Trends in Mortality Decrease and Economic Growth

et al. 2018

Socioeconomic status is commonly conceptualized as the social standing or well‐being of an individual or society. Higher socioeconomic status has long been identified as a contributing factor for mortality improvement. This paper studies the impact of macroeconomic fluctuations (having gross domestic product (GDP) as a proxy) on mortality for the nine most populous eurozone countries. Based on the statistical analysis between the time‐dependent indicator of the Lee and Carter (Journal of the American Statistical Association, 1992, 87(419), 659–671) model and GDP, and adaptation of the good features of the O'Hare and Li (Insurance: Mathematics and Economics, 2012, 50, 12–25) model, a new mortality model including this additional economic‐related factor is proposed. Results for male and female from ages between 0 and 89, and similar for unisex data, are provided. This new model shows a better fitting and more plausible forecast among a significant number of eurozone countries. An in‐depth analysis of our findings is provided to give a better understanding of the relationship between mortality and GDP fluctuations.

Section: Extrapolative Mortality Modelingmentioning

confidence: 99%

“…In the next section we follow the idea presented by Niu and Melenberg (2014) and examine the relationship between GDP and mortality rates for those nine populous eurozone countries.…”

Section: Economic Growth and Mortality Ratesmentioning

confidence: 99%

Section: Trends In the Lc Mortality Index And Gdp Fluctuationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mortality effects of economic fluctuations in selected eurozone countries

Seklecka¹,

Lazam

et al. 2018

Mortality effects of temperature changes in the United Kingdom

Seklecka

O’Hare

2017

Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter (Journal of the American Statistical Association, 1992, 87: 659–671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature‐related factor. The new model is shown to provide a significantly better‐fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed.

The impact of parameter uncertainty in insurance pricing and reserve with the temperature‐related mortality model

Seklecka

O’Hare

2018

Changes in mortality rates have an impact on the life insurance industry, the financial sector (as a significant proportion of the financial markets is driven by pension funds), governmental agencies, and decision makers and policymakers. Thus the pricing of financial, pension and insurance products that are contingent upon survival or death and which is related to the accuracy of central mortality rates is of key importance. Recently, a temperature‐related mortality (TRM) model was proposed by Seklecka et al. (Journal of Forecasting, 2017, 36(7), 824–841), and it has shown evidence of outperformance compared with the Lee and Carter (Journal of the American Statistical Association, 1992, 87, 659–671) model and several others of its extensions, when mortality‐experience data from the UK are used. There is a need for awareness, when fitting the TRM model, of model risk when assessing longevity‐related liabilities, especially when pricing long‐term annuities and pensions. In this paper, the impact of uncertainty on the various parameters involved in the model is examined. We demonstrate a number of ways to quantify model risk in the estimation of the temperature‐related parameters, the choice of the forecasting methodology, the structures of actuarial products chosen (e.g., annuity, endowment and life insurance), and the actuarial reserve. Finally, several tables and figures illustrate the main findings of this paper.