percent in November 2013 to an average level of 3.6 percent in 2017-18. This receding of inflation has not beeneven though, marked as it has been by seasonal surges,disruptive shocks including demonetisation, theGoods and Services Tax (GST), farmers’/transporters’agitations, a deep downturn in food inflation on acombination of cyclical, irregular and policy-relatedforces and high volatility in international crude prices.As a result, inflation has generally eased across states,but with wide variations.For an inflation targeting (IT) central bank,regional heterogeneity in price movements could havea significant impact on the effectiveness of monetarypolicy. Large inflation differentials among regionswithin an economy can lead to significant variationsin real interest rates and consequently, in levels ofaggregate demand (Cecchetti et al, 2002; Beck et al,*This article is prepared by Smt. Sujata Kundu, Shri Vimal Kishore and ShriBinod B. Bhoi in the Prices and Monetary Research Division of theDepartment of Economic and Policy Research, Reserve Bank of India. Theauthors sincerely thank Shri S. Pattanaik, Adviser, DEPR, for his valuablesuggestions. The views expressed in the article are those of the authors anddo not represent the views of the Reserve Bank of India.RBI Bulletin November 2018article2009). High dispersion of inflation across regionscould also have implications for the labour markets interms of wage rates and standards of living. Ignoringthe regional dimensions of inflation may limit theeffectiveness of a nationally set monetary policy insatisfying the needs of all regions equally (Beck andWeber, 2005; Weyerstrass et al, 2011). In the Indiancontext, ensuring that the benefits of low and stableinflation accrue across regions and states is critical foranchoring the credibility of the new monetary policyframework and for incentivising buy-in by the widestsections of society. Wide disparities across Indian statesin terms of economic, geographic and structural factorswarrant a careful examination of their role in regionalinflation dispersion and hence on national inflation.Additionally, as the all-India consumer price index(CPI) is compiled as a weighted average of the statelevel price indices, i.e., a bottom-up approach, relativeprice movements across states will have a bearingon overall inflation outcomes. Accordingly, drillingdown into the dynamics of regional inflation formationin India is the main motivation for this article. Tobriefly summarise, it finds that there is considerableregional dispersion, although largely influenced bysupply side food price shocks. The estimated kerneldensity function as well as beta (β) convergence testsconfirm that regional inflation tends to convergetowards the national average inflation during thesample period.The remainder of the article is structured intofive Sections. Section II provides a detailed analysis ofinflation and its volatility at the national and regionallevels as well as at aggregate and disaggregate levelsto understand the pattern and driver of regionalinflation dispersion in India. Section III draws onselect contributions to the theoretical and empiricalliterature on regional inflation dynamics andmonetary policy from a cross-country perspective.The convergence of inflation rates across states tothe national inflation level is tested empirically in57
articleRegional Inflation Dynamics in IndiaSection IV to examine whether an inflation target atthe national level is appropriate. Section V providesconcluding observations and policy implications.II.Some Stylised National/Regional FeaturesBeginning in December 2013, headline CPIinflation1 has eased from an average of 10.0 percent in 2012-13 to 3.6 per cent in 2017-18 and 4.2per cent in the first seven months of the currentfiscal year (April-October, 2018). Although a de jureflexible inflation targeting was established inSeptember 2016, the path to its adoption was laidby de facto pre-commitments that initiated thedisinflation and consolidated the gains accruingtherefrom2 (Chart 1).In line with the all-India trend, inflation alsomoderated across states (Chart 2), albeit with widevariations relative to the former.Notably, all the southern states had higher averageinflation than northern states like Punjab, Haryana,Uttar Pradesh and Uttarakhand as well as states in1Headline inflation is measured by year-on-year changes in the all India CPI-C (Rural Urban) with base year: 2012 100 released by the Central StatisticsOffice (CSO), Ministry of Statistics and Programme Implementation, Government of India.2In January 2014, the Reserve Bank adopted a self-imposed target to bring down headline CPI inflation in a sequential manner - to 8 per cent by