1. On the measure of sacrifice ratio for India
BISHU GIRI
A project report submitted in partial
fulfillment of the requirement for the award of the degree of
MASTER OF SCIENCE
IN
APPLIED QUANTITATIVE FINANCE
MADRAS SCHOOL OF ECONOMICS
and
CENTRAL UNIVERSITY OF TAMIL NADU
JUNE 2015

2. DEGREE AND BRANCH : MASTER OF SCIENCE
(APPLIED QUANTITATIVE FINANCE)
MONTH AND YEAR OF SUBMISSION : JUNE 2015
NAME OF STUDENT : BISHU GIRI
ROLL NUMBER : P130006
NAME OF SUPERVISOR : DR. NAVEEN SRINIVASAN
DESIGNATION OF SUPERVISOR : PROFESSOR
MADRAS SCHOOL OF ECONOMICS
CHENNAI-600025

3. ii

4. iii
On measure of Sacrifice Ratio for India
Bishu Giri
ABSTRACT
This study makes an attempt to measure the sacrifice ratio and examines its variation
over the period of time. Using the Phillips curve framework, that is the relationship
between inflation and output, the sacrifice ratios are calculated for various periods for
India. We use quarterly data on Gross Domestic Product (GDP) and the Wholesale Price
Index (WPI) for the period from 1980 to 2008. The average value of sacrifice ratio is
found 0.36, thereby indicating that the cost of output in terms of inflation is not very
high. Further, the empirical results indicate that the sacrifice ratio varies over different
periods of time.

5. iv
ACKNOWLEDGEMENT
I would like to thank my Supervisor, Dr. Naveen Srinivasan, for his unconditional
support and for sharing a great enthusiasm in working for this project, despite his
immense workload. His unchallengeable proficiency in the fields of Macroeconomics
and policy ensured an intense learning experience. I would like to thank my panel
member, Dr. Sartaaj Rasool , for not only always being there to help out in times of need
but also for giving me valuable inputs, which helped immensely while doing this project.
I am grateful to Dr. Brinda Biswanathan for her valuable inputs. I am indebted to all my
friends who have helped me in completing this project.
Bishu Giri

6. v
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................ vi
LIST OF FIGURES.......................................................................................................vii
INTRODUCTION............................................................ Error! Bookmark not defined.
CHAPTER-1………........................................................................................................................3
THEROETICAL FRAMEWORK…………………………………………………………………………………………3
CHAPTER-2 ......................................................................................................................7
REVIEW OF LITERATURE.......................................................................................11
CHAPTER-3 ....................................................................................................................11
METHODOLOGY .......................................................................................................11
CHAPTER-4 …………………………………………………………………………13
4.1 DATA AND VARIABLES……………………………………………… 13
4.2 EMPIRICAL RESULTS ................................... Error! Bookmark not defined.
CONCLUSION………………………………………………………………………....20
REFERENCES.................................................................................................................21

7. vi
LIST OF TABLES
Table 2.1 Estimates of sacrifice ratios in literature .......................................................................10
Table 4.1.1 Unit root tests ............................................................Error! Bookmark not defined.7
Table 4.1.2 Estimated sacrifice ratios using Wholesale Price Index ...........................................19

8. vii
LIST OF FIGURES
Figure 4.1.1 Inflation rate using wholesale price index over the period 1980-2008....................14
Figure 4.1.2 Actual output vs Trend output .................................................................................15
Figure 4.1.3 Inflation rate: fuel and petroleum……………………………………...16
Figure 4.2.1 Estimated sacrifice ratios over the period 1980-2008……………………19

