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Fiscal Deficit and Economic Growth Nexus in India: A Simultaneous Error Correction Approach

1. Introduction

The effects of fiscal deficit on economic growth remain a highly debated issue in macroeconomics. India has experienced a persistence of high fiscal deficit during the last four decades[footnoteRef:1]. The capital expenditure as a percentage of GDP and the developmental expenditure as a percentage of total expenditure have fallen in the last four decades. Unproductive expenditures like interest payments and subsidies as a percentage of GDP have increased. Thus, a significant proportion of the resources generated through fiscal deficit are used for seemingly unproductive expenditure. Therefore, it gives rise to several questions: Does the persistence of fiscal deficit hamper economic growth in India? Does it significantly affect economic growth in the long run and short run? Does any indirect linkage exist between fiscal deficit and economic growth in India? Does the composition of expenditure matter for economic growth in India? The empirical literature on the relationship between fiscal deficit and economic growth issue is addressed mostly in advanced and emerging countries. However, there is no study on India that deals with both direct and indirect effects of fiscal deficit on economic growth. With the recent rise in fiscal deficit in India, it is pertinent to study such relationship for India. Therefore, the broad objective of the paper is to empirically examine the effect of fiscal deficit on economic growth in India. Simultaneous Equation Models, which would address both the direct and indirect effects, are used to analyze the fiscal deficit-growth linkage in India. [1: The fiscal deficit of the central government has increased from 3.17 per cent of the gross domestic product (GDP) in the fiscal year 1970-71 to more than 5 per cent of GDP in the fiscal year 2013-14. Revenue deficit has increased from 4.52 per cent of fiscal deficit in 1981-82 to 70.59 per cent in 2013-14. It implies that the capital outlay, which was meant for productive capital formation, was reduced by the government. Total liabilities of the central government have risen from 45.25 per cent of GDP in 1980-81 to more than 55 per cent in 2013-14.]

The linkage between fiscal deficit and economic growth is complex and ambiguous (Adam and Bevan 2005). The undesirable effects of high fiscal deficit are linked to its pattern of financing, the outstanding debt stock, and the nature of expenditure. If the government finances the deficit through heavy borrowing and taxes, then it may distort incentives for productive investment, crowd out private investment etc., which consequently can hamper growth. Persistence of high fiscal deficit may adversely affect economic growth through increase in the burden of public debt and its repayment. The public debt has an impact on economic growth through a number of channels such as private saving, public investment, total factor productivity and long term nominal and real interest rates (Checherita and Rother 2010). An increase in public borrowing can also lead to crowding out of private investment, high interest payments, inflation, exchange rate fluctuations etc. (Burney et al. 1992, Arora and Dua 1993, Karras 1994, Darrat 2000).

However, if the resources generated through fiscal deficit are used in productive ways like creation of physical and human capital, infrastructure etc., then it will have a favorable effect on economic growth (Bose et al. 2007, Schelarck 2004, Kneller et al. 1999). However, Devarajan et al. (1996) found that an increase in the share of current expenditure has positive and statistically significant growth effects, whereas capital expenditure has negative effects on growth using annual data for 43 countries from 1970 to 1990.

Generally, there are three schools of thought concerning the economic effects of budget deficits on economic growth, i.e., Neoclassical, Keynesian and Ricardian. The Neoclassical view considers that fiscal deficits are detrimental to investment and growth (Fischer 1993, Easterly and Rebelo 1993, Cebula 1995, Fatima et al. 2011, Bleaney et al. 2001, Roy and Berg 2009). The Keynesian view stresses that fiscal deficit constitutes a key policy prescription and has beneficial effects on an economy (Eisner and Pieper 1984, Taylor et al. 2012). However, the Ricardian view considers that deficits have a neutral effect on the growth of an economy (Tan 2006, Daylop 2010, Nelson and Singh 1994).

Ghali (1997) examined the nature of the relationship between government expenditure and economic growth in Saudi Arabia for the period 1960-1996. Their empirical analysis found no consistent evidence that government spending can increase per capita output growth in Saudi Arabia. Gupta et al. (2005) assessed the effects of fiscal consolidation and expenditure composition on economic growth for a sample of 39 low income countries during 1990-2000. They found those budgetary surpluses are generally associated with higher economic growth in both the short run and the long run. Osinubi et al. (2006) examined the relationship between budget deficits, external debt and economic growth in Nigeria during 1970 to 2003. Their results confirmed the existence of the debt Laffer curve and nonlinear effects of external debt on growth in Nigeria. Keho (2010) examined the causal relationship between budget deficits and economic growth for seven West African countries over the period 1980-2005. He found mixed results of nonexistence of causality in three countries and adverse effects of deficits on economic growth in the remaining four countries. Avila (2011) analyzed the relationship between fiscal deficit, macroeconomic uncertainty and growth in Argentina for the period 1915-2006. He found that fiscal deficit hampered the growth of percapita income in Argentina through the volatility in relative prices.

This study contributes to the literature in the following four novel ways. First, it employs a combination of Autoregressive Distributed Lag (ARDL) and Simultaneous Error Correction approach, which is a methodological addition to the existing literature. Second, this could be one of the earliest studies to analyze both direct and indirect effects of fiscal deficit on economic growth in India. Third, it examines the differential impacts of fiscal deficit and revenue deficit on economic growth in India. Finally, it analyzes data for the time period from 1970-71 to 2013-14, thereby utilizing most recently available data that has not been used by existing studies.

The rest of the paper is organized in the following manner. Section 2 presents the analytical framework for specification of models used in the study. The data and the methodology are discussed in Section 3. Empirical results are presented and analyzed in Section 4. Conclusion of the paper is presented in Section 5.

