april 2012 crisil insight

CRISIL InsightApril 2012

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Improving Inflation Marksmanship

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Last updated: 31 March, 2011

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An alternative core inflation indicator for India

A report by CRISIL Centre for Economic Research

Analytical contacts

Vidya Mahambare

Dipti Saletore

Anuj Agarwal

Director, Economy Research [email protected]

Economist [email protected]

Economist [email protected]

CRISIL Insight

We would like to acknowledge the contribution of Rahul Srinivasan, Harshal Bhavsar, Rashmi Parab

and Ranjana Balagopalan who helped in preparing the report.

Contents Page

Key messages........................................................................................ 1

Objective of the paper ........................................................................... 2

Part I - Constructing an alternative core inflation indicator.................... 3

Part II - Desirable properties of core inflation indicator:

Does CRISIL Core Inflation Indicator measure up? ................. 6

Key messages

n CRISIL Research has released an alternative indicator of core inflation

- CRISIL Core Inflation Indicator (CCII).

n CCII captures the underlying demand-side pressures on prices better

and is more stable than the existing core inflation measure - non-food

manufacturing inflation - which is the existing measure of core

inflation. CCII can therefore supplement the existing indicators that

influence the Reserve Bank of India’s (RBI) interest rate decisions.

n For the computation of CCII, we add back processed foods and take

out base metals from the existing measure of WPI-based core inflation

measure. Both measures exclude prices of primary articles and fuels

from the wholesale price index.

n CCII significantly improves upon the current measure of core inflation.

It reduces volatility by excluding base metals and captures demand-

side pressures more accurately by including processed food articles -

prices of which are primarily influenced by demand strength.

n In 2011-12, while the underlying trends of the two measures of core

inflation have been similar, CCII has declined more sharply. Average

CCII is likely to drop below 5.0 per cent in 2012-13 from nearly 7.0 per

cent in 2011-12.

n Inaccurate measurement of demand pressures by the existing core

inflation measure, we believe, adversely affected monetary policy

actions in the past. While the average CCII was 4.2 per cent in 2009-

10, the non-food manufacturing inflation declined sharply to 0.2 per

cent. This delayed policy tightening till March 2010.

n CCII also has higher correlation with inflation measured by GDP

deflator, the most comprehensive measure of inflation. This implies

that overall changes in prices in the economy are tracked more

accurately by the new measure.

1

CRISIL Insight

2

Objective of the paper

In this paper, we present a new measure of core inflation for India, which we

believe tracks demand-side pressures on inflation in a more accurate

manner as compared to the RBI preferred measure of core inflation.

Part I of the paper explains the construction and rationale behind - CRISIL

Core Inflation Indicator (CCII). It also discusses the difference between the

two measures of core inflation and its policy implications. Part II of the paper

elaborates on the desired properties of core inflation indicators and tests if

CCII meets these criteria.

Part I - Constructing an alternative core

inflation indicator

CRISIL Research has computed an alternative indicator of core inflation -

CRISIL Core Inflation Indicator (CCII) - which will improve the accuracy of

measuring underlying demand-side pressures on prices. CCII is computed

using manufacturing prices in India's wholesale price index after excluding

the 'base metals' category. It therefore allows for inclusion of all

manufactured items (including food and metal products), prices of which are

demand driven. In contrast, base metals prices are directly linked to

international prices and hence prone to high volatility.

Currently, the core inflation measure used by the RBI as one of the inputs for

monetary policy decision making as well as communicating with the public is

the non-food manufacturing inflation. Non-food manufacturing excludes raw

and processed food and fuel prices from the WPI basket.

The CCII captures demand-side pressures on prices better and is more

stable than the existing core inflation measure. In terms of weight in WPI,

CCII has a slightly higher weight of 55.9 per cent compared to 55.0 per cent

weight of the non-food manufacturing index.

