Прогнозирование смертности в зависимости от характеристик социально-экономического положения индивида (статистический анализ смертности по данным Российского мониторинга экономики и здоровья)
Moscow is the region with the highest life expectancy in Russia. The country’s largest city, it has high incomes, a special population structure and a high concentration of all resources, including in the healthcare sector, which is given special attention by the city authorities. In some periods, the changes in life expectancy in Moscow have been unique compared to most other regions of Russia. The difference in life expectancy between Moscow and Russia in the period from the mid-1990s to the mid-2000s was mainly due to lower mortality in middle age. Since the mid-2000s, the main contribution to the difference in life expectancy has been shifting to old age mortality. Given the overall rapid decline of mortality in Moscow since then, changes in the mortality rates and life expectancy of certain age groups seem implausible. The quality of population and mortality data has a significant impact on the accuracy of estimates of mortality indicators and requires special attention in the case of Moscow. In particular, the number of people at advanced ages in Moscow is likely to be overestimated, which affects mortality rates in this age group. Peculiarities of mortality by causes of death in Moscow generally correspond to the average Russian trends; however, in Moscow a more rapid decrease in mortality from neoplasms is observed, as well as more realistic age-specific death rates in older age groups.
Insurance companies and pension funds are affected by many different kinds of risks. In life insurance there are two main risks: the demographic risk and the investment risk. The demographic risk can be dividing into insurance risk and longevity risk. The first risk associated with the random deviation of the number of deaths from its expected value, the second deriving from the improvement in mortality rates. Numbers of actuarial stochastic models have been developed to analyse the mortality changes. This work focuses on Lee-Carter, Cairns-Blake-Dowd models and their extended versions with the inclusion of the cohort effect. We construct 6 stochastic actuarial models on Russian data at the first time. For modelling we use age-specific mortality rates and the probability of dying between 1959 and 2014 for the population aged 20 to 88 years from the Human Mortality Database. We consider age range from 20 to 88. Using the "StMoMo" package in the R software environment, code was written for modelling and predicting mortality with the help of actuarial stochastic models. For comparison of models, information criteria (Bayesian information criterion and Akaike criterion) were used, as well as sensitivity to changing the time range.
The paper examines the role of migration in Russia in achieving the government's strategic goals of population growth and ensuring natural growth by 2024. For the migration forecasting, cohort-component method and the algorithms of replacement migration are used. As a result, annual migration growth of 300-304 thousand people is required to maintain the current population size within next five years. Annual migration growth of 6.0-8.9 million people is needed to ensure natural growth. The last means that the goal will not be fulfilled.