RELATIONSHIP BETWEEN BUDGET DEFICIT AND ... BETWEEN BUDGET DEFICIT AND INFLATION – AN ECONOMETRIC...

International Journal of Economic Issues, Vol. 4, No. 2 (July-December, 2011) : 293-308© International Science Press

RELATIONSHIP BETWEEN BUDGET DEFICIT ANDINFLATION – AN ECONOMETRIC STUDY OF

INDONESIA

KANCHAN DATTAReader, Department of Economics, University of North Bengal,

P.O. Raja Rammohanpur, Dist: Darjeeling, India.

CHANDAN KUMAR MUKHOPADHYAYProfessor, Department of Economics, University of North Bengal.

The relationship between Budget Deficits and Inflation is one of the important andcontroversial issues in the academic literature as well as in economic policy perspective.Using the annual data of Indonesia from 1971 to 1999, this study found an existence ofa stable long-run relationship between inflation and Budget Deficit. This study alsoshows that there exists a unidirectional causality from Inflation to BD over the periodof the study in Indonesia. Hence economic policy supports a contractionary fiscal policyto control budget deficit in Indonesia.JEL Classification: E31, E63, E41Keywords: Budget Deficits, Inflation.

SECTION- I

1. INTRODUCTION

Inflation is a situation of rising prices. According to Keynesian Theory in an underemployed economy, with rigid prices and wages, an increase in the budget deficitraises disposable income and thereby stimulating aggregate demand. An increase inaggregate demand stimulates inflation.

According to Ricardian Equivalence Proposition, an increase in bond-financeddeficits induced by tax cuts results in a corresponding increase in the present value offuture taxes. Since people are forward looking hence the increase in governmentspending is matched by an equivalent increase in private saving. Therefore, aggregatedemand and so inflation remains unaffected.

According to Feldstein (1986) a Monetarist, a sustained increase in the budgetdeficit leads to anticipated inflation and an increase in inflation uncertainty.According to him “inflation is always and everywhere a monetary phenomenon”.Sargent and Wallace (1981) argue that the fiscal authority can not finance persistent

294 / INTERNATIONAL JOURNAL OF ECONOMIC ISSUES

budget deficits continuously through bond selling, since, the demand forgovernments is limited by the size of the economy. Hence the government at onepoint of time has to depend on seignorage for the payment of the principal andinterest due on outstanding bonds. In other words, persistent deficits wouldultimately have to be money financed which leads to inflation; since inflation isgenerally associated with monetary expansion.

On the other hand continuous inflation also increases government’s expenditurecompared to its enhanced revenue. Hence inflation can also stimulate government’sbudget deficit. Therefore there are three possibilities:

(i) budget deficit can stimulates inflation(ii) inflation can also affects budget deficit(iii) no relation between these two variables.

2. THEORETICAL FOUNDATION:

From national income identity we get,Y = C + I + G + NX (1)

whereY = National IncomeC = ConsumptionI = InvestmentG = Government expenditureNX = Net export

againY = C + S + T (2)

whereS = Private savingT = TaxesEquating (1) and (2)

C + I + G + NX = C + S + T

Or, (T-G) = (I-S) +NX (3)Or budget gap = domestic investment-saving gap + net export.Now an expansionary fiscal policy increases G or reduces T which rises budget

deficit implies a fall of (T-G) in equation (3). To maintain the identity either the firstterm in the right hand side that is (I-S) or the second term NX has to fall or both.

Case 1: Considering (I-S) unchanged only NX falls.We know NX is inversely related to real exchange rate. So with the fall in NX real

exchange rate increases. Since real interest rate is equal to nominal exchange ratemultiplied by home price index divided by foreign price. Assuming nominal exchangerate and foreign price index unchanged increase in real exchange rate implies increasein home price index or inflation. Thus in an open economy keeping nominal exchange

RELATIONSHIP BETWEEN BUDGET DEFICIT AND INFLATION – AN ECONOMETRIC STUDY OF…/ 295

rate and foreign inflation unchanged, an increase in budget deficit stimulates domesticinflation.

Case 2: NX unchanged only (I-S) falls.(I-S) falls implies either I falls or S increases. I (investment) falls when rate of interest

increases and rate of interest increases when the deficit is financed through marketborrowings. Hence rise in interest rate in the home market rises inflow of foreigncapital. As a result, exchange rate declines it implies an appreciation of domesticcurrency, which ultimately reduces net export. On the other hand if S (private savings)increase keeping I unchanged it ultimately leads to Ricardian Equivalence Hypothesis.