2. Analytical Framework

This section presents the analytical framework for specification of models and the methodology used in the analysis.

2.1. Econometric Specification

Gross domestic product at factor cost (GDPFC) is used as a proxy for economic growth for this study. A continuous rise in fiscal deficit implies an accumulation of public debt and repayment of debt. An increase in debt service payments forces the government to cut down its spending on relevant like health, education, research and development, infrastructure etc. and so on. Thus, it reduces growth of both physical and human capital, which could impede economic growth in the long run. However, if the resources generated through fiscal deficit are used in productive ways, then it will have a favorable effect on economic growth. Since the variable of interest is fiscal deficit (FSDFG), the study has used it as a variable to estimate the growth equation (Fischer 1993, Easterly and Rebelo 1993, Easterly et al. 1994, Cebula 1995, Roy and Berg 2009).

It is widely believed that moderate and stable inflation rate accelerates economic growth by creating favorable business environment, enhancing investment, augmenting return to savers etc. However, persistence of high inflation rate may lead to uncertainty about future profitability of investment projects. It reduces a country’s international competitiveness by making its exports relatively more expensive. Hence, high inflation affects the economy adversely through its various negative externalities. It lingers at high level during the study period. Therefore, the study has used inflation rate (INFLA) as a regressor in the equation to measure its impact on economic growth (Faria and Carneiro 2001, Mallik and Chowdhory 2001, Barro 2013). It is widely accepted that higher capital formation and employment play a crucial role in achieving economic growth and prosperity. Gross domestic capital formation (GDCFG) & Employment in the organized sector (EMPLM) are used as a proxy for capital and employment respectively.

Therefore, it has used the following specification for the growth equation.

GDPFC= f (GDCFG, EMPLM, FSDFG, INFLA)………… (1)

2.2. Measuring Simultaneous Relationships

The study has employed a Simultaneous Equation Model (hereafter SEM) approach to explore the direct and indirect effects of fiscal deficit on economic growth in India. The SEM makes it possible to identify several intermediate channels through which fiscal deficit affects economic growth. The above equation (1) turns out to be the first equation in the SEM. The other equations for building the SEM are formulated in the following paragraphs.

The second equation in the SEM is for GDCFG (hereafter gross investment) to address the issue of simultaneity between capital formation and economic growth. It is expected that a higher growth rate would have a positive impact on gross investment. It creates a favorable economic environment, boosts up investor’s confidence and enhances the aggregate demand in an economy. Thus, it may influence output expectations and tend to augment gross investment in the economy. Therefore, GDPFC is added as an input to this equation. Fiscal deficit, financed by borrowing from domestic credit markets, is supposed to exert upward pressure on real interest rates, & hence, affects gross investment. It may reduce supply of funds available to the private sector for investment purposes. The nature and composition of expenditure raised through fiscal deficit also matters for further capital formation in the economy. Thus, it is added as one of the regressors to this equation. An increase in real interest rates (RINTR) raises real cost of capital, which may shrink gross investment. On the other hand, higher real interest rates encourage deposits and hence, increase the availability of funds to finance high yield investment projects while discarding low yield projects. Further, sufficient availability of bank credit (BANCRG) would facilitate finance for plants, machinery, equipment etc., which enhances investment in the economy. Hence, these variables i.e., RINTR and BANCRG are added to this equation. The second equation as gross investment for SEM is as follows.

GDCFG=f (GDPFC, FSDFG, RINTR, BANCRG)………… (2)

Next, an equation for fiscal deficit is constructed to measure possible reverse influence of economic growth on it along with potential effects of other variables. Higher growth rate leads to an improvement in revenue collection; hence it will reduce the fiscal deficit. Higher interest rate forces the government to pay more amounts of money on accumulated past debt, and hence, it augments fiscal deficit. An increase in current account deficit (CADFG) implies that more funds are required for resettlement of foreign exchange reserve. Hence, it may lead to a rise in fiscal deficit. Thus, the following specification is used for the fiscal deficit equation in the SEM.

FSDFG= f (GDPFC, RINTR, CADFG)……………….…… (3)

Then, the fourth equation for current account deficit is included in the SEM to examine the ‘Twin Deficit Hypothesis’ and its indirect influence on growth through fiscal deficit. Higher growth tends to increase aggregate demand due to expansion of aggregate income of an economy. It may lead to an increase in the demand for foreign goods and services. More openness (TOPNG) provides an easy access to foreign goods and services. Hence, economic growth and trade openness are added as explanatory variables to the equation. We have added exchange rate (REXCH) as another variable in this equation. An appreciation of domestic currency reduces competitiveness in the foreign markets, which helps in fueling current account deficit. On the other hand, depreciation of currency induces exports and restricts imports, which helps in reducing CAD. An increase in fiscal deficit would induce domestic absorption, which leads to import expansion and causes a current account deficit. Financing pattern of fiscal deficit also matters for its effect on CAD. Internal financing has indirect effect on CAD through an increase in domestic interest rate, causing capital inflows, exchange rate appreciation etc., while directly the creation of external borrowing to finance the fiscal deficit leads to a larger current account deficit. Therefore, fiscal deficit is added as an explanatory variable to the equation to examine the ‘Twin Deficits Hypothesis’, i.e., whether fiscal deficit fuels current account deficit. Rafiq (2010) examined the interaction between government fiscal deficits, the current account balance and the real exchange rate for the U.K. and U.S. economies. Thus, the fourth equation is specified in the SEM as follows.

