Narrow Results

This study uses data from 1986 to 2021 and the auto-regressive distributed lag model to explore India’s fiscal policy determinants of government health spending. The results find two structural breaks in time: (i) 2002 for state government health spending (SGHS) and (ii) 2014 for central government health spending. The results also show that central revenue transfers to states have a positive and statistically significant effect on SGHS in the long run. The results imply that a 1% rise in central revenue transfers to states leads to a 0.399% increase in SGHS. Further, state government public debt exhibits a negative and statistically significant relationship with SGHS, implying that a 1% rise in public debt leads to a 0.119% fall in SGHS in the long run. Fiscal management (i.e., revenue mobilization and debt sustainability) is essential to prepare a long-term strategy for health-care financing.

Observed public debt of developed economies is not only countercyclical but also acyclically increasing for fairly long peacetime. This paper proposes a politico-economic theory of public debt which coherently rationalizes both acyclical and countercyclical behaviors of public debt. An office-seeking policymaker decides fiscal policies to win over finitely-lived voters who face uninsurable idiosyncratic risk on their disposable incomes. The equilibrium public debt is (i) acyclically increasing, or (ii) countercyclical, or (iii) acyclically decreasing. An increase in the idiosyncratic risk can change public debt behavior to acyclically increasing from countercyclical, entailing rises in public debt.

To understand whether and how movements of government debt and household debt are related, stationary equilibrium government debt and household debt are characterized in a politico-economic model where office-seeking policymakers decide government debt and individual voters can borrow facing uninsurable idiosyncratic income shocks. An increase in uninsurable income risk unconditionally raises stationary equilibrium government debt and aggregate household debt together, while an increase in household-loan collateral value or population aging conditionally does so, entailing positive correlations between these two debts’ movements. In contrast, an increase in interest rate conditionally causes these two debts to move in the opposite directions.

In the wake of the recent global crisis, the international community is giving an increased focus on stability of the financial system. With the increasing need for data sets to undertake this analysis, the question naturally arises as to what types of data are needed? While various data initiatives are underway, two initiatives at the forefront are: (1) the International Monetary Fund/Financial Stability Board Group of Twenty (IMF/FSB G-20) Data Gaps Initiative (DGI) which is endorsed by the G-20 Finance Ministers and Central Bank Governors as well as the IMF's International Monetary and Financial Committee and (2) the new Special Data Dissemination Standard (SDDS) Plus, aimed particularly at economies with systemically important financial sectors. This paper explains the relevance of the DGI for financial stability analysis and the close link with the SDDS Plus.

Based on a sample during 1978–2014, this paper finds that India’s real GDP has a positive relationship with real depreciation during 1978–2002, the government debt/GDP ratio, the real stock price, the growth rate of U.S. real GDP, and a negative relationship with real depreciation during 2003–2014, the real lending rate and the expected inflation rate. Therefore, the stage of economic development may play an important role in deciding whether real depreciation or real appreciation may promote economic growth.