3. SURVEY OF LITERATURE

Empirical work on the relationship between deficits and inflation has yielded conflictingresults. Some studies provide results to support the idea that inflation is caused bydeficits, though in many studies there is no significant evidence.

On the other hand Aghevli and Khan (1978), Ahking and Miller (1985), Barnhartand Darrrat (1988), Hondroyiannis and Papapetrou (1997) find a bi-direction causationbetween deficits and Inflation. The different findings are shown in the Table below.

Table 1

Researcher Country Findings

Asif Idrees Agha and Pakistan(1973-2003) Fiscal Sector is dominant in explaining priceMuhammad Saleem Khan (2006) movementCatao and Terrones (2003) Developing and Strong Positive relationship between Fiscal

Advanced Countries deficits and Inflation among developingcountries but not amongadvanced economies.

Vieira (2000) Six major European There exists a long-run relationship betweenCountries inflation and deficits and it was inflation that

contributed to deficits rather than the reverse.Kivilcim Metin (1998) Turkey Budget Deficit significantly affects the inflationChoudhury and Ahmad (1995) Pakistan A positive relationship between Budget deficit

and InflationSowa (1994) Ghana Inflation is influenced more by output volatility

than by monetary factors both in the long-runand short-run.

Dogas (1992) Greece Public deficit affects InflationChoudhury & Parai (1991) Peru BD significantly affects Inflation

4. OBJECTIVE OF THE STUDY

In this paper an attempt has been taken to explore the relationship between Inflationand Budget deficit in the economy of Indonesia over the time period 1971 to 1999.

5. DATA

Data are taken from IFS , (yearly), inflation is considered as the 1st difference data oflog CPI and BD is taken as real BD as a percentage of real GDP. The variables aredeflated by GDP deflator with base 1995=100.


6. METHODOLOGY

The simplest way to examine the cause and effect relationship between two variablesis the simple bivariate regression model. But the problem is this technique is static itcan not capture the dynamism between the variables. The modern technique to capturethe dynamic causality is developed by Granger that is known as Granger CausalityTest in economic literature. It has two versions. One is short-run version that is capturedby Vector Error Correction Model and another one is long-run version that is depictedby Vector Auto-regression Model. But the primary task to perform these tests is to testthe stationarity of the time series variables and their cointegrating that is significantlong-run relationship. Unit root tests provide information about stationarity of the data.Non-stationarity data would contain unit roots. Results derived from the regressionmodels would produce ‘Spurious’ results if we use the data without checking theirstationarity properties.

In order to test for the existence of unit roots, and to determine the degree ofdifferencing we have applied ADF that is Augmented Dickey-Fuller Test (1979) andPhillips-Perron (PP) Unit Root Test (1988). For ADF test using OLS we run theunrestricted regression

( )p

t t t t j t jj

Y Y Y Y− − −=

− = α + β + ρ − + λ ∆∑1 11

1 (4)

And then, the restricted regression,p

t t j t jj

Y Y Y− −=

− = α + λ ∆∑11

(5)

where, ∆Yt = Yt – Yt-1, then a standard F ratio is calculated to test whether the restrictions(β = 0, ρ = 1) hold. Then we compare this computed F value to the table value given byDickey-Fuller. If the computed value is greater than the table value at certain level ofsignificance we reject the null hypothesis of non-stationarity.

Phillips and Perron (1988) have generalized the Dickey–Fuller tests to the situationswhere disturbance process εt are serially correlated. The PP test is intended to add a‘Correction Factor’ to the DF test statistic. The PP test is based on the estimation of thefollowing form of equation.

( ) ( )t t tt i

TY Y t Y− −∆ = Φ + ρ − + γ − + ϕ∆ + ξ11

2 (6)

Our next task is to test whether the series are cointegrated or not. Since Cointegratedvariables will not drift apart from each other and hence posses a long-run equilibriumrelationship. A non-stationary variable tends to rise overtime, but a pair of non-stationary variables may have the property that a particular linear combination wouldkeep them together, that is they do not drift too far apart. Hence they are said to becointegrated that is they posses a long-run equilibrium relationship. If both the variablesare stationary at level it implies they are automatically cointegrated. Hence we don’tneed to test for cointegration.


The presence of a cointegrating relation forms the basis of the Vector ErrorCorrection (VEC) specification. The Vector Error Correction Modeling providesimportant information on the short-run relationship between any two cointegratedvariables. The focus of the VEC Model analysis is the one period lagged error termsfrom the previously estimated cointegrating equations. This lagged terms provide anexplanation of the short-run deviations from the long-run equilibrium.