Both the measures have moved in near-tandem since April 2010, and are

currently showing a decline since December 2011 (Figure 1). However,

during periods of adverse shocks to the global economy, CCII is less

influenced by temporary shocks and therefore, is more stable. It is thus, a

better indicator of persistent demand pressures in the Indian economy. For

instance, during 2009-10, while the non-food manufacturing inflation

measure suggested that core inflation had fallen to 0.2 per cent, CCII was still

high at 4.2 per cent.

Our calculations show that CCII had reached 6.0 per cent by December 2009

itself. RBI, however, had started raising policy interest rate only in March

2010 when, among other factors, non-food manufacturing inflation had

started approaching 5.0 per cent. At the time, low levels of non-food

manufacturing inflation, were largely a result of a collapse of international

base metal prices in the aftermath of the global economic crisis. In hindsight,

early anchoring of demand-side pressures could have helped tame down

inflation pressures more effectively during 2010-11 and 2011-12.

3

CRISIL Core Inflation Indicator (CCII)

CCII less vulnerable to temporary

shocks, hence more stable

Non-food manufacturing - the existing

measure of core inflation in India

CCII indicates, monetary tightening

likely to have been delayed, in

hindsight

CRISIL Insight

4

Monetary po l icy e f fec t iveness

dependent on reliable measurement of

demand-side inflation

Core inflation measure, believed to

have influenced monetary policy

actions in the past

WPI inflation in India has remained high and above RBI's comfort zone of 5

per cent in the last 6 years. Given the persistence in inflation, it has become

increasingly important for the RBI to enhance the effectiveness of policy

actions and communicate its intent clearly to the public. Monetary policy

aims to control inflation in the economy by ensuring that demand moves in

line with supply. Monetary policy effectiveness therefore, depends crucially

on reliable measurement of demand-side pressures on inflation. Such

measurement should effectively eliminate the effects of transitory supply

shocks, for instance, an oil price shock, which by itself, has less lasting

impact on inflation. To meet these objectives the central bank computes a

measure of core inflation for India which looks at non-food manufacturing

inflation.

A core inflation measure seeks to gauge demand-side pressures on inflation

by removing the effects of transitory supply shocks which, unlike demand-

side factors do not generally require monetary policy response. Monetary

policy actions work with a lag and hence accuracy in inflation projections is

critical. A reliable core inflation indicator must therefore be forward-looking

with reasonable degree of forecast accuracy.

Since March 2010, the RBI has often referred to the non-food manufacturing

measure, in several of its communications related to monetary policy

decisions. This measure, therefore, is believed to have influenced monetary

policy actions in recent years. Unlike the RBI's preferred core inflation

measure, CCII includes processed food prices, but excludes the prices of

base metals (ferrous and non-ferrous) from manufacturing inflation. CCII

includes processed food and metal products to take into account the second-

round of impact of supply shocks and it excludes base metal prices which are

directly influenced by international prices. Both core measures exclude the

prices of primary commodities and fuels which reflect the first-round impact

of supply shocks.

Figure 1: CRISIL Core Inflation Indicator

Source: Ministry of Industry and Commerce, CRISIL Research

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

CRISIL Core Inflation Indicator (CCII)

RBI’s preferred measure of core inflation

-non-food manufacturing

Jan

-00

Jul-0

0

Jan

-01

Jul-0

1

Jan

-02

Jul-0

2

Jan

-03

Jul-0

3

Jan

-04

Jul-0

4

Jan

-05

Jul-0

5

Jan

-06

Jul-0

6

Jan

-07

Jul-0

7

Jan

-08

Jul-0

8

Jan

-09

Jul-0

9

Jan

10

Jul-1

0

Jan

-11

Jul-11

Fe

b-1

2

%, y-o-y

Jan

-12

Figure 1 reveals the disparities in the information that CCII and the non-food

manufacturing inflation measure provide about demand-side pressures.

