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


This paper examines the impact of fiscal deficit on economic growth in India, during the period from 1970–71 to 2018–19. Using a combination of Autoregressive Distributed Lag and Simultaneous Error Correction Approach, this study shows that fiscal deficit and revenue deficit have an adverse effect on economic growth both in the long run and in the short run. The empirical analysis confirms that fiscal deficit influences economic growth both directly, and indirectly through the routes of investment, interest rate, current account deficit and composition of government expenditure. Further, gross investment has a positive and inflation rate has a negative impact on economic growth. For a policy perspective, the government should control fiscal deficit and revenue deficit as suggested by the FRBM Act. The composition of government expenditure should be altered to devote more resources for the formation of productive capital in India.

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  1. The fiscal deficit of the central government has increased from 3.01% of the gross domestic product (GDP) in 1970-71 to more than 8% of GDP in 1986-87. Then, it has gradually declined to 4.4% in 2003-04 (FRBM Act, 2003 introduced in India), and it was more than 3% in 2018-19. It is to be noted that fiscal deficit has always been above the target set by FRBM Act (exceptional year 2007-08). Revenue deficit has increased from 4.5% of fiscal deficit in 1981-82 to nearly 65% in 2018-19. 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 42.11% of GDP in 1980-81 to nearly 50% in 2018-19.

  2. It states that the aggregate output of an economy for a given period depends on the labor force, capital formation, and total factor productivity.

  3. Here, GDP refers to gross value added (GVA) at basic prices, which is a true reflection of actual production of an economy. The new base year 2011-12 has replaced data of GDP at factor cost with GVA at basic prices. It is relatively a better representative than the GDP at market price because the value of GDP can be easily manipulated by altering indirect taxes and subsidies. Thus, the study has taken GVA at basic price for GDP.

  4. Mohanty (2018) found that the implementation of FRBM Act has influenced and weakened the fiscal deficit-economic growth linkage in India.

  5. Mohanty (2019a) found that fiscal deficit and its financing (especially internal financing) crowds out private corporate sector investment in India.

  6. Mohanty and Bhanumurthy (2020) find that fiscal deficit has a direct, though marginal, impact in the short-run, however, through the indirect channel, i.e., through inflation, it has a larger positive impact on interest rates in the long run in India.

  7. The study has selected the variables based on the availability of data in India, for e.g. employment, fiscal deficit 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.

  8. 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.

  9. 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.

  10. The specifications of the null and alternative hypotheses are removed due to space constraint.

  11. According to these authors, if the computed F-statistic is smaller than the lower bound value [assumed variables are I (0)], 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 [assumed variables are I (1)], 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.

  12. Equation (5) is not considered because of absence of cointegration by bounds test (for details see section “Bounds Testing Approaches to Cointegration”).

  13. 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 found that the cumulative multiplier for capital spending is even higher at 4.8%.


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The earlier version of the paper was presented at the National Seminar on Challenges and Issues in the Fiscal Federalism of India, organized by the School of Economics, University of Hyderabad, India in 2016. The author would like to thank the anonymous referees, N R Bhanumurthy, Pradipta Chaudhury and the seminar participants for their useful comments and suggestions. However, the errors, if any, are mine.

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Correspondence to Ranjan Kumar Mohanty.

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Mohanty, R.K. Fiscal Deficit and Economic Growth Nexus in India: A Simultaneous Error Correction Approach. J. Quant. Econ. 18, 683–707 (2020).

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