While testing the long-run dynamic relationship between model variablesconcerned, we may not make any a priory assumption of endogenity and exogenity ofvariables concerned. In such situation, Vector Auto-regression Model (VAR) can be used.This model treats all variables systematically without making reference to the issue ofdependence or independence. Moreover, a VAR model allows us to study the ‘ImpulseResponse Function’ and the ‘Variance Decomposition’ of the variables. Any shocks toany variables not only directly affects the respective variable only, but also it wouldbe transmitted to all of the endogenous variables in the model through dynamic (lag)structure of VAR. An Impulse Response Function tries to find out the effect of onetime shock to one of the innovations on current and future values of the endogenousvariables. Variance Decomposition is applied in the models to find out the informationabout relative importance of every random innovation in the question of its effects tothe variables concerned in the VAR system. We have tried to apply all these techniquesin this study.

SECTION-II

7. EMPIRICAL FINDINGS

Figure 1 shows the time plot of BD and Inflation. From the Figure it seems to us thatthe direction changes are more or less same between the two variables that is wheninflation (INF) increases , BD also increase and when BD declines (next period) INFalso declines but the magnitude of these changes differ. Only after 1995 onwards whenthe financial crisis in South-East Asia started, this similarly in the co -movement of BDand INF vanishes.

Figure 1: Time Plot of BD and Inflation (INF)


On the other hand if we ignore the small changes and consider the time plot oftwo variables as smooth line it seems to us the relation is opposite. That is there is adownward tendency of INF line but BD has an upward bias. Therefore thisdiagrammatic presentation gives us a rough idea about the co-movement of thesevariables. To get perfection, accuracy of dynamism we have to depend on sophisticatedtechnique.

Table 1Correlogram of BD and INF

The correlogram study is depicted by Table 1 to 2. Hence Table 1 shows there is nosignificant spike, where Table 2 (correlogram of BD) shows that there is one significantspike and there is no dying out pattern of spikes. Hence Table 1 and 2 shows both thevariables are stationary at level. For further confirmation we go for ADF and PP – unitroot test.

Table 3ADF and PP Unit Test Result on Inflation

Variable Exogenous ADF statistic Probability* Decision

Inflation Constant -3.707460 0.0098 I(0)Con.+linear trend -3.724586 0.0378 I(0)

Variable Exogenous PP-statistic Probability* Decision


*MacKinnon (1996) one-sided p-values.

Table 4ADF and PP Unit Test Result on BD

Variable Exogenous ADF statistic Probability* DecisionInflation Constant -2.780711 0.0743 I(0)

Con.+linear trend -4.311558 0.0153 I(0)Variable Exogenous PP-statistic Probability* Decision


*MacKinnon (1996) one-sided p-values.

Table 3 to 4 confirms both the variables are stationary according to both the ADFand PP test statistic at level in exogenous intercept, and constant + linear trend. There


fore both the variables (inflation and BD) are I(0). Since both are stationary at level,hence they are automatically cointegrated. That is there is a long-run equilibriumrelationship between BD and inflation in the economy of Indonesia.

Table 5Results of the VEC Model Estimation

D.Variable ECM DINF(-1) DBD(-1) R-squared F-statisticDINF -0.599430 -0.040846 -0.197774 0.313537 3.349454S.E 0.31710 0.28830 0.51399t-value -1.89034 -0.14168 -0.38478DBD -0.304701 0.304436 -0.143571 0.362905 4.177S.E 0.11256 0.10233 0.18244t-value -2.70709* 2.97499* 0.78694* implies level of significance at 5% level.

For the stability of the long-run relationship we estimate Vector Error CorrectionModel with lag one. The lag selection is based on minimum AIC. The results of theVEC model estimation is shown in Table 5. According to this finding when DBD isdependent variable the coefficient of error correction term is statistically significant.That is the deviation is significant. The estimated coefficient is -0.304701. Since it isless than one, hence it implies as time passes on the deviation restores to the long-runequilibrium path. Hence it implies the long-run equilibrium relation is stable. Moreover,the coefficient of DINF (-1) is positive and statistically significant. Inflation positivelyaffects Budget Deficit in the short-run. This implies that in the short-run there existsunidirectional causality from Inflation to Budget Deficit.