CADFG= f (GDPFC, FSDFG, TOPNG, REXCH)……………… (4)

Finally, an equation for interest rate has been added in the SEM. The purpose of this equation is to verify its indirect impact on economic growth through investment and fiscal deficit. Fiscal deficit is used as one of the regressors to estimate the interest rate equation because bond financing of fiscal deficit tends to increase the supply of fresh government bonds in the securities market. Excess supply of it would result in a fall in the prices of these government bonds and rise in the interest rates. Similarly, higher growth of money supply (GRWMS) would result in excess reserves and more availability of credit with the banks. These excess reserves might be used for purchasing of government bonds through open market operations. Hence, it would enhance the bond prices and reduce the rate of interest. The Fisher relation is still valid in the Indian context (Bhanumurthy and Agarwal 2003). Dua and Pandit (2002) found that foreign interest rates (FORINT) played a major role in determining the interest rates in India. Therefore, money supply, inflation & foreign interest rate are used as other explanatory variables in the equation. Thus, the following specification is used for interest rate equation.

RINTR= f (FSDFG, GRWMS, INFLA, FORINT)……………….… (5)

Thus, the complete SEM contains the above equations (1) to (5), which will be used to estimate the direct and indirect effects of fiscal deficit on economic growth in India.

3. The Data and Methodology

3.1. The Dataset

It has used annual time series data from 1970-71 to 2013-14 for the econometric analysis[footnoteRef:2]. The variables, namely, Gross Domestic Product, Gross Fiscal Deficit, Gross Domestic Capital Formation, Bank Credit, Money Supply, Exchange Rate, Current Account Deficit, Trade Openness, Revenue Deficit, Capital Expenditure, Revenue Expenditure (all these variables are in Billion Rupees), Employment (in Million), Inflation Rate, Domestic Interest Rate and Foreign Interest Rate are used in the study. All variables are measured in real terms by using GDP deflator. The inflation rate is calculated from the Wholesale Price Index (WPI). Growth rate of broad money (M3) is used as a proxy for money supply. Growth rate of employment is calculated from employment in the organized public and private sectors. The trade openness is measured from total trade volume (sum of export and import) to GDP ratio. Exchange rate refers to amount of Indian rupees per unit of US dollar. Thus, an increase in exchange rate implies depreciation of Indian rupees. Annual (Gross) redemption yield of long term government of India securities is used as a proxy for domestic interest rate. These dataset are obtained from the Handbook of Statistics on Indian Economy of the Reserve Bank of India and the National Accounts Statistics of the Central Statistics Office (CSO). The USA’s real interest rate is used as a proxy for foreign interest rate and it is collected from the World Development Indicator, World Bank. [2: The study has selected the variables based on the availability of data in India, for e.g. Employment, Capital etc. Data belongs to the fiscal year, which starts with the April of the current year and ends with the March of the next year.]

3.2. Methodology

3.2.1. ARDL Model Specification

The Autoregressive Distributed Lag (ARDL) approach, developed by Pesaran et al. (2001), is used to empirically analyze the long run relationships among the included variables. Different methods like Fully Modified OLS (FMOLS), Dynamic OLS (DOLS) etc. are available for estimating more efficient estimators in a single cointegration relation. However, these methods of cointegration require all the variables to be of I (1) type. The variables considered in this study are a mix of I (0) and I (1) types. Therefore, these methods of cointegration are not appropriate for this study. The ARDL model is employed since it can be applied for all series regardless of their levels of integration, whether purely I (0), purely I (1) or mutually cointegrated. The test is very simple and is more efficient in small or finite sample data. However, this method can’t be applied to I (2) series. Hence, to examine the long run relationship, it adopts the ARDL approach to cointegration analysis for each of these equations. The following specifications of ARDL models are used in this study.

Where,

LGDPFC- Log of Gross Domestic Product at Factor Cost, LGDCFG- Log of Gross Domestic Capital Formation as a Percentage of GDP, EMPLMG- Growth Rate of Employment in the Organized Sectors, LFSDFG- Log of Central Government’s Fiscal Deficit as a Percentage of GDP[footnoteRef:3], INFLA- Inflation Rate, RINTR- Real Interest Rate, LBANCRG- Log of Total Bank Credit to Commercial Sector as a Percentage of GDP, CADFG- Current Account Deficit as a Percentage of GDP, REXCH- Exchange Rate, LTOPNG- Log of Trade Openness, GRWMS- Money Supply, FORINT- Foreign Real Interest Rate, T- Trend, ∆ is the first difference operator, ai, bi, ci, di and gi, are intercepts and coefficients, and εt is the error term of the estimated equations. [3: Fiscal deficit of the central government is used here for several reasons. First, combined fiscal deficit data isn’t available during the study period. However, it is available from 1980-81 onwards. Second, policy makers give much more importance to fiscal deficit of the central government in India than other deficits. Third, borrowing requirements of states are under the purview of central government. Hence, the study has only used the fiscal deficit of the central government.]

3.2.2. Bounds Testing Approach

After estimation of equations (1a) to (5a), the Wald test (F-statistic) can be conducted by imposing linear restrictions on the estimated long-run coefficients of one period lagged level of variables. The existence of long run relationship among the variables can be checked by testing null hypothesis of no cointegration against its alternative hypothesis of cointegration relationship. The null and alternative hypotheses are as follows:

For equation (1a)

(No long run relationship)

(Long run relationship exists)

For equation (2a)



For equation (3a)



For equation (4a)



For equation (5a)



The computed value of F-statistic will be compared with the critical values tabulated in Table CI-(V) of Pesaran et al. (2001). According to these authors, the lower bound critical values are based on the assumption that the explanatory variables are integrated of order zero, or I(0), while the upper bound critical values are based on an assumption that are integrated of order one, or I(1). Therefore, if the computed F-statistic is smaller than the lower bound value, then the null hypothesis of no long run relationship cannot be rejected. Conversely, if the computed F-statistic is greater than the upper bound value, then the null hypothesis of no cointegration can be rejected. On the other hand, if the computed F-statistic falls between the lower and upper bound values, then the results are inconclusive. After the cointegration relationship is confirmed, the following four long run models are estimated[footnoteRef:4]. [4: Bounds test do not reject the null of no cointegration for the interest rate equation.]

