Long-run relationship between insurance premiums and driving factors in Mongolia

[Purpose] The insurance industry in Mongolia is indeed expanding, though it has received comparatively less attention than other sectors within the financial industry. However, it is crucial to understand the factors that reflect and influence long-run development trends of the insurance industry, in order to actively work towards its growth and development in the future. This study examines the variables in terms of their long-run effects on the insurance industry in Mongolia. [Methodology] Data are sourced from the World Bank’s databases. Spanning the period from 1995 to 2022, with a total of 28 observations. Johansen Cointegration rank tests, estimation of cointegrating vector, and testing of hypotheses on long-run coefficients are used to investigate the long-run relationship between insurance premiums and variables that drive them. [Findings] Our results show that price level, internet usage, and household income are the long-run variables influencing the total amount of insurance premiums in Mongolia. The long-run coefficients of these variables determine the long-run elasticities of insurance premiums. The long-run coefficient of household income is the largest and then household income is the primary factor of insurance premiums. Also, life expectancy is excluded from the cointegrating relation because zero restriction on its coefficient cannot be rejected. The role of life expectancy is ambiguous and is needed for further investigation. [Practical Implications] Based on the main results, it is imperative to focus on long-run factors influencing the aggregate insurance premium income in Mongolia. Consequently, our findings offer valuable perceptions for the insurance sector, shedding light on their influence on the development of the industry. The development of the insurance industry in Mongolia is full of challenges and uncertainties. Hence, our study is related to the practice of decision sciences, providing insights into decision-making processes in optimal ways under conditions of uncertainty faced by government officials and practitioners.