end-2014,6 per cent by end-2015 and 5 per cent by end-2016 - which is called the glide path for inflation (Patra, 2017). A flexible inflation targeting (FIT) monetarypolicy framework was provided a statutory basis with the amendment to Reserve Bank of India (RBI) Act in May 2016, under which price stability has beenmandated as the primary objective of monetary policy, while keeping in mind the objective of growth. Price stability has been defined in terms of a numericalinflation target (year-on-year change in the consumer price index-combined, i.e., CPI-C) set by the government at 4 per cent with an upper tolerance levelof 6 per cent and a lower tolerance level of 2 per cent.58RBI Bulletin November 2018
articleRegional Inflation Dynamics in Indiaother regions like Maharashtra and Madhya Pradesh.Bihar recorded the highest inflation of 16.1 per cent(November 2013), while Chhattisgarh recorded thelowest inflation level of (-) 2.3 per cent (June 2017)as against the national-level maximum of 11.5 percent (November 2013) and minimum of 1.5 per cent(June 2017).Intra-year volatility (measured by the standarddeviation of monthly year-on-year (y-o-y) inflationrates) varied considerably at both all-India and statelevels (Chart 3). Generally, headline inflation volatilityhas increased, barring a blip in 2015-16, in spite ofliterature3. In fact, when inflation averaged a high of10.0 per cent in 2012-13, its volatility was at the lowestin the period of study at 0.5 per cent; volatility rose to 1.2per cent when average inflation was at its lowest levelof 3.6 per cent in 2017-18 (Chart 4a). This relationshipalters dramatically, however, in the regional setting.Unlike the all-India pattern, state-level inflation andinflation volatility co-moved during 2012-13 to 2017-18(Chart 4b). Another interesting observation is that thestates/regions that experienced high average inflation(e.g., Bihar, Chhattisgarh, Odisha and West Bengal) alsorecorded high volatility in inflation.did not exhibit any noteworthy co-movement, whichAt a disaggregated level, all-India headlineinflation was driven largely by the movements in foodinflation (Chart 5a). In fact, the sharp moderation ininflation during 2017-18 can be largely attributed tofood inflation, with its contribution to overall inflationfalling below 30 per cent from an average of 52 percent in the previous five years (Chart 5b). Other majorcontributors were the miscellaneous group (whichcovers miscellaneous goods and services includingis in contrast with the two-way causality posited in thepetroleum products) and housing rentals.the moderation in mean inflation. A similar patternis observed at the state level, with inflation volatilitybecoming more pronounced than at the nationallevel, with states in the central and eastern regionsexperiencing higher inflation volatility than the otherregions and at the all-India level (Table 1).Over this period, inflation and inflation volatility3According to the Friedman-Ball hypothesis, a rise in inflation raises inflation volatility; on the other hand, according to the Cukierman-Meltzer arguments,higher inflation volatility fuels inflation (Kim and Lin, 2012; Hossain and Arwatchanakarn, 2016).RBI Bulletin November 201859
articleRegional Inflation Dynamics in IndiaTable 1: Regional CPI-C Inflation – Key Summary Statistics (2012-13 to 2017-18)MaximumMinimumStandard DeviationSkewnessKurtosisNorthern khand1.065.611.5Western harashtra8.35.910.3Rajasthan6.66.711.1Central 1.7Eastern 13.5Odisha2.936.615.2WB6.996.513.6Southern Region24.706.811.5Andhra 711.1Tamil Nadu5.556.911.9Telangana3.166.913.8North-eastern Region3.906.311.6Of which, Assam2.636.111.7Union Territories (UTs)0.56.310.8All India100.006.411.5Note: North-eastern states and UTs are shown as groups for better representation.Source: CSO; and RBI staff estimates.Weights in All India 60MeanRBI Bulletin November 2018
Regional Inflation Dynamics in IndiaFood inflation also exhibited the highest volatility(Chart 6), which, in turn, was transmitted to overallinflation, given the large weight of food (45.9 per cent)in the all-India CPI-C.At the sub-national level, there exists a positiverelationship between average food inflation andoverall inflation (Chart 7a). Similarly, a positiverelationship between overall inflation volatility andRBI Bulletin November 2018articlefood inflation volatility can be observed across states(Chart 7b).Overall, there seems to exist a strong comovement between the inflation spread (measuredas state headline inflation minus all-India headlineinflation) and its volatility with the food inflationspread (measured as state food inflation minusall-India food inflation) and its volatility across states61
