Fiscal consolidation with high growth: A policy simulation model for India

In this paper a fiscal consolidation program for India has been presented based on a policy simulation model that enables us to examine the macroeconomic implications of alternative fiscal strategies, given certain assumptions about other macro policy choices and relevant exogenous factors. The model is then used to estimate the outcomes resulting from a possible strategy of fiscal consolidation in the base case. The exercise shows that it is possible to have fiscal consolidation while at the same time maintaining high GDP growth of around 8% or so. The strategy is to gradually bring down the revenue deficit to zero by 2014–15, while allowing a combined fiscal deficit for centre plus states of about 6% of GDP. This provides the space for substantial government capital expenditure, which translates to a significant public investment program. This in turn leads to high overall investment directly and indirectly, via the crowding in effect on private investment, which drives the high GDP growth. The exercise has also tested the robustness of this strategy under two alternative scenarios of higher and lower advanced country growth compared to the base case.     

Recent trends in the condition of children in India: A statistical profile

Global recession has not affected the status of Indian children directly, thanks to the basic insularity of the country's economy, but it has constrained the government's ability to maintain/expand child-related programmes in real terms. This paper analyses recent trends in a large set of quality of life indicators for children in India. While labour market data and nutrition statistics appear ambiguous, other measures such as anthropometric measures, e.g. infant mortality, life expectancy and literacy rates, do not show generalized deterioration and occasionally provide evidence of improvements. The latter, however, show important inter-state variations, with conditions actually deteriorating in some states. Moreover, the improvements observed have been registered under very low absolute conditions of living.     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.