Impact of fiscal transfers policy on regional growth convergence in India

Abstract

This study is an attempt to empirically analyze the effect of fiscal transfers on growth and regional growth convergence in India during 2005–2019, using the standard growth convergence model for panel data. Results indicate the growth convergence across Indian States. The regional income gaps reduced at a rate of 17.7–31.9% per annum. The fiscal transfers contribute to the growth of 22 out of 29 States and also contribute significantly to the convergence. Moreover, there is strong evidence for convergence across General Category States and across Special Category States. The average income growth is higher in Special Category States and higher in post global crisis period. It is our hope that these results will be useful to policymakers and other stakeholders to take appropriate strategies to design fiscal transfer policy such that it will speed up the convergence process in India.

Keywords:

• Growth convergence
• fiscal transfers
• Indian states
• panel data methods

JEL CLASSIFICATION:

• E22
• E62
• R11
• R12
• R23
• C23

Acknowledgement

We are thankful to Dr. C. Rangarajan, the Chairman of Madras School of Economics for his valuable suggestions on an early version of this paper. We are also thankful to two anonymous referees of the journal for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 However, Barro (1999) argues that transfers and associated tax finance will generally distort economic decisions. A greater level of income redistribution will induce more distortions which tend to reduce investment and thus slow down economic growth.

2 As per the XV^{^th} Finance Commission Report, Vol. IV The States, October 2020.

3 In 1992, the 73^rd and 74^th Constitutional amendments empowered the urban and the rural local governments as the third tier.

4 In contrast to this, one can postulate that all regions in the long run have no tendency to display variation in the rates of investment, capital depreciation, population growth etc. In such case, the model will generate unconditional or absolute convergence to a common steady state per capita income. In other words, if all the economies in a group have the same steady state, poorer economies in the group should grow faster, which is called absolute convergence.

5 The conditional convergence can be related to “Club Convergence”, which can be traced back to Baumol (1986), but its more rigorous formulations owes to Durlauf and Johnson (1995) and Galor (1996). In the case of conditional convergence, equilibrium differs by the economy and each economy approaches its own but unique equilibrium. In contrast, the club convergence is based on models that yield multiple equilibrium. An economy will reach one of these different equilibriums depending on its initial position or some other characteristics. A group of economies/States may reach a particular equilibrium if they share the initial location or attribute corresponding to that equilibrium. However, many argue that evidence of conditional convergence is to a large extent consistent with the club convergence.

6 Durlauf and Quah (1999) argue that over 90 different conditioning (X) variables have been appeared in existing studies, which analyze country level data. At the State level, most variables are not relevant and many State specific variables are not available or unobservable.

7 . Following past studies on the topic like Rao, Shand, and Kalirajan (1999) and Nagaraj, Varoudakis, and Vèganonès (1998), we have added these contraol variables and a few other control variables like railway length, % of non-primary sector GSDP in total GSDP, literacy rate etc. in the initial estimation. As other control variables are not significant, they are dropped in the final analysis. It is noted that all remaining conditioning variables can be subsumed under the region (State) specific fixed effects.

8 This assumption of common rate of convergence is tenable for all Indian States which are likely to be similar in terms of the underlying parameters of technology and preferences. This rate indicates that any sample State on an average reduces 12.8% of the gap between its steady state income and initial income every year.

Additional information

Notes on contributors

Shanmugam K.

Shanmugam, K. is currently a research scholar at Madras School of Economics. His specializations are Public Finance and Applied Economics. He has recently retired as Chief Secretary of Government of Tamil Nadu.

Shanmugam K. Rangasamy

Shanmugam, K.R. is the Director and Professor of Madras School of Economics. His specializations are Public Finance, Applied Econometrics, and Economics of Human Resources. He has published more than 50 research articles in national and international journals and published 6 books.