9. viii

10. 1
INTRODUCTION
In literature number of studies focuses on measuring sacrifice ratio; the amount of output
loss arising due to the deliberate effort to reduce inflation permanently. The sacrifice
ratio is the cumulative loss in output, measured as a percent of one year’s gross domestic
product (GDP), resulting from one percentage-point permanent reduction in inflation.
Historically, many attempts have been made to estimate the sacrifice ratio, based on the
theoretical premise of Phillips Curve. By estimating Okun found out that output cost of
reducing inflation by one percentage point of between 6 & 18 percent of a year’s GNP,
with a mean of 10 percent. The main drawback of this study is that it doesn’t take into
account the possibility of changes in the ratio as the disinflation experiment proceeds.
However, Gordon and King (1982) found out the problem with Okun’s methodology
and hence proposed a structural vector auto regression model (SVAR) to measure
sacrifice ratio. One important advantage of this method is that it accounts for the impact
of supply and demand shocks. However, the main disadvantage of this models are the
large data requirements. Sacrifice ratio has also been calculated using slope of aggregate
supply curve (ball, 1994). The data requirements are less as compared to the VAR
model. It treats inflation and disinflation episodes symmetrically. However, the effect of
inflationary and disinflationary period may be different. So, the ball’s method does not
consider the issue.
Furthermore, very few paper has been published in this topic for the developing
countries. RBI has attempted to measure the sacrifice ratio for India. Using the slope of
aggregate supply curve they found the sacrifice ratio for India around 2%. Kapur and
Patra (2003) estimate an alternative specification of aggregate supply function and
provides estimates of a sacrifice ratio ranging from 0.3 to 4.7 percentage.

11. 2
Sacrifice ratio, depends upon the rate of change in inflation and output, and hence may
not be constant over the period of time. So far no attempt have been made to estimate
time varying sacrifice ratio. Therefore, this paper attempts to construct measures of time
varying sacrifice ratio for India for the period 1980q1 to 2008q4. The main advantage of
this methodology is that it will capture the changing relationship between strict
monetary policy and it’s effect on inflation. It will provide the changing relationship
between inflation and output which might help in monetary policy formulation. The rest
of the paper is organized as follows: chapter 1 provides with the theoretical framework
of the study, chapter 2 analyses the various research on estimation of sacrifice ratio,
section 3 describes the methodology used, chapter 4 discuss the data and empirical
results and finally conclusion.

12. 3
CHAPTER 1
THEORETICAL FRAMEWORK:
Sacrifice ratio, the output cost of lowering inflation has gained it’s momentum since
1958 when a New Zealand born economist William Phillips published his paper titled
The Relation between Unemployment and the Rate of Change of Money Wage Rates in
the United Kingdom, 1861-1957. In this paper Phillips discovered an inverse relationship
between rates of unemployment and corresponding rates of inflation that result in an
economy. Though it is considered to be original but the relationship between the
inflation & unemployment can be traced back to the 17th century. David Hume (1711-
1776), on his analysis talked about the relationship as
…“though the high price of commodities be a necessary consequence of the increase of
gold and silver, yet if follows not immediately upon that increase; but some time is
required before the money circulates through the whole state and makes its effect be felt
on all ranks of people. At first, no alteration is perceived; by degrees the price rises, first
of one commodity, then of another; till the whole at of one commodity, then of another;
till the whole at last reaches a just proportion with the new quantity of specie….In my
opinion, it is only in this interval or intermediate situation, between the acquisition of
money and rise of prices, that the increasing quantity of gold silver is favourable to
industry…”
From the above statement by David Hume we can point out some relevant links. First,
the trade-off is between unemployment & unperceived changes in money & prices; it
vanishes once perceptions fully adjust to reality. Secondly, price perception though slow
to adjust, eventually catch up to one-time changes in the level of money & prices. It
implies that such changes are temporary. Third, the only way the trade-off can be