The diagnostic checking of the VEC model estimation is shown by the auto-correlation function of the error series (Figure 2), Jarque-Bera Normality test (Table 6)of the error series. From Figure 2 it is clear that there is no auto correlation among theerror series of the estimated VEC model. This implies the estimated coefficients areefficient. Though the significant value of the JB statistic implies the null hypothesisregarding the normality of the error series is rejected.

Figure 2: Correlogram of the Error Series of VEC Residuals


Table 6VEC Residual Normality Test

Component Skewness Chi-sq df Prob.

1 2.227737 21.50552 1 0.00002 -0.153569 0.102195 1 0.7492Joint 21.60772 2 0.0000

Component Kurtosis Chi-sq df Prob.

1 8.873456 37.37227 1 0.00002 2.527846 0.241506 1 0.6231Joint 37.61378 2 0.0000

Component Jarque-Bera df Prob.

1 58.87779 2 0.00002 0.343702 2 0.8421Joint 59.22149 4 0.0000

8. DIAGNOSTIC CHECKING OF THE VEC MODEL ESTIMATION

To measure the long-run causality we estimate the Vector Auto regression model. Thelag selection is based on AIC. Here lag 2 is selected. Table 7 shows the results ofestimation of VAR model.

Table 7Vector Autoregression Estimates

D.Variable INF(-1) INF(-2) BD(-1) BD(-2) Constant

Inflation 0.329183 -0.100033 -0.619937 0.578293 3.991338S.E 0.20413 0.28685 0.55638 0.53657 1.64578t-value 1.61265 -0.34873 -1.11424 1.07775 2.42519R-squared 0.192399BD 0.009071 -0.261377 0.370861 0.027265 0.457978S.E 0.07382 0.10374 0.20122 0.19406 0.59521t-value 0.12287 -2.51949* 1.84307 0.14050 0.76943R-squared 0.457950

D. Variable = Dependent Variable,* Implies level of significance at 5% level.

When inflation is dependent variable no estimated parameter is statisticallysignificant. It implies BD does not Granger Cause Inflation. On the other hand, whenBD is dependent variable, two period’s back inflation negatively affects current BD. Thismeans to control inflation government of Indonesia takes some contractionary fiscalpolicy which raises taxes or declines government expenditure which ultimately reducesbudget deficit. These findings also support the results of traditional Granger Causalitytests which uses F-statistic.

Table 8Results of Traditional Granger Causality

Pairwise Granger Causality TestsLags: 2

Null Hypothesis Obs F-Statistic Probability

BD does not Granger Cause INF 26 0.81140 0.45768INF does not Granger Cause BD 3.24304 0.05924


Here at lag 2 the null hypothesis of INF does not Granger Cause BD is rejected at5% level of significance.

9. DIAGNOSTIC CHECKING OF THE VAR MODEL ESTIMATION

The correlogram of VAR residuals implies there is no autocorrelation in the error series.Hence it implies the efficiency of the VAR estimation. On the other hand the significantvalue of JB statistic (Table 9) implies non normality of the VAR residual series.

Figure 3: Correlogram of the VAR residuals

Table 9VAR Residual Normality Tests

Orthogonalization: Cholesky (Lutkepohl)H0: residuals are multivariate normalComponent Skewness Chi-sq df Prob.1 2.007410 17.46201 1 0.00002 0.025521 0.002822 1 0.9576Joint 17.46483 2 0.0002Component Kurtosis Chi-sq df Prob.

1 7.360202 20.59564 1 0.00002 2.236628 0.631298 1 0.4269Joint 21.22694 2 0.0000Component Jarque-Bera df Prob.

1 38.05765 2 0.00002 0.634120 2 0.7283Joint 38.69177 4 0.0000


Figure 4 shows the time plot of the error series of the estimated VAR model. After1995 the inflation error starts to increase and in 1997 it reaches at its highest level. Maybe this is the outcome of the South Asian Financial Crisis. This huge error may be oneof the important reasons for the non normality of the error series.

Figure 4: Time Plot of VAR Residuals

SECTION-III

10. IMPULSE RESPONSE FUNCTION AND VARIANCE DECOMPOSITION:

As we know a VAR model allows us to study the ‘Impulse Response Function’ and the‘Variance Decomposition’ of the variables. Any shocks to any variables not only directlyaffects the respective variable only, but also it would be transmitted to all of theendogenous variables in the model through dynamic (lag) structure of VAR.