n In 2009-10, following the global economic crisis, while prices of non-food

manufacturing articles contracted during April-October 2009, CCII

declined, but never fell below 2.4 per cent during this period. A sudden

and sharp decline in international base metal prices during this period

was responsible for an equally sharp decline in non-food manufacturing

inflation in 2009-10.

n By December 2009, CCII had nearly touched 6.0 per cent, while non-food

manufacturing inflation was still hovering around 2.5 per cent implying

that the latter underestimated demand-side pressures.

n In recent years, while both measures of core inflation have moved in

tandem, CCII has generally been lower (except in 2009-10), but more

stable than non-food manufacturing index, even if we exclude the 2009-

10 episode. A similar difference in two measures of inflation was

witnessed in 2004-05, when prices of base metals witnessed a steep rise.

n In recent months, CCII has begun to decline since November 2011, a

month earlier than the non-food manufacturing inflation measure, and

has dropped more sharply thereafter. In January-Februrary 2012, CCII

was lower at 5.5 per cent average as compared to non-food

manufacturing inflation at 6.2 per cent.

n In 2012-13, we believe CCII would decline faster than non-food

manufacturing inflation measure, barring another collapse of

international metal prices. This reflects a sharper decline in demand

pressures on inflation.

The disparity in the two measures reflects the difference in movement of

prices of processed food and metals. Prices of processed food (included in

CCII; excluded from non-food manufacturing measure) rose by over 13 per

cent y-o-y in 2009-10. In contrast, prices of base metals (excluded from CCII;

included in non-food manufacturing measure) fell by over 8 per cent in 2009-

10, following the Lehman crisis.

The possibility that demand-side pressures were relatively firm in 2009-10

has significant policy implications. It suggests that the monetary policy

loosening post-October 2008 might have been sharper-than-warranted.

Further, subsequent interest rate hikes should have started much earlier than

March 2010. Had this happened, inflation rate during the last couple of years

could have been lower. Overall WPI inflation, however, would have continued

to hover above the RBI's threshold level as an expansionary fiscal policy (led

by sharp rise in government consumption expenditure) reduced the

effectiveness of monetary policy actions.

Disparity in two core inflation

measures, reflective of differences in

movements of base metal and

processed food prices

5

7

Computation of core inflation from CPI,

exclusion of metal prices, and inclusion of

processed foods - a global practice

CRISIL Insight

6

Part II - Desirable properties of core inflation

indicator: Does CCII measure up?

Across the world, several central banks (such as Bank of England, Reserve

Bank of New Zealand and Riksbanken - central bank of Sweden) monitor core

inflation through a variety of measures, which are typically constructed using

sub-categories of CPI data and hence exclude metal prices. Most core inflation

measures also tend to include processed foods (Table 1). These measures

either permanently exclude highly volatile prices (exclusion methods) or

exclude volatile components based on statistical results on a periodic basis

(statistical methods). In India, an early attempt at estimation of core inflation

was made by Mohanty, Rath and Ramaiah (2000). More recently, Durai, Sethu

& Ramachandran (2007), and Raj & Misra (2011) have estimated several

measures of core inflation for the country.

Table 1: Official Core Inflation Measures: Cross-Country Practices

Core Inflation Targeting

Countries Canada CPIX that excludes 8 most volatile components

like fruits, vegetables, gasoline, natural gas, fuel

oil, mortgage interest costs, intercity

transportation and tobacco products

Sweden CPI excluding interest and indirect tax

Norway CPI excluding tax and energy

New Zealand CPI excluding interest charges

Thailand Core CPI excludes fresh food and energy prices

which include rice, flour, cereal products,

vegetables, fruits, electricity charges, cooking

gas, and gasoline

Other countries with official core inflation measures

Japan CPI excluding fresh food

Peru CPI excluding 9 volatile items like food, fruits and

vegetables, urban transport

United States CPI excluding food and energy

Philippines CPI excluding rice corn fruits vegetables, LPG,

Kerosene, Oil, Gasoline, Diesel

Korea CPI excluding non-grain agricultural products and

petroleum products

Columbia CPI excluding agricultural food, public services

and transport

Spain CPI excluding energy and unprocessed food

Netherlands CPI excluding fruits, vegetables and energy

Portugal CPI excluding energy and unprocessed food

Source: Raj, J. & Misra, S (2011) Measures of Core Inflation in India – An Empirical Evaluation, RBI working paper No 16