13. 4
sustained is to generate a continual succession of changes in money & prices.
(Humphrey)
In his 1926 International Labour Review article, “A Statistical Relationship between
Unemployment & Price Changes,” he discovered the correlation between
unemployment & lagged price changes. Fisher used monthly US data for the period
1915-1925, he obtained correlation coefficients as high as 90 percent between the two
variables. From this results he concluded that there was a strong causal relationship from
price to unemployment. Causalities from price to unemployment arise from the fixed
contracts, the inertia of custom & other inhibiting factors that prevent costs from
adjusting as fast as prices when prices change. Keynes in his early writings also warned
about the inverse relationship. In his word
“Each inflation & deflation alike, has inflicted great injuries. Each has also an effect in
overstimulation or retarding the production of wealth, though here deflation is more
injurious.”
“The fact of falling prices injures entrepreneurs; consequently the fear of falling prices
causes them to protect themselves by curtailing their operations’ (Keynes 1923)
After the great depression of 1929 it was discovered that the effect of inflation &
deflation are not equal. It was found that deflation has larger effect on the economy. The
effect of deflation has been strengthen by the ineffectiveness of monetary policy due to
zero bound on the nominal interest rate.
It was Phillips formulation and not those of his predecessors that captured the attention
of the economist in inflation-output trade-off. In 1960, Paul Samuelson & Robot Solow
also examined the explicit link between inflation & unemployment. They concluded
their paper with the inverse relationship between inflation & unemployment.

14. 5
Many industrial countries believed that the results are valid that is there is permanent
stable relationship between inflation & unemployment. One implication of this for the
government policy was that governments could control unemployment & inflation with
the Keynesian policy.
πt= (µ+z)-αut
1
In 1970s, many countries experienced both the higher level of inflation & higher level of
unemployment, as opposite to the traditional belief of inverse relationship. Philips curve
comes under the attack by many economist. Milton Friedman, in his paper, “The Role of
Monetary Policy” argued that the Philips curve is a short run phenomenon. In his
language
“there is always a temporary trade-off between inflation & unemployment; there is no
permanent trade-off. The temporary trade-off comes not from inflation per se, but from
unanticipated inflation, which generally means, from a rising rate of inflation.”
According to him, the inverse relationship that Philips observed between inflation &
unemployment is due to failure to distinguish between nominal wages and real wages.
He talks about the “natural” rate of unemployment.
Natural rate of unemployment is the unemployment rate such that the actual price level
is equal to the expected price level. Equivalently, and more conveniently here, the
natural rate of unemployment is the unemployment rate such that the actual inflation rate
is equal to the expected inflation rate. After considering the expected inflation rate
(actual inflation rate of previous period), the way of thinking about the Phillips curve
changes. The change in inflation rate depends on the difference between the actual and
the natural unemployment rates. When the actual unemployment rate is higher than the
natural unemployment rate, the inflation rate decreases; when actual unemployment rate
is lower than the natural unemployment rate, the inflation rate increases. The natural rate
of unemployment is the rate of unemployment required to keep the inflation rate

15. 6
constant. This is why the natural rate is also called the nonaccelerating inflation rate of
unemployment (NAIRU). (Blanchard, 2000)
πt-πt-1= -α(ut-un)
After the emergence of rational expectation theory, the case for natural rate of
unemployment has become stronger. Robert Lucas & John Muth in their paper argues
that the workers, consumers and firms are rational. To assume rational expectations is to
assume that agent’s expectations may be wrong, but are correct on average over time.
For example, negotiations between workers and firms will be influenced by the expected
level of inflation.
Recent paper by George Akerlof, William Dickens, and George Perry (1996) has shown
that there is a moderate level of trade-off between level of inflation and unemployment.
They argued that the nominal wage rigidity eliminates monetary neutrality. No longer is
a change in nominal GDP growth neutral with respect to unemployment; as the inflation
rate approaches zero, a deceleration in nominal GDP growth creates a permanent
increase in unemployment, rather than the temporary increase that is usually fed into
conventional measures of the “sacrifice ratio.” According to them, the sacrifice ratio no
longer involves trade-off between the permanent benefits of a lower inflation rate and
the temporary cost of lower output. Instead, the cost of lower output and higher
unemployment is permanent and swamps the permanent benefits of a zero inflation rate.

