Abstract
We examine the impact of life and health insurance spending on subjective well-being. Taking advantage of insurance spending and subjective well-being data on more than 700,000 individuals in Japan, we examine whether insurance spending can buffer declines in subjective well-being due to exposure to mass disaster. We find that insurance spending can buffer drops in subjective well-being by approximately 3–6% among those who experienced the mass disaster of the great East Japan earthquake. Subjective health increases the most, followed by life satisfaction and happiness. On the other hand, insurance spending decreases the subjective well-being of those who did not experience the earthquake by approximately 3–7%. We conclude by monetizing the subjective well-being loss and calculating the extent to which insurance spending can compensate for it. The monetary value of subjective well-being buffered through insurance spending is approximately 33,128 USD for happiness, 33,287 USD for life satisfaction, and 19,597 USD for subjective health for a person in one year. Therefore, we confirm that life/health insurance serves as an ideal option for disaster adaptation. Our findings indicate the importance of considering subjective well-being, which is often neglected when assessing disaster losses.
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Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code Availability
The codes generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Notes
Earthquake insurance is bundled with fire insurance. To be precise, having fire insurance is necessary in order to have earthquake insurance. Fire insurance policyholders who do not have earthquake insurance may incorporate it at any point during the policy duration.
Separating the subscription fee and annual premium in the model may be possible and interesting from the viewpoint of behavioral economics. However, in other fields, it is common to treat all price-related variables together, as consumers are believed to be rational enough to care only about the final prices that they must pay. Therefore, we use subscription fees and annual premiums together in our empirical analysis.
Since we match the data by geographic and socioeconomic features, not at the individual level, there is no guarantee that the same individuals are counted in the two data sources. However, our merged dataset shows correlations between the first and the second datasets; for example, the first dataset's income and age levels were correlated at rates of 99.21% and 94.13%, respectively, with those in the second dataset. The reason why the correlation is not 100% is because we merge the two datasets based on the range (income and age) rather than the precise number. Therefore, there are slight differences in these variables if we look closer.
Group 1 and Group 2 are mutually exclusive.
Proportions that range from 3.86% to 7.50% show the same estimated coefficients; therefore, our results focusing on the 7.50% proportion show the upper bound of the relationship between insurance spending and SWB.
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Acknowledgements
The author would like to acknowledge the financial supports from Specially Promoted Research through a Grant-in-Aid (20H00648) from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Environment Research and Technology Development Fund (JPMEERF20201001) from the Japanese Ministry of the Environment. The Life-insurance data is provided by Dai-Ichi Life Insurance Company.
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Appendix
Appendix
Table 7 shows a distribution of the socio-economic variables of our sample and government statistics.
Because SWB variables are categorical variables, it is possible to run a regression using ordered logit analysis. Thus, we conduct an additional robustness check by running our main regression model written in Eq. (1) based on ordered logit regression. Table 8 presents our results, and we do not find qualitatively different results from the main result in Table 3; thus, we confirm that our main results in Table 3 are robust.
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Yoo, S., Kumagai, J., Kawabata, Y. et al. Insuring Well-Being: Psychological Adaptation to Disasters. EconDisCliCha 6, 471–494 (2022). https://doi.org/10.1007/s41885-022-00114-w
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DOI: https://doi.org/10.1007/s41885-022-00114-w