Existing core inflation measure, prone

to volatility and less useful for

estimating future demand pressures

7

Desirable properties of core inflation are:

Globally, core inflation is usually calculated on the basis of the CPI after

eliminating certain food and energy products as their prices are highly

volatile and vulnerable to temporary domestic or global shocks. Moreover,

CPI, by construction, does not include base metal prices.

In India, the RBI calculates core inflation on the basis of WPI. The central

bank's preferred measure of core inflation excludes all food prices (raw and

processed) and energy prices.

This core measure used by the RBI has two drawbacks:

a) It is prone to volatility: it includes base metals prices, directly linked to

international price movements, which are influenced by temporary

shocks.

b) It is less useful for gauging future demand-side pressures: it

excludes processed food prices (manufactured food).

a) A good core inflation measure should exclude the impact of temporary

movement in overall inflation. It should reveal that component of overall

price change which is likely to persist for an extended period, and can be

easily forecasted. Base metals prices do not meet this criterion as they

are highly volatile and linked to international metal prices which are inturn

influenced by temporary supply shocks (Figure 2). The CCII therefore,

excludes this component in its calculation.

1.Core inflation should be less volatile than overall inflation and

should remove the impact of temporary shocks

Figure 2: WPI-base metals inflation vis-à-vis international base metals inflation

Note: International base metal prices are calculated by taking simple averages of inflation in base metal category commoditiesSource: CRISIL Research, Ministry of Industry

20.0

15.0

10.0

5.0

0.0

5.0

10.0

15.0

20.0

25.0

-70.0

-50.0

-30.0

-10.0

10.0

30.0

50.0

70.0

90.0

110.0

130.0

Mar-06 Jan-07 Nov-07 Sep-08 Jul-09 May-10 Mar-11

International inflation in base metals: left axis WPI-base metals inflation

%, y-

o-y

%, y-

o-y

9


Note: Data till February 2012

Base metals Metal products

Mean Volatility (standard deviation)


FY96-05

FY06-12

7.0

6.1

7.3

8.5

2.6

10.2

3.9

5.3

Table 2: Base metals and metal products inflation

Exclusion of base metals from core

inflation reduces volatility and impact of

temporary supply shocks

CRISIL Insight

8

For instance, in a recent RBI working paper on core measures of inflation in

India, Raj & Misra (2011) noted that volatility in domestic metal prices

increased in the 2000s vis-à-vis the 1990s, reflecting strong correlation with

global metal prices. Volatility in domestic prices of metals such as iron and

steel has been particularly high in recent years.

Prices of metal products, in contrast, mirror the second-round impact of

changes in the base metal prices, and thus, act as an indicator of demand

pressures in the economy (Table 2). During FY96-FY05, when economic

growth was relatively low, metal products inflation was at 2.6 per cent as

compared with base metals inflation of 7 per cent. But during a relatively high-

growth phase of FY06-FY12, despite lower base metals inflation, at 6.1 per

cent, metal products inflation shot up significantly to 10.2 per cent.

Table 3 WPI and Core Inflation Measures(April 2005 to February 2012)

Weight Mean Standard Deviation

Coefficient of Variation

Volatility around Trend (annual)

Headline WPI

Non-food manufacturing

CCII

100.0

55.0

55.9

6.6

4.7

4.7

3.0

2.7

1.5

8.8

7.2

2.4

1.1

1.3

1.0


Going forward, global metal prices are likely to remain volatile since price

contracts of iron ore and other metals have been converted into quarterly from

annual contracts. If, in future, the contracts are moved to the monthly basis, it

would bring further volatility to the measure of non-food manufacturing

inflation which includes base metals prices. In addition, domestic metal prices

are also influenced by temporary fluctuations in the value of rupee, which we

believe will remain weak atleast during 2012-13 vis-à-vis the dollar.