16. 7
CHAPTER-2
LITERATURE REVIEW
Sacrifice Ratio has been one of the major subjects in macroeconomics. It is associated
with the two major macroeconomics variable, inflation & unemployment. Not only from
the economical perspectives but from the social perspectives also it has major impact on
the society. The shape of the Philips curve has been an important direction for the
conduct of monetary policy in modern economy. Paul Volcker, chairman of the Federal
Reserve under Presidents Jimmy Carter and Ronald Reagan was credited with ending
the high levels of inflation seen in Unites States during 1970s and early 1980s. This
period is viewed as the best period for testing the validity and information content of the
sacrifice ratio. (Kapur & Patra)
Before we start with the cost of fighting inflation we need to understand the cost &
benefit of achieving low inflation environment. The potential benefits of low inflation
include faster economic growth, higher productivity, a more stable economic
environment and fewer tax distortions. The cost of achieving and maintaining low
inflation include lost output, higher unemployment, and related social ills. (filardo,
1998)
Sacrifice ratio is also very important because it provides the total output cost of fighting
inflation in a single number. In this sense, the sacrifice ratio becomes relevant not
merely in assessing policy effectiveness in a one-time transition from high inflation to
low inflation but also in the context of the ongoing policy rule. (Kapur & Patra)
Okun started with the estimation of sacrifice ratio. He examined a family of Phillips
curve modes for the US. He found out that ‘the average estimate of the cost of a 1 per
cent reduction in the basic inflation rate is 10 per cent of a year’s GNP, with a range of 6
per cent to 18 per cent. Analysing the result, it 10 per cent seems to be quite high cost
for a per cent reduction. Okun assumed a linear Phillips curve which may not be the
case.

17. 8
Gordon and King (1982) modified the Okun’s methodology where they have used
traditional and vector auto regression model within the assumption of a linear Phillips
curve model. Estimated sacrifice ratio lies within 0 to 8 percent. They compute the cost
of a resolute disinflation policy and compare it with the cost of stabilizing inflation at its
present rate. Stanley (1993) examines the role of macroeconomic stability on growth. He
present international cross-sectional regression evidence that supports the view that
growth is negatively associated with inflation, and positively associated with good fiscal
performance & undistorted foreign exchange markets. He simply regress
macroeconomic factors on the growth equation. He also talks about nonlinear
relationship between inflation & growth. His findings imply that inflation reduces
growth by reducing investment, and by reducing the rate of productivity growth.
Laurence Ball (1994) estimated sacrifice ratio using a different approach. He identifies
the various disinflation episodes, then summing up the total output losses over the
episode divided by the change in trend inflation. He examined disinflations from 1960 to
the present in moderate-inflation countries of the Organization for Economic
Cooperation and Development (OECD). Trend inflation is measured as the moving
average of actual inflation. They found out that sacrifice ratio is decreasing in the speed
of disinflation i.e. gradualism makes disinflation more expensive. Also the ratio is lower
in countries with more flexible labour contracts.
Filardo (1998) estimated the sacrifice using non-linear Phillips curve. They found out
that the optimal monetary policy changes with the shape of the Phillips curve, but there
exists a unique equilibrium no matter whether the Phillips curve is linear or non-linear.
Cecchetti & Rich used structural VAR models to estimate the sacrifice ratio for US for
the period 1959:Q1-1997:Q4. They estimated sacrifice ration within the range of 0.1 to
10.
Andersen & Wascher (1999) estimated sacrifice ratios for 19 industrialised countries,
using three alternative approaches: 1) estimating aggregate supply curve; 2) estimating
structural price and wage equations; and 3) comparing actual changes in inflation with

18. 9
changes in standard measures of output and labour market slack. In their sample, as the
average inflation rate for the 19 countries fell from 8% to 31/2 %, the average sacrifice
ratio has increased form around 1.5 to about 2.5.
Comparatively there has been very few attempts made in estimation of sacrifice ratio.
The only known effort being the work reported in the Report on Currency and Finance
(RBI, 2002a). The have estimated sacrifice ratio using slope of aggregate supply curve.
Using linear Phillips curve they found out that sacrifice ratio equals to 2. Kapur & Patra
has also estimated sacrifice ratio for India. They have estimated a single sacrifice ratio
for the period 1970: 2001. With WPI as a measure of inflation they have estimated
sacrifice ratio which lies between 1.9 to 2.7 for the full sample period. It is fairly close to
that of 2.0 in RBI (2002). Table1 provide a tabular form of some of the important
literature on sacrifice ratio.