The use of a dynamic error correction specification is justified as all the variables have cointegration relationship. Then, Error Correction Mechanism (ECM) specifications are interpreted to model the short run dynamics of these equations. The residuals from these equations are considered as disequilibrium terms measuring the discrepancies between actual values of variables and their long run equilibrium values. These residuals are denoted as error correction terms, such as, ECM1, ECM2, ECM3 and ECM4 for each of the above estimated long run equations respectively.

3.2.3. Simultaneous Error Correction Approach

The above system of equations (1b to 4b) are then estimated by using a systems method i.e., Full Information Maximum Likelihood (FIML) Method. Four dynamic ECM models corresponding to the four long run relations can be estimated by using this method. Following Muscatelli et al. (1992), the Generalized Error Correction equation is specified as:

Where,

Yt= (LGDPFCt, LGDCFGt, LFSDFGt, CADFGt)

Xt= (EMPLMGt, INFLAt, RINTRt, LBANCRGt, LTOPNGt, REXCHt)

Ut-1= (ECM1t-1, ECM2t-1, ECM3t-1, ECM4t-1)

In the above equation (6), equation ΔLGDPFCt is estimated by imposing zero restrictions on the exogenous variables appear in other equation (i.e. only contains LGDPFC equation variables). ΔLGDCFGt is calculated by imposing zero restrictions on the exogenous variables appear in other equation (i.e. only contains LGDCFG equation variables). Similarly, ΔLFSDFGt is estimated by imposing zero restrictions on the exogenous variables appear in other equation (i.e. only contains LFSDFG equation variables). Further, ΔCADFGt is calculated by imposing zero restrictions on the exogenous variables appear in other equation (i.e. only contains CADFG equation variables).

4. Empirical Analysis

4.1. Unit Root Tests

The Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests are applied to check the order of integration of these variables. These tests assume that the null hypothesis of a series is I (1) against the alternative of it I (0). The results of unit root tests are reported in table 1 (See Appendix). The variables such as LGDPFC, LFSDFG, LGDCFG, LBANCRG, CADFG, LTOPNG, REXCH, REVDFG, LCAPEX, LREVEX and FORINT are stationary at their first difference and hence, integrated of the same order, i.e., I (1). The variables EMPLMG, INFLA, RINTR and GRWMS are stationary at their levels, i.e., I (0). Hence, it finds that these variables are a combination of both stationary and non-stationary series i.e., I (0) & I (1). This gives a valid rationale for using the ARDL bounds testing approaches to the cointegration analysis.

4.2. Bounds Testing Approaches to Cointegration

Table 2 reports the results of the bounds test. The computed value of F-statistic (6.113) for the first equation is higher than the upper bound critical value (5.72) at one per cent level of significance. The second and third equation’s computed F-statistics (6.134 and 7.275 respectively) are also greater than their critical value at one per cent level of significance. The computed F-statistic (7.908) of the fourth equation is also larger than its critical value at one per cent level of significance. Thus, the null hypothesis of no cointegration is rejected for each of these models. However, the fifth equation’s computed F-statistics (1.515) falls in the critical region as it is lower than the lower bound critical value. Thus, it accepts the null hypothesis of no cointegration. Therefore, bounds testing approaches to cointegration support a long run relationship for the Models (1) to (4) and discard the long run relationship for the Model (5). Hence, it has employed ARDL approach to cointegration for Models (1) to (4) and Two Stage Least Squares (2SLS) method for Model (5) to estimate the coefficients of these equations.

Table 2: Results of Bounds Test

ARDL Models

F Statistics

Model (1)

LGDPFC=f(LGDCFG, EMPLMG, LFSDFG, INFLA)

6.113*

Model (2)

LGDCFG=f(LGDPFC, LFSDFG, RINTR, LBANCRG)

6.134*

Model (3)

LFSDFG=f(LGDPFC, RINTR, CADFG)

7.275*

Model (4)

CADFG=f(LGDPFC, LFSDFG, LTOPNG)

7.908*

Model (5)

RINTR= f(LFSDFG, INFLA, GRWMS, FORINT)

1.515

Critical Value Bounds of F-Statistics

Lower

Bound I(0)

Upper

Bound

I(1)

10 % Level

3.03

4.06

5 % Level

3.47

4.57

1 % Level

4.40

5.72

Note: Case V: Unrestricted Intercept and Unrestricted Trend, Asymptotic Critical Value Bounds for F Statistics by Pesaran et al. (2001). * denotes 1 per cent level of significance.

4.3. Estimation of Long Run Coefficients using the ARDL Approach

After confirmation of long run relationships, equations (1b), (2b), (3b) and (4b) are estimated. Lag selection for these models are based on Schwarz Bayesian Criterion, Akaike Information Criterion and General to Specific modeling approach by strictly following the diagnostic checks. The results are reported in table 3.