Based on this evidence, we believe, while metal products prices should be

included in a measure of core inflation, base metals prices should be

excluded - as is the international practice - to reduce volatility and temporary

fluctuations.

b) A measure of core inflation should not only eliminate volatility, but should

also be able to gauge demand pressures. The exclusion of processed food

prices from non-food manufacturing inflation, the RBI's preferred core

inflation measure, defeats this purpose.

Primary food inflation has become persistent in nature at 10.7 per cent

average in the April 2005 to January 2012 period. This structural shift in

primary food inflation, backed by strong demand has yielded into second-

round impact on manufactured food inflation (Table 4), which remained

elevated even during 2009-10 when non-food manufacturing inflation

declined sharply (Figure 3). This makes it important to include the

manufactured food prices in core inflation measure to aptly gauge demand-

side pressures. Going ahead, if global food prices continue to trend upwards

due to persistent demand pressure, high food inflation may no longer be a

temporary phenomenon.

Prices of processed food also provide information about future inflation.

Producers of processed food tend to change their prices infrequently, even

though their production costs fluctuate frequently. Knowing that their next

price adjustment may take some weeks or months, such producers need to

be forward-looking when setting prices. If they perceive a temporary jump in

the prices of their inputs - basic food, they may not fully pass on the higher

input cost into their price. If however, they see a more permanent increase in

prices of their inputs and a commensurate increase in demand for their

products, they may increase the price of their products. Movements in prices

for these sorts of items thus provide information about future price

developments. In sum, we believe processed food prices should not be

excluded from a measure of core inflation for India.

9

Inclusion of processed foods in the core

inflation measure allows for more

accurate estimation of demand-side

pressures on manufactured inflation

CRISIL Insight

Figure 3 Inflation in manufactured food and non-food manufacturing

-5.0

0.0

5.0

10.0

15.0

20.0

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4*

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

Manufactured food Non - food manufacturing inflation%, y-o-y

Note: *Data for Q4 2011-12 is only for January-February 2012Source: Ministry of Industry and Commerce, CRISIL Research

Primary food articles Manufactured food products



FY96-05

FY06-12

5.3

10.0

4.9

5.2

4.5

6.2

4.6

4.4

Table 4: Primary and manufactured food inflation


Note: Data till February 2012

Figure 4: GDP deflator inflation and core inflation measures


0

2

4

6

8

10

12

CRISIL Core Inflation Indicator Non food manufacturing inflation

GDP Deflator

20

00

-01

20

01

-02

20

02

-03

20

03

-04

20

04

-05

20

05

-06

20

06

-07

20

07

-08

20

08

-09

20

09

-10

20

11-1

2*

20

00

-01

20

01

-02

20

02

-03

20

03

-04

20

04

-05

20

05

-06

20

06

-07

20

07

-08

20

08

-09

20

09

-10

20

11-1

2*

20

10

-11

%, y-o-y

20

10

-11

10

2.Core inflation measure should be able to predict future trends

in overall inflation

Since monetary policy changes influence inflation with a lag, policy actions

are largely based on forecast of inflation. It is therefore, critical that core

inflation be able to predict future inflation with reasonable accuracy. The most

comprehensive measure of inflation in a country is a percentage change in

GDP deflator. CCII tracks overall inflation in the economy as reflected in GDP

deflator better than the non-food manufacturing inflation (Figure 4).

