Socioeconomic differentials in mortality: implications on index-based longevity hedges

Lyu, Pintao; Li, Johnny Siu-Hang; Zhou, Kenneth Q.

doi:10.1080/03461238.2022.2104131

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…12 We are grateful to the Human Mortality Database and to the Office for National Statistics, respectively, for the provision of these data sets. Mortality data sorted by the IMD is frequently used to investigate and model socioeconomic mortality differences in England, compare, for example, Lyu et al (2023) or Wen et al (2023). Freimann (2021) for all technical details, including the resulting model parameters.…”

Section: Mortality Modelmentioning

confidence: 99%

On the economics of the longevity risk transfer market

Börger,

Freimann,

Ruß

2023

J of Risk & Insurance

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We present a model of a longevity risk transfer market with different market players (primary insurers, reinsurers, and capital market investors) and investigate how market dynamics and the market players' roles evolve with progressing market saturation. We find that reinsurers' appetite for longevity risk is the key driver in the early stage of market development. Since diversification benefits with other businesses decrease with every transaction, the reinsurance market is intrinsically antimonopolistic. With the increasing saturation of the reinsurance sector as a whole, its competitiveness shrinks leading to rising expected risk‐adjusted returns for capital market investors. We show that in a saturated market, reinsurers should assume the entire longevity risk from primary insurers, diversify it within their business mix, and subsequently pass on only specific (nondiversifiable) components of the longevity risk to the capital markets. Our findings provide valuable suggestions on how to make the best use of the market's limited risk absorption capacity.

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Section: Mortality Modelmentioning

confidence: 99%

On the economics of the longevity risk transfer market

Börger,

Freimann,

Ruß

2023

J of Risk & Insurance

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“…In recent years, there have been numerous studies on this relationship: Bennett et al (2015) discuss modelling life expectancies in different areas of England and Wales via a Bayesian model with spatial effects; Raleigh and Kiri (1997) study trends in life expectancy in relation to deprivation; Woods et al (2005) describe mortality in England and Wales by deprivation and in each government office region during 1998; Cairns et al (2019) identify different socio-economic groups in Denmark and model their mortality rates using an affluence index and Wen et al (2021) explore mortality rates in populations identified by deciles of the English Index of Multiple Deprivation (IMD). Deciles of the IMD index are also used by Lyu et al (2022) to identify three socio-economic groups that are then used to study the effectiveness of an indexbased longevity hedge for which the three groups are modelled with a generalised three-way Li-Lee model. Mayhew et al (2020) also use the IMD to measure deprivation and study its impact on demographic differences measured by life expectancy, lifespan variation and mortality.…”

Section: Introductionmentioning

confidence: 99%

“…Deciles of the IMD index are also used by Lyu et al . (2022) to identify three socio-economic groups that are then used to study the effectiveness of an index-based longevity hedge for which the three groups are modelled with a generalised three-way Li–Lee model. Mayhew et al .…”

Section: Introductionmentioning

confidence: 99%

Modelling socio-economic mortality at neighbourhood level

2023

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In this study, we quantify the relationship between socio-economic status and life expectancy and identify combinations of socio-economic variables that are particularly useful for explaining mortality differences between neighbourhoods in England. We achieve this by examining socio-economic variation in mortality experiences across small areas in England known as lower layer super output areas (LSOAs). We then consider 12 socio-economic variables that are known to have a strong association with mortality. We estimate the relationship between those variables and mortality rates using a random forest algorithm. Based on the resulting estimate, we then create a new socio-economic mortality index – the Longevity Index for England (LIFE). The index is constructed in a way that eliminates the impact of care homes that might artificially increase mortality rates in LSOAs with care homes compared to LSOAs that do not contain a care home. Using mortality data for different age groups, we make the index age-dependent and investigate the impact of specific socio-economic characteristics on the age-specific mortality risk. We compare the explanatory power of the LIFE index to the English Index of Multiple Deprivation (IMD) as predictors of mortality. While we find that the IMD can explain regional mortality differences to some extent, the LIFE index has significantly greater explanatory power for mortality differences between regions. Our empirical results also indicate that income deprivation amongst the elderly and employment deprivation are the most significant socio-economic factors for explaining mortality variation across LSOAs in England.

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