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The impact of climate change on life insurance premiums: a panel data analysis of EU countries

Aluno: Mariana IrinÉo Godinho


Resumo
This work examines the relationship between the life insurance market and climate change using four distinct regression models with data from the 27 countries of the European Union (EU), including the United Kingdom (UK), for the period from 2011 until 2019. Two of the models uniquely study the impact of greenhouse gas emissions on life insurance premiums, resorting to distinct panel data models: the pooling model, and the fixed effects model. The other two models feature other climate change-related variables, including the number of fatalities, the deviation of temperature with respect to a baseline climatology, corresponding to the period 1951–1980, and the Gross Domestic Product (GDP), also using the pooling and the fixed effect models. As the name suggests, the pooling model implies the use of a panel data set of all 28 countries together, whereas the fixed effects model requires some type of aggregation. Therefore, for the second panel data set, the countries are grouped into three clusters accounting for historical trends and future climate change projections used in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). The goal is to analyse whether the use of the fixed effects model provides a better tool to understand the variation of life insurance premiums provoked by climate change when compared to the pooling model. The results of the first two models have shown that an increase of one thousand tonnes in greenhouse gas emissions is estimated to increase the total life insurance premiums by, respectively, 0.2099 and 0.2243 million US dollars. However, when the other variables are included in the first two models, these values change to −0.2681 and −0.2660 million US dollars, respectively. Meanwhile, the explicative power of the life insurance premiums variance increases significantly from the first two to the other two models, which may be a positive indicator.


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