In the Model (1), the coefficient of fiscal deficit is negative and significant at one per cent level. It shows that one per cent increase in fiscal deficit to GDP ratio is likely to decrease economic growth by 0.12 per cent. Thus, in the long run, persistence of fiscal deficit has a detrimental effect on economic growth in India. Capital expenditure to GDP ratio has dropped from 5.61 per cent in 1970-71 to 1.82 per cent in 2013-14. The share of non- developmental expenditure in total expenditure has increased from 43.33 per cent in 1980-81 to more than 51 per cent in 2013-14. Interest payment as percentage of GDP has grown from 1.36 per cent in 1970-71 to 3.63 per cent in 2013-14. Subsidies to GDP ratio has also gone up from 0.21 per cent in 1970-71 to 2.43 per cent in 2013-14. Thus, the resources generated through fiscal deficit may have been used more in an unproductive manner, which has a detrimental effect on the growth rate of the economy. The results also show that gross investment has a positive and significant impact on economic growth. Also, there exists a positive and significant relationship between employment in the organized sector and economic growth. Hence, higher capital formation and employment growth would accelerate growth of the economy in India. However, inflation rate has a significant and negative impact on economic growth. Thus, high inflation is not favorable for growth of the economy. The diagnostic test statistics of the Model indicate that there is no evidence of serial correlation, model misspecification, heteroscedasticity and non-normality (see Section “4.7” for diagnostic tests of the selected models).

In the Model (2), the coefficient of economic growth is positive and significant at one per cent level. It indicates that higher growth would stimulate gross investment in the economy. The estimated results also reveal that fiscal deficit has a negative and significant impact on gross investment in India. It shows that one per cent rise in fiscal deficit to GDP ratio leads to 0.13 per cent fall in gross investment, which is significant at five per cent level. Domestic market borrowing has constituted a major portion of financing the fiscal deficit in India. It may put upward pressure on real interest rates and also reduce total supply of funds available to private sector for investment. Because of an increase in debt service payments, government is unable to allocate more resources for productive capital formation. Thus, fiscal deficit has a negative impact on gross investment. Further, it shows that real interest rate has a negative impact on gross investment and its impact is significant at five per cent level. Thus, in the long run, higher cost of borrowing restricts both private and public investment in India. It also finds that the availability of bank credit would induce gross investment in India as the estimated coefficient is positive and significant at one per cent level. This equation has also passed all these diagnostic tests.

Table 3: Results for the Estimated Long Run Coefficients

Regressor

Model (1)

Model (2)

Model (3)

Model (4)

LGDPFC

LGDCFG

LFSDFG

CADFG

Coefficients

Coefficients

Coefficients

Coefficients

LGDCFG

0.269***

(1.85)

-

-

-

LGDPFC

-

0.204*

(3.99)

-1.455**

(-2.26)

10.181**

(2.06)

LFSDFG

-0.122*

(-2.79)

-0.133**

(-2.66)

-

3.415**

(2.22)

EMPLMG

0.039*

(3.69)

-

-

-

INFLA

-0.007**

(-2.66)

-

-

-

RINTR

-

-0.005**

(-2.19)

0.027**

(2.61)

-

LBANCRG

-

0.312*

(3.16)

-

-

CADFG

-

-

0.120*

(3.69)

-

LTOPNG

-

-

-

0.061

(0.02)

REXCH

-

-

-

-0.143*

(-2.87)

C

7.925*

(17.95)

0.476***

(1.90)

14.026**

(2.62)

-81.686**

(-2.04)

TREND

0.057*

(16.39)

0.075**

(2.06)

-0.644*

(-2.54)

Notes: *, ** and *** denote statistical significance at the one, five and ten per cent levels respectively. The figures in parentheses are the t-statistics. For Model (1), ARDL (2,0,0,0,0) selected based on Schwarz Bayesian Criterion (SBC). Model (2), ARDL (2,0,0,0,0) selected General to Specific modeling approach. Model (3), ARDL (1,1,0,0) selected based on SBC. Model (4), ARDL (2,0,0,2,0) selected on Akaike Information Criterion (AIC).

In the Model (3), economic growth has a negative and significant effect on fiscal deficit. It provides the justification for using a simultaneous equation model, as the bi-directional relationship is confirmed. The government will receive more revenue, if the economy experiences a higher growth. Thus, it would reduce the fiscal deficit by shrinking the gap between expenditure and revenue. The coefficient of interest rate is positive and statistically significant. This is expected as higher interest rate compels the government to pay more on past accumulated debt. Thus, an increase in interest payment leads to rise in fiscal deficit. It also shows that CAD has a positive and significant impact on fiscal deficit. It leads to outflow of money from domestic economy to the rest of the world. Thus, it may increase government expenditure, which would swell up fiscal deficit of the economy. The estimation has also passed all these diagnostic tests.

In the Model (4), the coefficient of economic growth is positive and statistically significant at 5 per cent level. Higher growth tends to increase aggregate income, which augments demand for goods and services both inside and outside of the economy. Fiscal deficit has a significant positive impact on CAD. Hence, it supports the famous ‘Twin Deficits Hypothesis’ for the Indian economy. More openness facilitates an easy access to foreign goods and services. Hence, it enhances the CAD in the economy. But trade openness has insignificant impact on CAD. However, the coefficient of exchange rate has a negative and statistically significant effect on CAD. This is expected as depreciation of currency induces exports and reduces imports, which helps in reducing CAD. The estimated equation has also passed all these diagnostic tests.

4.4. Estimation of Coefficients using the 2SLS Method

Estimation of Model (5) by 2SLS method shows that fiscal deficit has a positive impact on interest rate, which is significant at 1 per cent level (table 4).

Table 4: Results of Estimated 2SLS Coefficients for Model (5)

Dependent Variable: RINTR

Method: Two-Stage Least Squares

Variable

Coefficient

Std. Error

t-Statistic

P-value

LFSDFG

4.0674*

1.5138

2.6868

0.0106

GRWMS

-0.0309

0.0976

-0.3172

0.7528

FORINT

0.4159*

0.1471

2.8256

0.0075

INFLA

-1.0497*

0.0958

-10.9565

0.0001

C

1.6623

3.0834

0.5391

0.5930

R-squared: 0.9144, Adjusted R-squared : 0.9053, F-statistic : 101.3114,

Prob(F-statistic): 0.0001

Note: *denotes 1per cent level of significance.

