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Executive Summary
Life insurance demand is a complex phenomenon that can be measured in multiple ways. Understanding it in the context of various potential drivers is of immense interest to insurance companies, insurance markets, regulators, and broader society. Unfortunately, to our knowledge, not much work has been done to address this topic in the current academic literature. That is why in this work, we perform a spatial regression analysis using multi-scale geographically weighted regression (MGWR) approach. As response variables, we consider annual permanent life insurance premiums and annual term life insurance premiums for 2020, which total $12,840,615,055, as proxies for insurance demand in the United States. The covariates considered were broadly classified into two groups: social capital and population composition. Because the COVID-19 pandemic emerged in 2020, results of this study may or may not represent a typical year. Identifying the impact of COVID-19 on results is beyond the scope of this study.
Our findings show that among those covariates found to be statistically significant, not all were relevant on the same spatial scales. Some were globally relevant, meaning they exhibit a relatively equal association with insurance demand across the entire country as a whole, other covariates are regionally relevant, with effects that are realized in certain broad areas of the country, and other covariates operate locally, with effects that specific to small amounts of counties. The spatial scale on which covariates were relevant also depends on whether permanent or term life insurance is considered.