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KINGA MM ARKETSF
UNCTIONBETTE
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Improving Inflation Marksmanship
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An alternative core inflation indicator for India
A report by CRISIL Centre for Economic Research
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Analytical contacts
Vidya Mahambare
Dipti Saletore
Anuj Agarwal
Director, Economy Research [email protected]
Economist [email protected]
Economist [email protected]
CRISILInsight
We would like to acknowledge the contribution of Rahul Srinivasan, Harshal Bhavsar, Rashmi Parab
and Ranjana Balagopalan who helped in preparing the report.
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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
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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 Indias (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 inflationmeasure. 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
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CRISILInsight
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.
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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 hadstarted 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 temporaryshocks, 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
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CRISILInsight
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 manufacturingmeasure, 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)
RBIs preferred measure ofcore inflation
-non-food manufacturing
Jan-00
Jul-00
Jan-01
Jul-01
Jan-02
Jul-02
Jan-03
Jul-03
Jan-04
Jul-04
Jan-05
Jul-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan10
Jul-10
Jan-11
Jul-11
Feb-12
%, y-o-y
Jan-12
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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
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Computation of core inflation from CPI,
exclusion of metal prices, and inclusion of
processed foods - a global practice
CRISILInsight
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
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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--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
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Source: Ministry of Industry and Commerce, CRISIL Research
Note: Data till February 2012
Base metals Metal products
Mean Volatility(standarddeviation)
Mean Volatility(standarddeviation)
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
CRISILInsight
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--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 StandardDeviation
Coefficientof Variation
VolatilityaroundTrend(annual)
Headline WPI
Non-foodmanufacturing
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
Source: Ministry of Industry and Commerce, CRISIL Research
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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, itwould 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--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 themanufactured 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
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CRISILInsight
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 inf lation%, 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
Mean Volatility(standarddeviation)
Mean Volatility(standarddeviation)
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
Source: Ministry of Industry and Commerce, CRISIL Research
Note: Data till February 2012
Figure 4: GDP deflator inflation and core inflation measures
Source: Ministry of Industry and Commerce, CRISIL Research
0
2
4
6
8
10
12
CRISIL Core Inflation Indicator Non food manufacturing inflation
GDP Deflator
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2011-12*
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2011-12*
2010-11
%, y-o-y
2010-11
10
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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 deflatorand 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 exercise1
suggests 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 interms of linear relationships with their past values. While the basic ARIMA models do not incorporate futureinformation, 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
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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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18/2015
CRISIL Centre for Economic Research (C-CER)
Macroeconomics:
Financial Economics:
Environmental Economics:
CRISIL EcoView
The Centre for Economic Research is a division of CRISIL. Set up in April 2002, C-CER reflects CRISIL's commitment to provide
an integrated research offering to help corporates and policy makers take more informed business decisions.
C-CER applies sound economic principles to real world applications, creating conceptual and contextual linkages that are
unique to CRISIL. C-CER also supports Standard & Poor's Asia Pacific by analysing and forecasting macroeconomic variables
for 14 countries in the region.
C-CER's core strengths emerge from a strong understanding of and capabilities in the following areas:
Regular monitoring and forecasting of macroeconomic indicators, assessment of domestic and global
events, and analysis of longterm structural changes in the economy.
Analysis and forecasting of interest rates and exchange rates.
Public Finance: Analysis and forecasting of central and state government revenues, expenditures and borrowing requirements.
Analysis of Indian firms' impact on environmental, social and governance parameters.
C-CER reviews developments in the Indian economy on a monthly basis and provides its outlook on the economy through a
dedicated publication .
CRISIL EcoView is used by CEOs, CFOs, economists, corporate strategy teams, marketing teams, treasuries and knowledge
management teams of various corporates and management consultancy firms to make appropriate strategy level decisions.
The C-CER team comprises senior economists with over a decade's experience of working with premier research institutes.
Dharmakirti Joshi
Sunil K. Sinha
Vidya Mahambare
Parul Bhardwaj
Dipti Saletore
Anuj AgarwalAindrila Roy Chowdhury
Senior Director and Chief Economist
Director
Director
Economist
Economist
EconomistEconomist
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Our Capabilities
Economy and Industry Research
Funds and Fixed Income Research
n Largest and most comprehensive database on Indias debt market, covering more than 14,000securities
n Largest provider of fixed income valuations in India
n Value more than Rs.33 trillion (USD 650 billion) of Indian debt securities, comprising 85 per cent ofoutstanding securities
n Sole provider of fixed income and hybrid indices to mutual funds and insurance companies; we maintain12 standard indices and over 80 customised indices
n Ranking of Indian mutual fund schemes covering 73 per cent of assets under management andRs.5 trillion (USD100 billion) by value
n Retained by Indias Employees Provident Fund Organisation, the worlds largest retirement schemecovering over 50 million individuals, for selecting fund managers and monitoring their performance
Equity and Company Research
n Largest independent equity research house in India, focusing on small and mid-cap companies;
coverage exceeds 100 companiesn Released company reports on all 1,401 companies listed and traded on the National Stock Exchange; a
global first for any stock exchange
n First research house to release exchange-commissioned equity research reports in India
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 Indias commercial banks use our industry research for credit decisions
n Special coverage on key growth sectors including real estate, infrastructure, logistics, and financialservices
n Inputs to Indias leading corporates in market sizing, demand forecasting, and project feasibility
n
Published the first India-focused report on Ultra High Net-worth Individualsn All opinions and forecasts reviewed by a highly qualified panel with over 200 years of cumulative
experience
Making Markets Function Better
KINGA MM ARKETSF
UNCTIONBETTE
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CRISIL LimitedCRISIL House, Central AvenueHiranandani Business Park, Powai, Mumbai - 400 076. IndiaPhone: +91 22 3342 3000 | Fax: +91 22 3342 8088www.crisil.com
CRISIL Ltd is a Standard & Poor's company
Our Offices
Ahmedabad706, Venus Atlantis
Nr. Reliance Petrol Pump
Prahladnagar, Ahmedabad, India
Phone: +91 79 4024 4500
Fax: +91 79 2755 9863
Bengaluru
W-101, Sunrise Chambers
22, Ulsoor Road
Bengaluru - 560 042, India
Phone: +91 80 2558 0899
+91 80 2559 4802
Fax: +91 80 2559 4801
Chennai
Thapar House,
43/44, Montieth Road, Egmore
Chennai - 600 008, India
Phone: +91 44 2854 6205/06
+91 44 2854 6093
91 44 2854 7531Fax: +
Hyderabad
3rd Floor, Uma Chambers
Plot No. 9&10, Nagarjuna Hills
(Near Punjagutta Cross Road)
Hyderabad - 500 482, IndiaPhone: +91 40 2335 8103/05
Fax: +91 40 2335 7507
KolkataHorizon, Block 'B', 4th Floor
57 Chowringhee Road
Kolkata - 700 071, India
Phone: +91 33 2289 1949/50
Fax: +91 33 2283 0597
New Delhi
The Mira, G-1
1st Floor, Plot No. 1 & 2
Ishwar Nagar, Mathura Road
New Delhi - 110 065, India
Phone: +91 11 4250 5100
+91 11 2693 0117/121
Fax: +91 11 2684 2212
Pune
1187/17, Ghole Road
Shivaji Nagar
Pune - 411 005, India
Phone: +91 20 2553 9064/67
Fax: +91 20 4018 1930