A rise in public borrowing with the issue of new government bonds and securities would push up interest rates in the economy. Money supply has a negative but insignificant effect on interest rate. Inflation has a significant and negative impact on interest rate. The coefficient of foreign interest rate is positive and significant at 1 per cent level. It is as expected, since any positive difference between domestic and foreign interest rate would cause outflow of capital from the domestic economy.

4.5. Estimation of Short Run Coefficients using FIML

The results of the dynamic error correction models are shown in the table 5. The coefficients of ECM term for all these models are negative and highly significant at one percent level. It justifies the use of dynamic specification of these cointegrated equations. The magnitudes of these ECM terms are high, i.e., -0.76, -0.88, -0.57 and -0.86 respectively. These reflect high speed of the adjustment process which corrects the disequilibrium of the previous year’s shock converges the system back to the long run equilibrium in the current year. In the short run, fiscal deficit has a negative impact on economic growth, but it is insignificant. Employment has a significant positive effect on it, while inflation has a significant negative influence on it. It shows that gross investment has an insignificant effect on economic growth in the short run. Availability of bank credit affects gross investment positively in the short run. Real interest rate has a negative and significant impact on gross investment. However, both economic growth and fiscal deficit have an insignificant effect on it in the short run. All variables such as economic growth, interest rate and CAD have an insignificant effect on fiscal deficit in the short run, which indicates that the fiscal deficit is an independent and policy variable in India. In the short run, trade openness has a positive and significant effect on CAD. However, other variables have an insignificant influence on CAD in the short run. Thus, it is found that fiscal deficit does not influence economic growth, gross investment and CAD in the short run. But in the long run, fiscal deficit has an influence on these variables.

Table 5: Results for the Estimated Short Run Coefficients

Regressor

Model 1

Model 2

Model 3

Model 4

ΔLGDPFC

ΔLGDCFG

ΔLFSDFG

ΔCADFG

Coefficients

Coefficients

Coefficients

Coefficients

ΔLGDCFG

0.007

(0.13)

-

-

-

ΔLGDPFC

-

1.036

(1.62)

-0.580

(-0.26)

7.868

(0.90)

ΔLGDPFC_1

0.325**

(2.40)

-

-

-

ΔLFSDFG

0.028

(-0.73)

-0.139

(-1.27)

-

1.190

(0.67)

ΔEMPLMG

0.008*

(3.99)

-

-

-

ΔINFLA

-0.002*

(-3.04)

-

-

-

ΔRINTR

-

-0.009*

(-4.26)

0.007

(1.15)

ΔLBANCRG

-

0.433**

(2.51)

-

-

ΔCADFG

-

-

0.005

(0.15)

-

ΔLTOPNG

-

-

-

4.364**

(2.25)

ΔREXCH

-

-

-

-0.028

(-0.83)

C

0.038*

(4.87)

-0.050

(-1.34)

0.034

(0.27)

-0.605

(-1.13)

ECMt-1

-0.769*

(-2.92)

-0.884*

(-5.87)

-0.578*

(-2.75)

-0.866*

(-4.35)

Notes: *, ** and *** denote statistical significance at the one, five and ten per cent levels respectively. The figures in parentheses are the t-statistics.

4.6. Robustness Check

It has extended the Model (1) to check the robustness and also to understand the complex relationship between growth and deficit. It has again estimated the Model (1) by replacing revenue deficit for fiscal deficit keeping the others the same as before. Further, Model (1) is re-estimated with revenue expenditure and capital expenditure (as a percentage of GDP) in place of fiscal deficit[footnoteRef:5]. It is basically to estimate the differential impact of these expenditures on economic growth. It has reported the results of this extended Model (1) under the headings of Model (1.1) and Model (1.2) respectively. The previously defined methodology and procedures are followed for the analysis of these extended equations. The long run coefficients and short run dynamics for these extended Models are shown in the table 6. [5: Bose and Bhanumurthy (2015) estimated the values of capital expenditure multiplier, transfer payments multiplier and other revenue expenditure multiplier are 2.45, 0.98, and 0.99, respectively for India. They also fund that the cumulative multiplier for capital spending is even higher at 4.8 per cent.]

Table 6: Results for the Estimated Long Run and Short Run Coefficients for Model (1.1) and Model (1.2)

Long Run Estimation

Short Run Estimation

Dependent variable is LGDPFC

Dependent variable is ΔLGDPFC

Regressor

Model (1.1)

Model (1.2)

Regressor

Model (1.1)

Model (1.2)

Coef.

Coef.

Coef.

Coef.

LGDCFG

0.303***

(1.71)

0.302**

(2.31)

ΔLGDPFC_1

0.292**

(2.04)

0.315**

(2.33)

REVDFG

-0.026***

(-1.65)

-

ΔLGDCFG

0.077***

(1.88)

0.069***

(-1.73)

LCAPEX

-

0.0102

(0.15)

ΔEMPLMG

0.007*

(3.38)

0.007*

(3.59)

LREVEX

-

-0.366**

(-2.48)

ΔREVDFG

-0.004

(-1.03)

-

INFLA

-0.007**

(-2.19)

-0.008*

(-2.92)

ΔLREVEX

-

-0.096

(-1.58)

EMPLMG

0.0391*

(3.10)

0.034*

(3.37)

ΔLCAPEX

-

0.010

(0.53)

C

7.635*

(15.63)

8.414*

(16.25)

ΔINFLA

-0.002*

(-3.37)

-0.002*

(-3.54)

TREND

0.060*

(11.17)

0.060

(10.42)

C

0.039*

(4.68)

0.039*

(4.99)

ECMt-1

-0.706 **

(-2.69)

-0.746*

(-2.77)

Notes: *, ** and *** denote one, five and ten per cent levels of significance respectively. REVDFG is revenue deficit of the central government as per cent of GDP. LCAPEX is log of capital expenditure as per cent of GDP and LREVEX is log of revenue expenditure as per cent of GDP. For Model (1.1), ARDL (2,0,0,0,0) selected based on Schwarz Bayesian Criterion (SBC). Model (1.2), ARDL (2,0,0,0,0,0) selected based on SBC. Coef. refers to coefficients.