19. 10
Table 2.1: Estimates of Sacrifice Ratios using various methodology
Study Methodology Coverage and Study
period
Estimates of
Sacrifice Ratio (%)
1 2 3 4
Okun (1978) Aggregate supply
curve
USA 6-18
Ball (1993) Actual development
in output and inflation
19 industrial
Countries; 1960-92
Average 5.8 percent
for quarterly data
and 3.1 percent for
annual
Filardo (1998) Aggregate supply
Curve (non-linear)
In a VAR framework
USA 5.7 for a linear
specification; 5.0
for a weak economy
and 2.1 for an
overheated
Economy for a non
linear specification.
Anderson and
Wascher (1999)
Aggregate supply
curve; structural wage
and price equations;
Actual developments
in Output and
inflation
19 OECD
countries; 1965-98
Average of 1.5
(1965-85) and 2.5
(1985-98)
RBI (2002) Aggregate supply India; 1971-2001 2.0

20. 11
CHAPTER-3
METHODOLOGY
In the literature, three methodologies have generally been used to calculate the sacrifice
ratio. One approach, which is followed by ball (1994) focuses on historical episodes of
disinflation and calculate sacrifice ratio for each period. So, for all the different
disinflationary period we will have different sacrifice ratios. The problem with this
method is that it doesn’t consider the inflationary period. Thus, ignores the correlation or
transition from inflation to deflation. Also cost-push inflation are not neutralized.
Second approach estimate sacrifice ratio using VAR model. (Gordon & King, 1982); the
data requirement for this kind of model is very high. In their model Gordon & King
estimated the variable with only four degrees of freedom. Also the model are very
complex to estimate. Sacrifice ratios obtained from structural VARs are highly sensitive
to the size of the model and identification restrictions.
Third approach estimates sacrifice ratios form the slope of the aggregate supply curve,
i.e., the Phillips curve. This approach has various advantages as compared to other
methods. It treats inflation and disinflation episodes symmetrically; and can control
supply shocks. The data requirement are less intensive than a VAR-based models. RBI
(2002) has used this methodology. They have estimated a single estimate of a sacrifice
ratio for the whole sample period. They have assumed a linear Phillips curve which may
not be the case.
Most of the recent literature has casted doubt on the linearity of the Phillips curve. Size
of the sacrifice is changing over the period of time. It has been argued that the Phillips
curve may have three shapes i.e. convex, concave & convex-concave. So, the previous
methodology has major disadvantage of assuming a constant sacrifice ratio over the
period of time.
In this paper I am going to perform a rolling regression using a simple Phillips curve
equation. For rolling estimation first we choose a time interval (which is a subset of total

21. 12
sample period). Then we start the regression, by selecting observations starting from the
first observations. After estimating for the first sample period, then we move the
estimation 1 step ahead, i.e. estimating from the second observation in the sample. In
this way we can have rolling estimation. The main advantage of this method is that it
will give us numbers of sacrifice ratios. Using the results we can track the movement of
the sacrifice ratio. In other words we can check how much cost is involved for per cent
reduction in inflation.
Econometric Model
Since, the very first equation given by Samuelson & Solow, Phillips curve equation has
evolved over the period of time. For the estimation purpose I have used a simple Phillips
curve equation.
πt= απt-1+ βDt + γ zt
where πt is the current inflation rate (measured using WPI), Dt is the excess demand (or
output gap), Zt is supply shocks (cost-push inflation). L is the lag operator.

