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How Costly is the Deliberate Disinflation in India? Estimating the Sacrifice Ratio

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Abstract

Methods followed in earlier studies for estimating the sacrifice ratio or the real cost of deliberate disinflation have focused only on aggregate supply side ignoring aggregate demand. The present study considers the adjustment path obtained as a locus of short run equilibria to arrive at a theoretically acceptable sacrifice ratio. The study uses quarterly data from the period between 1996-97Q1–2013-14Q4 in India and employs both the regression as well as the direct methods to estimate the ratio. The results have revealed a sacrifice ratio ranging from 1.7 to 3.8 depending on the method and the measure of inflation used. Such a magnitude of the real cost of disinflation in India, also relevant in the medium to long run, clearly contradicts the dominant view among policymakers that the trade-off, if any, is negligible. Deliberate disinflation policy needs to consider the real cost of sacrificing output and employment particularly when its magnitude is substantial.

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Notes

  1. Use of alternate techniques for filtering like Baxter and King, Beveridge Nelson decomposition etc. has yielded similar results.

  2. The sacrifice ratio is defined in terms of cumulative loss of output and not employment. Since the direct method takes real GDP, it does not depend on the assumption of constant labor productivity that many economists object to.

  3. The lagged dependent variable used in the equation here is not the “statistical lag” followed in standard models. It is generated on a year on year basis which is theoretically more acceptable. The usual statistical lag would give perverse results due to seasonality involved in quarterly estimates. Hence, Cochrane-Orcutt procedures would be acceptable in our case.

  4. In essence, 5 percentage points decline in M3 growth rate converts roughly into 25 % reduction in its own percentage terms.

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Acknowledgments

Authors gratefully acknowledge useful comments by an anonymous referee of this journal. Thanks with usual disclaimers are also due to Prof. Abhiman Das for helpful discussions.

Author information

Authors and Affiliations

  1. IIM, Vastrapur, Ahmedabad, 380 015, India

    Ravindra H. Dholakia & Kadiyala Sri Virinchi

  2. University of Hyderabad, Hyderabad, India

    Kadiyala Sri Virinchi

Authors
  1. Ravindra H. Dholakia
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  2. Kadiyala Sri Virinchi
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Corresponding author

Correspondence to Ravindra H. Dholakia.

Appendix: Regression Diagnostics

Appendix: Regression Diagnostics

 Stationarity tests

S. No.

Unit root tests

Phillips–Perron unit root tests

 

KPSS LM stat

 

Variable

t values

P values

 

1

OG

\(-\)11.747

0.00

0.047863

2

M3growthrate

\(-\)7.865

0.00

0.193832

3

CubM3growthrate

\(-\)8.222

0.00

0.14

4

Growthgap2

\(-\)25.068

0.00

0.029082

5

SqGG2

\(-\)11.861

0.00

0.711341

6

LLOG

\(-\)8.3

0.00

0.189896

7

UsInflation

\(-\)3.3

0.01

0.147967

8

SQUSI

\(-\)3.67

0.005

0.126783

9

REERgrowthrate

3.535

0.007

0.138682

10

GDP Deflator based inflation

\(-\)2.853

0.05

0.343427

11

Lagged Inflation

\(-\)2.765

0.06

0.342339

12

Diff of lagged inflation

\(-\)5.847

0

0.073673

13

CPI based inflation

\(-\)2.596

0.094

0.271037

14

Lagged Inflation

\(-\)2.55

0.107

0.272884

15

Diff of lagged inflation

\(-\)6.842

0

0.042016

16

WPI based inflation

\(-\)3.433

0.009

0.439212

17

Lagged Inflation

\(-\)3.421

0.01

0.401258

18

Diff of lagged inflation

\(-\)5.02

0

0.057062

 

Critical values at 1, 5 and 10 % are respectively \(-\)3.559, \(-\)2.918, \(-\)2.594

  

Critical values are 0.73900,0.463000,0.347000 at 1, 5 and 10 % respectively

  1. Phillips–Perron Unit root tests reveal that most of the variables involved in the equation are Stationary at conventional 5 % significance Level. The variables GDP deflator based Lagged Inflation, CPI Based Inflation and CPI based Lagged Inflation turn out be stationary at 10 % levels. But these same variables turn out be very significantly stationary when the KPSS test was employed. Hence, no transformations were done

 Breusch--Godfrey LM test for autocorrelation

Equation

Lags(p)

Chi2

Df

\(\hbox {Prob} >\hbox {Chi}2\)

Equation 4

1

1.063

1

0.3026

Equation 3 based on GDP Deflator

1

1.629

1

0.2019

Equation 3 based on CPI

1

0.103

1

0.7481

Equation 3 based on WPI

1

0.834

1

0.3610

 Variance inflation factor

Variable

VIF

1/VIF

Equation 3 based on GDP Deflator

  

   LaggedInflation

5.13

0.194849

   Diffof LaggedInfl

5.06

0.197584

   M3growthrate

1.09

0.917000

   Mean

3.76

 

Equation 3 based on CPI

  

   LaggedInflation

3.93

0.254507

   Diffof LaggedInfl

3.85

0.259855

   M3growthrate

1.06

0.944507

   Mean

2.95

 

Equation 3 based on WPI

  

   LaggedInflation

4.85

0.206141

   Diffof LaggedInfl

4.75

0.210391

   M3growthrate

1.11

0.898525

   Mean

3.57

 

Equation 4

  

   USInflation

4.06

0.246204

   M3growthrate

3.94

0.253488

   LLOG

3.19

0.313694

   Growthgap2

1.53

0.65149

   REERGrowthrate

1.01

0.98641

   Mean

2.75

 

Equation 5a

  

   M3growthrate2

14.22

0.070343

   CUBM3

7.55

0.132424

   LLOG

3.85

0.26002

   Growthgap2

3.3

0.302992

   SQGG2

2.97

0.336182

   SQUSI

2.73

0.36643

   REERGrowthrate

1.02

0.981556

   Mean VIF

5.09

 

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Dholakia, R.H., Virinchi, K.S. How Costly is the Deliberate Disinflation in India? Estimating the Sacrifice Ratio. J. Quant. Econ. 15, 27–44 (2017). https://doi.org/10.1007/s40953-016-0039-2

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