Since the beginning of the last decade, inflation as measured by changes in

GDP deflator has moved directionally in line with CCII. Based on quarterly

data since 2005-06, while the correlation between changes in GDP deflator

and CCII is around 0.79 for, it is only 0.52 for changes in GDP deflator and the

non-food manufacturing inflation measure.

Preliminary statistical exercise reveals high correlation of both measures of

core inflation with WPI inflation of around 0.82 between April 2005 and

February 2012. Although the overall correlation between the two measures of

core inflation with WPI inflation is similar, the ratio of WPI inflation to CCII

however is relatively stable implying that during volatile period, CCII is a

better gauge for underlying inflationary trends.

To serve as an indicator for future headline inflation, core inflation measure

should be able to forecast its own future trends. A preliminary exercise 1suggests that core inflation projections based on the ARIMA forecasting

method for CCII are significantly better than that for the non-food

manufacturing inflation measure. The forecast errors (% difference between

forecast and actual values) are significantly smaller (Table 5) than for the non-

food manufacturing inflation. This means that CCII has higher forecast

accuracy. Current data for CCII has better predictive abilities than non-food

manufacturing measure. Forecast errors, are also largely unidirectional with

the actual values being higher than the forecast except in 2010-11 which

makes it easier to make out-of-model adjustments, if necessary, in the

forecast of CCII.

Once the forecasts for core inflation are generated, information based on

assumptions for the balance components of WPI inflation (viz. primary

articles, fuel and base metals) can be included to arrive at a forecast for

overall inflation.

1ARIMA – Autoregressive Integrated Moving Average – models describe the current behavior of a variable in

terms of linear relationships with their past values. While the basic ARIMA models do not incorporate future information, it is the most general form of modeling a time series which displays high persistence.

11

CCII has higher forecast accuracy

Inflation forecasts, critical for policy

actions

13

Non-metal manufacturing Non-food manufacturing

2008-09

2009-10

2010-11

2011-12

2012-13

Forecast Actual Forecast Actual

5.0 5.2 6.6 5.7

3.6

5.5

6.2

4.0

4.2

5.3

7.1*

-

1.6

4.7

9.9

5.3

0.2

6.1

7.5*

-

Note: *Data till February 2012Source: Ministry of Industry and Commerce, CRISIL Research

Table 5: Comparison of actual v/s ARIMA out of sample forecast

Concluding Remarks

References:

There is no single ideal measure of core inflation which would necessarily

outperform all other measures across all time periods. Hence, it is better to

judge inflation pressures on the basis of different measures which together

provide a coherent picture of overall inflation dynamics. According to our

analysis, of the two measures of core inflation – non-food manufacturing and

CRISIL Core Inflation Indicator – the latter is less prone to supply-side shocks

and is therefore less volatile. CCII also allows for better understanding of

underlying demand pressures on inflation, and has better predictive abilities.

CCII can therefore be an appropriate tool for policymakers to take effective

monetary policy decisions.

Durai, S., Raja Sethu, and M. Ramachandran. "Core Inflation for India." Journal

of Asian Economics 18(2), April 2007: 365-383.

Mohanthy, D., D.P. Rath, and M. Ramaiah. "Measures of Core Inflation for

India. “Economic and Political Weekly, January 2000: 273-283.

Raj, J. and S. Misra. "Measures of Core Inflation in India - An Empirical

Evaluation.” 2011: RBI Working Paper No.16. 2011.

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n Assigned the first IPO grade in India

n Largest team of economy and industry research analysts in India

n Coverage on 70 industries and 139 sub-sectors; provide growth forecasts, profitability analysis, emerging trends, expected investments, industry structure and regulatory frameworks

n 90 per cent of India’s commercial banks use our industry research for credit decisions

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n Inputs to India’s leading corporates in market sizing, demand forecasting, and project feasibility

n Published the first India-focused report on Ultra High Net-worth Individuals

n All opinions and forecasts reviewed by a highly qualified panel with over 200 years of cumulative experience

Making Markets Function Better

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april 2012 crisil insight

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