The results of Model (1.1) show that the coefficients of gross investment and employment growth are positive and significant. Inflation has a significant negative impact on economic growth in India. These results confirm the robustness of the earlier findings. An increase in revenue deficit has a negative and significant effect on economic growth. Thus, high revenue deficit has an adverse effect on economic growth in the long run. Estimation of Model (1.2) shows that gross investment and employment growth have positive impact and inflation has negative impact on economic growth in India. Therefore, it also confirms the robustness of the earlier findings. The results also show that an increase in revenue expenditure has a detrimental effect on economic growth and it is significant at five per cent level. Interest payment and subsidies, which are unproductive in nature, absorb a significant part of the revenue expenditure,. Capital expenditure has a positive impact on economic growth but it is not statistically significant. Thus, the estimated results show that the composition of expenditure matters for growth in the economy.

The results of the dynamic error correction models for the extended Models (1.1) and (1.2) are also presented in the table 6. The coefficients of ECM term for both these equations are negative and highly significant at one percent level. In the short run, revenue deficit has a negative impact on economic growth, but it is insignificant. The impacts of employment and inflation rate on economic growth are consistent with the earlier findings. However, both revenue and capital expenditure have an insignificant effect on economic growth in the short run.

Hence, it is found that fiscal deficit and revenue deficit have an adverse effect on growth of the economy. Indirectly, high fiscal deficit hampers gross investment, hence growth of the economy. There exists a bidirectional relationship between fiscal deficit and CAD. Thus, persistence of twin deficit affects economic growth. Fiscal deficit tends to increase interest rate. Higher interest rate has an adverse effect on investment, thus, adversely affects growth of the economy. Revenue expenditure has an adverse impact on economic growth. Hence, fiscal deficit affects economic growth through its effects on investment, interest rate, current account deficit, and revenue expenditure.

4.7. The Diagnostic Tests and Stability Test

Table 7 shows the results of diagnostic tests for all these estimated models. The diagnostic tests indicate that there is no evidence of Serial Correlation, Model Misspecification, Non-Normality, Heteroscedasticity and ARCH effect at 5 per cent level of significance. The results of the stability tests (CUSUM and CUSUMSQ) are given in the appendix. If the plots of CUSUM and CUSUMSQ statistics stay within 5 per cent significance level, then the estimated coefficients are said to be stable. The CUSUM and CUSUMSQ statistics of all these models are plotted against the critical bound of 5 per cent significance level. The results show that all these estimated coefficients are stable over time. Hence, the reported results can be reliable.

Table 7: Diagnostic Tests

Models

Test Statistics

Serial Correlation

Ramsey's RESET

Normality

Hetero-scedasticity

ARCH Test

Model (1)

LM- Version

1.984

[0.159]

0.571

[0.449]

1.011

[0.603]

0.009

[0.924]

0.979

[0.613]

F- Version

1.636

[0.210]

0.455

[0.504]

0.008

[0.926]

0.382

[0.686]

Model (2)

LM -Version

1.035

[0.309]

0.767

[0.381]

1.105

[0.575]

2.207

[0.137]

0.993

[0.609]

F- Version

0.859

[0.360]

0.632

[0.432]

2.218

[0.144]

0.399

[0.674]

Model (3)

LM -Version

0.070

[0.791]

0.322

[0.570]

4.500

[0.105]

2.994

[0.084]

9.749

[0.136]

F -Version

0.057

[0.813]

0 .263

[0.611]

3.071

[0.087]

1.461

[0.226]

Model (4)

LM -Version

0.724

[0.395]

0.291

[0.590]

2.164

[0.339]

0.471

[0.492]

2.050

[0.359]

F -Version

0.543

[0.467]

0.216

[0.645]

0.454

[0.504]

0.769

[0.472]

Model (1.1)

LM -Version

3.561

[0.059]

0.044

[0.835]

0.748

[0.688]

0.169

[0.681]

0.094

[0.954]

F -Version

3.056

[0.090]

0.034

[0.854]

0.162

[0.689]

0.036

[0.965]

Model (1.2)

LM -Version

2.922

[0.087]

0.291

[0.590]

1.866

[0.393]

0.006

[0.937]

1.344

[0.511]

F -Version

2.393

[0.132]

0.223

[0.640]

0.006

[0.939]

0.512

[0.604]

Note: The figures in parentheses are the P-values.

5. Conclusion

A combination of ARDL and Simultaneous Error Correction approach is used to examine the relationship between fiscal deficit and economic growth in India. The ARDL bounds test approaches to cointegration confirm the existence of long run relationship for Models (1) to (4). The findings show that fiscal deficit has a negative and significant effect on economic growth at one per cent level. Thus, high fiscal deficit has a detrimental effect on growth of the Indian economy in the long run. Economic growth has a negative and significant effect on fiscal deficit at 5 per cent level. Thus, there exists a bi-directional relationship between fiscal deficit and economic growth in the long run. Gross investment and employment growth have a positive impact, whereas, inflation rate has a negative impact on economic growth in the long run. The results also reveal that fiscal deficit has a negative and significant impact on gross investment in India. The findings also support the ‘Twin Deficit Hypothesis’ as fiscal deficit has a significant positive impact on CAD. The results of 2SLS for interest rate show that fiscal deficit has a positive and significant impact on interest rate.