22. 13
CHAPTER-4
4.1 Data and Variables
I am estimating the sacrifice for India. The sample period for our data set is from 198q1
to 2008q4. Given, the requirement of the equation we have taken the data for each
variable. We have taken the quarterly time series data.
Variables:
Inflation Rate
It is the main variable in the equation. For our equation inflation rate is the dependent
variable. For inflation rate, I have taken the quarterly data from Reserve Bank of India
(RBI) database for Wholesale price index (WPI). To derive the inflation rate from the
index I have used the below formula
100*
1
1



t
tt
t
p
pp


23. 14
Figure 4.1.1: Inflation rate using Wholesale Price Index over the period 1980-2008
We can see that inflation rate for India has almost be stationary. With the highest
inflation rate of around 20 % in 1980 to the lower of 2.45 % in 1982, the average has
been around 7%. Though, the population of poor & middle income people is far greater
than rich, average inflation rate of 7% is considered to be healthy for an emerging
economies like India.
Excess Demand
It is one of the independent variable in our model. The coefficient of excess demand is
interpreted as the on average how much the inflation rate will change due to change in
excess aggregate demand given the previous year inflation rate and supply shocks.
I have taken the annual data for gross domestic product (GDP). To normalize the data I
have done the log transformation. Using the Hodrick-Prescott (HP) filter I have
generated the potential GDP data without the random component. By using below
formula I have calculated the excess gap
Excess gap = actual output – potential output
0.000
5.000
10.000
15.000
20.000
25.000
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
inflationrate
Year
Wholesale Price Index

24. 15
Figure 4.1.2: Actual output vs. Trend output
As we can observe the output gap (or excess demand) has been fluctuating around zero.
Greater variation has been observed in some years such as 1990, 2002, 2007 & 2010.
Supply shocks
Since sacrifice ratio measures the change in inflation rate due to change in aggregate
demand, we will try to neutralize the effect of other factor on change in inflation rate.
So, we will use supply shocks to show it’s effect on inflation rate separately. Various
variables has been use as supply shocks. In my model I have used fuel & petroleum
inflation as the supply shocks. Since the oil in one of the main component of CPI or WPI
& the price of oil is determined by the demand in domestic market it is one of the good
measure for supply shocks. Data for fuel & petroleum has taken from RBI.
0
200,000
400,000
600,000
800,000
1,000,000
55 60 65 70 75 80 85 90 95 00 05
GDP HPTRENDGDP

25. 16
Figure 4.1.3: Inflation rate: Fuel and Petroleum for the period 1980-2008
0
5
10
15
20
25
30
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
inflationrate
Years
Fuel and Petroleum
Series2

26. 17
4.2 EMPIRICAL RESULTS:
The first step in estimation is to check for the stationarity of the variables, which is the
necessary condition for any time series estimation. Table 1 presents the results of the
unit root test. We checked the stationarity using Augmented Dickey-Fuller test (ADF
test) & Phillips-Perron (PP) test. Both the test have null hypothesis of presence of unit
root. According the ADF test all our variables are stationary (at 5% level of significance)
except the log of gross domestic product. As per the PP test, log of GDP (LNGDP) &
output gap (YGAP) are not significant. Taking the results of ADF test we have all the
necessary variables stationary. Model is correctly specified using residual analysis.
Using this criteria one lag of inflation using WPI is included. No other lags are included
as they are not significant.
Table 4.2.1: Unit Root Tests
Variables Augmented Dickey-Fuller
Test
Phillips-Perron(PP)test
INFW -3.97***
(0.004)
-4.06***
(0.003)
INF(F&P) -5.14***
(0.0002)
-5.12***
(0.0002)
LNGDP 1.56
(0.99)
2.80
(1.00)
YGAP -3.74***
(0.0084)
-2.37
(0.15)
Note: Figures in square brackets indicate the p-value.*, ** and *** indicate significant
10%, 5% and 1% level.

