ABSTRACT This study aimed to set upper and lower bounds for the expected present value of whole life annuities and whole life insurance policies from incomplete mortality data, generalizing previous results on life expectancy. Since its inception, in the 17th century, actuarial science has been devoted to the study of annuities and insurance plans. Thus, setting intervals that provide an initial idea about the cost of these products using incomplete mortality data represents a theoretical contribution to the area and this may have major applications in markets lacking historical records or those having little reliability of mortality data, as well as in new markets still poorly explored. For both the continuous and discrete cases, upper and lower bounds were constructed for the expected present value of whole life annuities and whole life insurance policies, contracted by a person currently aged x, based on information about the expected present value of these respective financial products subscribed to by a person of age x + n and the probability that an individual of age x survives to at least age x + n. Through the bounds of a continuous annuity, in an environment where the instantaneous interest rate is equal to zero, the results shown also set bounds for the complete life expectancy, which implies that the contribution of this research generalizes previous results in the literature. It was also found that, for both annuities and insurance plans, the length of constructed intervals increases as the data gap size increases and it decreases as the survival curve becomes more rectangular. Illustratively, bounds for life expectancy at 40 and 60 years of age, for the 10 municipalities showing the highest life expectancy at birth in Brazil in 2010, were constructed by using data available in the Atlas of Human Development in Brazil.

Mortality and morbidity experience data from 2010 through 2013 on group insurance policies

Mortality experience data from 2009 through 2013 on individual payout annuities

Mortality experience data from 2009 through 2015 on fully underwritten individual life insurance policies

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Period life expectancy by age and sex for the UK. Each national life table is based on population estimates, births and deaths for a period of three consecutive years. Tables are published annually.

Life expectancy (life years) free of stroke, affected by stroke, and total life expectancy at age 50 by sex, period, and income group.

Mortality experience data from 2009 through 2013 on structured settlements

Post Level Term mortality and lapse experience data from 2000 through 2012 on fully underwritten individual life insurance policies

ABSTRACT Life tables have been elaborated throughout much of human history. However, the first life table to use actuarial concepts was only constructed in 1815 by Milne for the city of Carlisle in England. Since then, numerous tables have been elaborated for different regions and countries, due to their crucial importance for analyzing various types of problems covering a vast range of possibilities, from actuarial studies to forecasting and evaluating demands in order to define public policies. The most common problem nowadays in an actuarial calculation is choosing a suitable table for a given population. Brazil has few specific tables for the pensions market and has been using imported tables that refer to other countries, with different cultures and different mortality experiences. Using data from the Integrated Human Resource Administration System, this table constructs life tables for Executive branch federal civil servants for the period from 1993 to 2014, disaggregated for sex, age, and educational level (high school and university). The international literature has recognized differences in mortality due to sex, socioeconomic differences, and occupation. The creation of the Complementary Pension Foundation for Federal Public Servants in 2013 requires specific mortality tables for this population to support actuarial studies, healthcare, and personnel policies. A mathematical equation is fitted to the data. This equation can be broken down into infant mortality (not present in the data), mortality from external causes, and mortality from senescence. Recent results acknowledging an upper limit for old age mortality are incorporated into the adjusted probabilities of death. Assuming a binomial distribution for deaths, the deviance was used as a figure of merit to evaluate the goodness of fit of the observed data both to a set of tables used by the insurance/pensions market and to the adjusted tables.

The data and programs replicate tables and figures from "Combining Life and Health Insurance", by Koijen and Van Nieuwerburgh. Please see the Readme file for additional details.

Indonesia Life Insurance: Mortality Expenses data was reported at 55,717.284 IDR mn in Feb 2025. This records an increase from the previous number of 29,536.019 IDR mn for Jan 2025. Indonesia Life Insurance: Mortality Expenses data is updated monthly, averaging 367,190.454 IDR mn from Aug 2017 (Median) to Feb 2025, with 91 observations. The data reached an all-time high of 13,921,729.518 IDR mn in Dec 2017 and a record low of -60,520.942 IDR mn in Jan 2022. Indonesia Life Insurance: Mortality Expenses data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGF002: Insurance Statistics: Life Insurance: Income Statement.

Historical and emerging trends in U.S. population mortality by cause of death

Description of the functions in qlifetable for building quarterly life tables.

Descriptive statistics of the number of insured individuals, exposures in person-years, and number of events by income group, sex and period.

Risks (HR) of stroke incidence, death without stroke, and death after stroke incidence of the higher income group compared to the lower income group by sex.

Life Insurance: Mortality Expenses在2025-02达55,717.284IDR mn，相较于2025-01的29,536.019IDR mn有所增长。Life Insurance: Mortality Expenses数据按月度更新，2017-08至2025-02期间平均值为367,190.454IDR mn，共91份观测结果。该数据的历史最高值出现于2017-12，达13,921,729.518IDR mn，而历史最低值则出现于2022-01，为-60,520.942IDR mn。CEIC提供的Life Insurance: Mortality Expenses数据处于定期更新的状态，数据来源于Indonesia Financial Services Authority，数据归类于Indonesia Premium Database的Insurance Sector – Table ID.RGF002: Insurance Statistics: Life Insurance: Income Statement。

Description of the functions in qlifetable for dealing with microdata.

Examples of exact ages at events as a function of the length of the year utilised to calculate them when births and events happen at exactly the same moment in two different time years.