However, the findings of generalized error correction approach show that fiscal deficit has a negative impact on economic growth; but it is insignificant in the short run. Fiscal deficit has an insignificant influence on gross investment and CAD. Thus, fiscal deficit does not influence economic growth, gross investment and CAD in the short run. The revenue deficit has a negative and significant effect on economic growth. Thus, high revenue deficit has an adverse effect on GDP in the long run. The results show that an increase in revenue expenditure has a detrimental effect on economic growth, while capital expenditure has a positive but insignificant impact on it in the long run. In the short run, revenue deficit, revenue expenditure and capital expenditure have insignificant negative impacts on economic growth. The study suggests that government should control fiscal deficit and revenue deficit. The composition of expenditure should be altered to devote more resources towards productive capital formation. Necessary steps should be taken to control high inflation since it hampers the growth of the economy.

Acknowledgements

The author is grateful to the National seminar participants at University of Hyderabad, India and especially to Prof. N R Bhanumurthy and Prof. Pradipta Chaudhury for their valuable suggestions to improve the quality of the paper. However, the errors, if any, are mine.

Appendix

Table 1: Results of Unit Root Tests

Variables

ADF Test

PP Test

Level

First Difference

Level

First Difference

C

C&T

C

C&T

C

C&T

C

C&T

LGDPFC

3.16

(1.00)

-1.98

(0.60)

-6.00

(0.00)

-7.50

(0.00)

3.48

(1.00)

-1.99

(0.59)

-6.04

(0.00)

-7.90

(0.00)

LFSDFG

-2.82

(0.06)

-2.67

(0.25)

-6.90

(0.00)

-6.93

(0.00)

-2.79

(0.06)

-2.63

(0.26)

-7.92

(0.00)

-8.19

(0.00)

LGDCFG

-0.76

(0.82)

-2.82

(0.19)

-8.28

(0.00)

-8.26

(0.00)

-0.36

(0.91)

-2.82

(0.19)

-9.74

(0.00)

-11.75

(0.00)

EMPLMG

-4.14

(0.00)

-4.46

(0.00)

-7.90

(0.00)

-7.99

(0.00)

-4.15

(0.00)

-4.55

(0.00)

-12.74

(0.00)

-14.41

(0.00)

INFLA

-4.71

(0.00)

-5.43

(0.00)

-6.59

(0.00)

-6.51

(0.00)

-4.57

(0.00)

-4.97

0.00)

-14.96

(0.00)

-14.22

(0.00)

RINTR

-3.70

(0.00)

-4.11

(0.01)

-6.56

(0.00)

-6.48

(0.00)

-3.70

(0.00)

-4.19

(0.00)

-11.25

(0.00)

-11.03

(0.00)

LBANCRG

-1.15

(0.68)

-1.55

(0.79)

-5.15

(0.00)

-5.11

(0.00)

-1.12

(0.69)

-1.87

(0.65)

-5.30

(0.00)

-5.27

(0.00)

CADFG

-2.91

(0.06)

-3.13

(0.11)

-7.93

(0.00)

-7.81

(0.00)

-2.88

(0.06)

-3.10

(0.12)

-8.06

(0.00)

-7.98

(0.00)

GRWMS

-5.08

(0.00)

-5.10

(0.00)

-9.37

(0.00)

-9.25

(0.00)

-5.08

(0.00)

-5.11

(0.00)

-15.15

(0.00)

-14.94

(0.00)

LTOPNG

-0.37

(0.90)

-1.61

(0.77)

-5.26

(0.00)

-5.18

(0.00)

-0.42

(0.89)

-1.84

(0.66)

-5.26

(0.00)

-5.18

(0.00)

REXCH

-2.91

(0.05)

-3.08

(0.12)

-4.61

(0.00)

-4.74

(0.00)

-3.26

(0.02)

-2.99

(0.15)

-4.63

(0.00)

-4.77

(0.00)

REVDFG

-1.96

(0.30)

-3.15

(0.11)

-6.81

(0.00)

-6.74

(0.00)

-1.79

(0.37)

-3.18

(0.10)

-7.54

(0.00)

-7.45

(0.00)

LCAPEX

0.16

(0.96)

-3.14

(0.11)

-7.51

(0.00)

-7.62

(0.00)

-0.15

(0.93)

-3.03

(0.14)

-7.87

(0.00)

-9.39

(0.00)

LREVEX

-2.50

(0.12)

-2.55

(0.31)

-6.34

(0.00)

-6.32

(0.15)

-2.87

(0.06)

-2.54

(0.30)

-6.43

(0.00)

-6.50

(0.00)

FORINT

-2.24

(0.19)

-2.12

(0.52)

-5.71

(0.00)

-5.83

(0.00)

-2.07

(0.26)

-1.89

(0.64)

-5.71

(0.00)

-5.84

(0.00)

Notes: Brackets show MacKinnon (1996) one-sided p- values. C- Constant and T- Trend.

Figure 4.1: Cusum and Cusumsq test for Model (1)

Plot of Cumulative Sum of Recursive Residuals

The straight lines represent critical bounds at 5% significance level

-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012

Plot of Cumulative Sum of Squares of Recursive Residuals


-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012




-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012



-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012




-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012



-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012




-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012



-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012

Figure 4.5: Cusum and Cusumsq test for Model (1.1)



-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012



-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012

Figure 4.6: Cusum and Cusumsq test for Model (1.2)



-5

-10

-15

-20

0

5

10

15

20

1972

1977

1982

1987

1992

1997

2002

2007

2012



-0.5

0.0

0.5

1.0

1.5

1972

1977

1982

1987

1992

1997

2002

2007

2012

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journal€¦ · web viewhowever, the ricardian view considers that deficits have a neutral effect...

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