27. 18
INFW = inflation rate using Wholesale Price Index
INF (F&P) = fuel and petroleum inflation
LNGDP= log of Gross Domestic Product
YGAP= output gap (actual output – trend output)
The estimates based on the econometric equation are set out in Table2. The estimated
equations have satisfactory fit and explain a substantial amount of variation in the
dependant variable. The residuals diagnostics shows that the residuals are random &
have normal disturbances.
Table 4.2.2: Estimates using WPI Inflation
Time period π (t-1)
ygap Sacrifice ratio
(1-α)/β
1980q1-2001q4 0.98*
(0.000)
0.055
(0.14)
0.36
1981q1-2002q4 0.96*
(0.00)
0.123*
(0.002)
0.32
1982q1-2003q4 0.99*
(0.00)
0.102*
(0.0114)
0.09
1983q1-2004q4 0.96*
(0.00)
0.074
(0.0814)
0.54
1984q1-2005q4 0.97*
(0.00)
0.087*
(0.0489)
0.34
1985q1-2006q4 0.96*
(0.00)
0.087
(0.0569)
0.45
1987q1-2007q4 0.96*
(0.00)
0.094*
(0.038)
0.42
Note: Figures in the bracket are p-values. * indicate significant 5% level.

28. 19
The coefficients of the lagged inflation terms are all significant at 5 % level. The
coefficients are in the range of 0.95-0.98 which implies that the previous period inflation
can substantially explain the large portion of variation in current inflation. Therefore, it
support the adaptive expectation hypothesis. Output gap is significant for some time
periods & insignificant for others. It suggest that the output gap can successfully explain
the variation in next period inflation rate. The sacrifice ratio lies between 0.3-0.6 except
for the period 1982q1 to 2003q4. This means for a unit change in inflation the output
will change by less than a percent. The supply shocks are not significant in explaining
the change in inflation rate. The results shows that there is minimal trade-off between
inflation and output for India. From the results, we can track the movement of sacrifice
ratio over the period of time.
Figure 4.2.1: Estimated sacrifice ratios for the period 1980-2008
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7
Sacrificeratio
Window
sacrificeratio
sacrifice ratio

29. 20
CONCLUSION
This study makes an attempt to measure the sacrifice ratio and examines its variation
over the period of time. Using the Phillips curve framework, that is the relationship
between inflation and output, the sacrifice ratios are calculated for various periods for
India. We use quarterly data on Gross Domestic Product (GDP) and the Wholesale Price
Index (WPI) for the period from 1980 to 2008. The average value of sacrifice ratio is
found 0.0000 thereby indicating that the cost of output in terms of inflation is not very
high. Further, the empirical results indicate that the sacrifice ratio varies over different
periods of time. The hypothesis that the sacrifice ratio is not constant is supported by the
results.
The measurement of sacrifice ratio depends upon the various factors. The evidence
suggest that the output gap has been insignificant for some time periods. So, the other
measure of output, such as Index of Industrial Production (IIP) may improve the
measurement of the sacrifice ratio. The close examination of the results shows the
inconsistency in the relationship over the period of time. So, the calculation of sacrifice
ratio with more accurate measures and robust technique is suggested.

30. 21
REFERENCES
 Andersen, Palle S. and William L Wascher (1999), Sacrifice Ratios and the
Conduct of Monetary Policy in Conditions of Low Inflation, BIS Working
Papers, 82.
 Ball, L.N. (1994), What Determines the Sacrifice Ratio? In N.Gregory Mankiw
(ed.) Monetary
Policy, University of Chicago Press.
 Barro, Robert (1995), Inflation and Economic Growth, Bank of England
Quarterly Review, May.
 Blanchard, Olivier (2000). Macroeconomics (Second ed.). Prentice
Hall. ISBN 0-13-013306-X.
 Cecchetti, Stephen G. and R.W. Rich (2001), Structural Estimates of the US
Sacrifice Ratio,
Journal of Business and Economics Statistics, Vol. 19, No. 4.
 Durai, S.Raja and Ramachandran M (2012), Pondicherry University, Sectoral
effects of disinflation: evidence from India, Macroeconomics and Finance in
Emerging Market Economics, Vol.6, No. 1, 77-87
 Filardo, Andrew J. (1998), New Evidence on the Output Cost of Fighting
Inflation, Federal Reserve Bank of Kansas City, Economic Review Third Quarter.

31. 22
 Fisher, Irving. “A Statistical Relation between Unemployment and Price
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