Although the Granger Causality tests which can be established through VEC(Short-run) and VAR model (long-run) to show the presence of a causal relationshipbetween the variables involved, they do not sufficiently answer the question on whatis the extent of causality and as such, is it destabilizing in nature? VarianceDecomposition helps to determine the proportion of the total variance in the volatilityof one variable explained by innovations in the volatility of the other variables. Figure5 shows the graphical presentation of Impulse Response Function.

Figure 5 shows that the response of one S.D. impulse of inflation to Inflation in thefirst two periods declines. After that in the next periods the response is not significant.The response of inflation to BD is also short lived. First period inflation declines thenit increases and coincides with the base line. The third one shows the response of BDto one S.D impulse of Inflation. This response is not short-lived. It continues uptoseven years, and then it coincides with the long-run base line. Finally the response ofBD to BD, it falls continuously up to five periods and then meets with the base line.Therefore from Impulse Response Function it is clear that out of sample period anyshocks in Inflation will have far reaching response to BD. This result supports ourfindings that inflation granger causes BD both in the short-run and in the long-run.


The results of Variance Decomposition is shown in Table 10.

Table 10Variance Decomposition

Variance of Inflation explained byPeriod S.E Inflation shocks BD shocks1 3.864439 100.0000 0.0000002 4.247561 96.30661 3.6933903 4.252193 96.11495 3.8850464 4.281432 95.68770 4.3123005 4.281515 95.68731 4.3126886 4.293775 95.65111 4.3488927 4.297025 95.60149 4.3985098 4.297896 95.60292 4.3970769 4.298998 95.60508 4.39492410 4.299116 95.60514 4.394857Variance of BD explained byPeriod S.E Inflation shocks BD shocks1 1.397612 11.23624 88.763762 1.486951 10.79657 89.203433 1.838148 40.32650 59.673504 2.030252 48.81129 51.188715 2.054931 49.90973 50.090276 2.069500 50.57074 49.429267 2.072331 50.70015 49.299858 2.072859 50.69899 49.301019 2.073680 50.70185 49.2981510 2.074111 50.71516 49.28484

Figure 5: Impulse Response Function


From the above Table it is clear that more than 50% variation of BD is explained byinflation shocks in coming ten years on the other hand only 4% variance of Inflation isexplained by BD shocks. This also supports our findings that inflation takes the pivotalrole in fiscal management.

11. SUMMARY AND CONCLUSION

The relationship between Budget Deficit and Inflation is one of the important andcontroversial issues in the academic literature as well as in economic policy perspective.This study shows that in the short-run inflation positively affects budget deficit whereas in the long-run just opposite relation exist. That is two previous years inflationnegatively affects current BD. May be this is the outcome of contractionary fiscal policyof the government of Indonesia.

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APPENDIX

Vector Error Correction Model:

t t t t tDINF Z DINF DBD− − −= α + ρ + β + β + µ1 1 1 2 1 1 (A1)

t t t t tDBD Z DBD DINF− − −= α + ρ + γ + γ + µ1 1 1 1 1 2 1 2 (A2)

Vector Auto Regression Model:

VAR Lag Order Selection Criteria

Endogenous variables: INF BD

Lag LogL LR FPE AIC SC HQ

0 -114.1859 NA* 37.30782 9.294872 9.392382* 9.321917

1 -109.4963 8.253763 35.38438 9.239701 9.532231 9.320836

2 -104.3969 8.158998 32.68534* 9.151751* 9.639301 9.286976*

3 -101.5243 4.136488 36.48568 9.241946 9.924516 9.431262

* indicates lag order selected by the criterion.LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction errorAIC: Akaike information criterion, SC: Schwarz information criterionSC: Schwarz information criterion, HQ: Hannan-Quinn information criterion.

The Estimable Model:

t t t t t tINF INF INF BD BD− − − −= α + β + β + γ + γ + µ1 1 2 2 1 1 2 2 1 (A3)

t t t t t tBD BD BD INF INF− − − −= α + γ + γ + β + β + µ1 1 1 2 2 1 1 2 2 2 (A4)

VAR Stability Condition checks: Roots of Characteristic Polynomial:

Table 1VAR Stability Check Condition

Roots of Characteristic PolynomialEndogenous variables: INF BDLag specification: 1 2

Root Modulus

0.592725 - 0.286732i 0.658436

0.592725 + 0.286732i 0.658436

-0.242703 - 0.532403i 0.585113

-0.242703 + 0.532403i 0.585113

(i) the Modulus of all the roots are less than one.


(i) No root lies outside the unit circle.(ii) VAR satisfies the stability condition.

Figure 1

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