Financial sector development and life insurance inclusion in India: an ARDL bounds testing approach

Abstract

PurposeThis paper analyses the relationship between financial sector development (FSD) and life insurance inclusion in India during the period from 1971–1972 to 2016–2017. The study analyses the effect of financial deepening on life insurance inclusion in India.Design/methodology/approachThe study employs augmented Dickey–Fuller (ADF) unit roots test to check the stationarity properties of the time series data. It estimates a life insurance inclusion model using the auto-regressive distributed lag model (ARDL) bounds testing approach to cointegration.FindingsThe study finds evidence of a cointegrating relationship between financial deepening and life insurance inclusion in India. A significant error correction coefficient indicates automatic adjustments to short-run disequilibrium, reinforcing the cointegrating relationship between financial sector and life insurance inclusion.Research limitations/implicationsA major limitation of the study is that it excludes the first-time sum assured (FSA) contributed by the private sector life insurance companies due to lack of data availability.Practical implicationsThe results of the study call for faster expansion of the financial sector and provision of a low interest rate regime in the Indian economy. The study invokes the need for sufficient training to the personnel in the banking and non-banking institutions to cater to the complex needs of life insurance buyers.Originality/valueThe paper estimates the link between FSD and life insurance inclusion and introduces a new measure of life insurance demand, the life insurance inclusion, measured using the FSA.

What Drives the Development of Life Insurance Sector? Empirical Evidence from Tanzania

“…Findings of the study according to table 3 indicate that income level has a negative statistically significant influence on life insurance development (-0.30, P-value<0.05) hence the null hypothesis is rejected. The study's findings are consistent with the findings of Mathew and Sivaraman (2021), who confirm that income is negatively related to life insurance consumption, and Bhatia and Jain (2013), who confirm that income level measured by per capita GDP negatively affects the growth of insurance penetration. In line with this study, Alhassan and Biekpe (2016) and Kabrt (2021) and Segodi & Athenia (2022) confirmed that income led to a decline in life insurance development.…”

“…Milijana et al (2017) confirm that GDP has a positive influence while interest rates have a negative influence on life insurance development. Furthermore, Mathew and Sivaraman (2021) confirmed that interest and income are negatively related to life insurance development. In light of these contradictory findings from previous empirical studies, the following null hypotheses were developed and tested by the study: H1: There is no statistically significant relationship between interest rates and life insurance sector development in Tanzania H2: There is no statistically significant relationship between income level and life insurance sector development in Tanzania H3: There is no statistically significant relationship between domestic saving and life insurance sector development in Tanzania H4: There is no statistically significant relationship between financial depth and life insurance sector development in Tanzania H5: There is no statistically significant relationship between inflation rate and life insurance sector development in Tanzania…”