articleRegional Inflation Dynamics in India(Chart 8), with possible externalities for inflationvariable while controlling for differences in incomeexpectations.levels across states through gross state domesticA simple panel regression4 covering all states withproduct (GSDP) growth spread (measured as stateGSDP growth minus all India GDP growth) reveals thatthe food inflation spread as the explanatory variable69 per cent of the variation in the inflation spread isand the headline inflation spread as the dependentexplained by food inflation spread alone (Table 2).The Breusch-Pagan Lagrange Multiplier (LM) test suggests that an ordinary least squares (OLS) regression is better suited than a random effects panelregression, although there are no major changes in the coefficients and their level of significance between the two models in our results.462RBI Bulletin November 2018
articleRegional Inflation Dynamics in IndiaTable 2: Results of the Panel RegressionExplanatoryVariablesDependent Variable: Headline Inflation Spread(20 States#; Period : 2012-13 to 2016-17)role played by the exchange rate mechanism (ERM)(Busetti et al, 2007). A single monetary policy for theEuro area appears to have helped to stabilise inflationCoefficientt-valueacross member countries to a large extent. Evidence offood inflation spreadit0.698.80***GSDP growth spreadit-0.04-1.27divergence was also found, with inflation differentialsconstant0.111.13No. of observationsF (2, 97)R squaredacross European regions observed to be large and also100long-lasting (Beck et al, 2005; 2009). By contrast, price39.18***levels among cities in the US are observed to revert0.75to mean at an exceptionally slow rate (Cecchetti etNote: ***: represents level of significance at 1 per cent.#: Includes Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, HP, J&K,Karnataka, Kerala, Tamil Nadu, MP, Maharashtra, Odisha, Punjab,Rajasthan, UP, WB, Manipur, Meghalaya, Tripura.Source: CSO; and RBI staff calculations.The following equation is estimated in theregression:Headline inflation spreadit α β Food inflation spreadit γGSDP growthspreadit εitwhere, i stands for state, t stands for year and ε is theerror term.al, 2002), while socio-economic factors like income,wages, demographic structure and housing pricegrowth explain regional price dispersions in Korea(Chang and Kim, 2017). Regional inflation and itsvolatility were higher in the post South-East Asiancrisis period (September 1999 - July 2006) among 26regions in Indonesia than during the pre-crisis years(Wimanda, 2006). For OECD economies, the adoptionof inflation targeting contributed to a higher degree ofdisinflation (Ball and Sheridan, 2004).economic, institutional and financial structures;For India, significant cross-sectional dependencein prices across regions is observed for data on centrewise CPI for Industrial Workers (CPI-IW), althoughrelative price levels in various regions tend to meanrevert (Das and Bhattacharya, 2008). The strengtheningof institutions on spatial competition – productmarket reform (measured by state easing barriers toentrepreneurship and opening up to internationaltrade and investment) – could lead to convergence ofinflation among states (Pillai et al, 2012).differences in product and factor markets and theIV.III. The Lessons from the LiteratureThe issue of regional inflation dynamics andconvergence has attracted attention, particularlyafter the introduction of the Euro (Cecchetti et al,2002; Beck et al, 2005; 2009). The primary focus hasbeen to check the validity of the law of one price ina monetary union. Several factors have been cited- national policies designed by the government;stage of economic development that the region isgoing through (Hendrikx and Chapple, 2002). Regionswith high shares of food in consumption baskets aswell as those that are heavily dependent on importingfood tend to experience higher inflation than otherregions.Analysis of inflation dispersion in the Euro areaduring 1980-2004 has found evidence supportingthe convergence hypothesis – an indication of theRBI Bulletin November 2018Testing for ConvergenceGiven that food inflation spread drives theoverall inflation spread as discussed earlier and thatthe food inflation spread fluctuates within a narrowerrange than spreads in respect of other componentsof inflation (Table 3), the estimated kernel density(Epanechnikov kernel, bandwidth 0.40) of theannual average deviations of the regional inflationrates from the all-India average between April 2012 andMarch 2018 moves in a range of about 15 percentage63
