Public Debt and Regional Inequality in India: Spatial Planning Implications

Abstract

The Indian economy has shown a high rate of growth of gross domestic product of 7.56% during 2015–16 relative to 6.69% during 2011–12. In the post-liberalization economic era the size of fiscal debt has been skyrocketing as well. The fiscal debt is defined as the difference between total government expenditure and current revenue which has been escalating to an unsustainable level in recent periods. Maharashtra the most industrialized state in Western India has the largest debt followed by Uttar Pradesh and West Bengal in Central and Eastern India. Also, Tamil Nadu, Karnataka and Andhra Pradesh in the south have witnessed the maximum increase in debt during the past five years. Although Kaur et al. (2014) suggest that debt position of states at the aggregate level is sustainable. Gupta (2001) on the contrary opined unsustainability of debt at the state level. Government borrows debt to finance plan expenditure such as building roads, dams and airports and non-plan expenditure such as paying salaries or making interest payments. Debt is not bad provided states are growing at a rapid economic rate to service loans. This implies the interest payment to gross state domestic product (GSDP) ratio will be a better indicator of sustainability. Using these criteria, it is observed that states from all corners in India, i.e. Tamil Nadu, West Bengal, Gujarat and Punjab have been consistently paying huge amounts of servicing costs. Given this overview, this research examines temporal and spatial patterns of state debt in India during the period 2002–15. National and state level data are utilized to study the spatial analysis of state debt in India for 30 regions comprising 28 states and 2 territories, i.e. National Capital Territory of Delhi and Puducherry. The following questions are addressed in this paper: (1) What insights does the literature on public debt provide in improving the understanding of the relationship between public debt and growth? (2) What are the broad trends of public debt and regional inequality in India at the state level? and (3) What are the characteristics of space-time patterns of public debt in India during 1991–2015? Several spatial analytical methods such as Gini Coefficient, Kernel density, Theil Entropy and regression analysis are utilized to identify and describe spatial patterns of state liability and its geographical dynamics in India. Data for analysis are obtained from Planning Commission and Reserve Bank of India. Spatial planning implications are addressed as well.