The Reserve Bank of India released Volume 45, No.1, 2024 of its Occasional Papers, a research journal containing contributions from its staff. This issue contains three articles and three book reviews.
Articles:
- Determinants of Household Saving Portfolio in India: Evidence from Survey Data
This paper provides an assessment of the household saving and investment behaviour by studying both household-specific determinants and time-varying macroeconomic factors. Based on the ‘Aspirational India’ database of CPHS-CMIE, the econometric analysis based on a multinomial logit model suggests that the likelihood of owning financial assets and maintaining a well-diversified portfolio rises with an increase in household income. The paper also underscores the role played by financial inclusion captured through bank branch penetration, especially in rural areas in influencing household savings behaviour. The results suggest that lower unemployment rate increases the likelihood of household savings across all financial asset categories.
- Access to External Finance and Efficiency Gains from Firm’s Innovation: Stochastic Frontier and Lewbel’s Approach
This paper examines the impact of access to external finance on firm’s efficiency from its innovation activities. Credit constraints can make innovations unviable due to their high sunk costs and reduce rewards from the existing innovations. The results suggest that gains from innovations in terms of technical efficiency are higher when the firm has access to external finance for its short-term working capital needs. The analysis suggests that access to external finance for working capital is associated with higher spending on skilled workers, non-manufacturing workers and training.
- Assessing Corporate Sector Health in India: A Machine Learning Approach
This paper analyses financial health of corporates using machine learning algorithms, while experimenting with varying thresholds for several established parameters, such as interest coverage ratio (ICR) and net worth. The paper also combines ICR and net worth to define a new and more stringent criteria which further enhances the predictive performance of the model. The paper underlines the superiority of the Machine Learning (ML) model over the predominantly used logistic model. The variable importance scores indicate cash flow and leverage to be the most important predictors of potential corporate stress.