articleRegional Inflation Dynamics in IndiaTable 3: Inflation – Minimum, Maximum and Average Inflation Spread Volatility across States(April 2012 to March 2018)Sub-groupsMinimum Inflation(in per cent)Maximum Inflation(in per cent)Difference betweenMaximum and MinimumInflation Spread Volatility(in percentage points)Food and beverages (45.9)5.88.72.90.67Pan, tobacco and intoxicants (2.4)5.113.28.11.36Clothing and footwear (6.5)5.010.25.21.03Housing (10.1)3.49.05.61.39Fuel and light (6.8)-0.111.011.12.06Miscellaneous (28.3)2.96.83.90.83CPI-C (100)5.47.92.50.60Note: Figures in parentheses indicate the group’s weight in overall CPI-C. Inflation spread volatility is measured by the cross-sectional standard deviationof inflation divergence from all-India average.Source: CSO; and RBI staff estimates.points in the inflation spread experienced by differentstates in India (Chart 9)5. Further, the plot is more orless symmetric, implying that state-level inflationrates tend towards the national average inflation.The distribution also seems to be quite leptokurtic innature, which could be due to the role of local priceshocks in a few states in certain periods.This observation seems to be validated by trendsin cross-sectional variability in inflation differentialsand the average inflation differentials (Chart 10).Against this backdrop, inflation convergence istested in a random effects panel regression model(Table 4). As the Breusch-Pagan Lagrange Multiplier(LM) test suggests that an OLS regression is better suitedthan a random effects panel regression, the OLS resultsare also reported as a robustness check here (Table5). The most widely used measures of convergenceavailable in the literature are beta (β )-convergenceand sigma (σ )-convergence (Busetti et al., 2007;Lopez and Papell, 2012; Barro and Sala-i-Martin, 1992;Mankiw et al., 1992). σ-convergence occurs when thedispersion of the levels of a given variable betweenTable 4: Results of the Beta Convergence Test6Explanatory VariablesCoefficientZ-valueinflation spreadit-1-0.77-6.06***constant0.242.16**No. of observationsWald chi2(1)R-squaredA kernel density plot is equivalent to a smoothened histogram. Histogramsare limited by the fact that they are inherently discrete (via bins) and canbe very sensitive to bin size. A kernel density estimation, on the other hand,is a non-parametric way of estimating the probability density function of arandom variable. The area under the curve between any two data points,say x1 and x2, estimates the probability of the random variable X fallingbetween x1 and x2, assuming that X was generated by the same process thatgenerated the data which was fed into the kernel density estimate.564Dependent Variable: Inflation Spread(36 States and UTs; Period : 2012-13 to 2017-18)18036.75***Within: 0.45; Between: 0.19; Overall: 0.41.Note: ***, ** and * represent levels of significance at 1 per cent, 5 per centand 10 per cent, respectively.Source: RBI staff estimates.6Coefficients and Z values correspond to robust standard errors. A random effects generalized least squares regression was carried out. Thechoice between random effects and fixed effects panel estimation wasbased on the Hausman test.RBI Bulletin November 2018
articleRegional Inflation Dynamics in Indiadifferent regions tends to decrease over time. InIndia inflation, i stands for state, t stands for year andcontrast, β convergence allows the identification ofε is the error term. The size of β measures the speed ofa common benchmark (Beck and Weber, 2005)7.coefficient signals the existence of convergence andβ convergence requires the estimation of the followingthe closer the absolute value of the β coefficientis to 1, the higher is the speed of convergence. The inflation spreadit α β inflation spreadit–1 εitconvergence, i.e., convergence of regional inflationthe speed with which shocks dissipate across regionseven as the variable of interest converges towardsequation:where, is the difference operator, inflation spreadmeasures the difference between state inflation and allTable 5: Results of the Beta Convergence TestDependent Variable: Inflation SpreadExplanatory Variables (36 States and UTs; Period : 2012-13 to 2017-18)Coefficientt-valueinflation spreadit-1-0.77-6.85***constant0.241.85*No. of observations180F (1, 178)46.90***R squared0.4148Note: ***, ** and * represent levels of significance at 1 per cent, 5 per centand 10 per cent, respectively.Source: RBI staff estimates.7The literature also suggests bi-directional relationship between inflationand inflation volatilit
Regional Inflation Dynamics in India Food inflation also exhibited the highest volatility (Chart 6), which, in turn, was transmitted to overall inflation, given the large weight of food (45.9 per cent) in the all-India CPI-C. At the sub-national level, there exists a
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