Data granularity in mid-year life table construction

Abstract

[EN] Life tables have a substantial influence on both public pension systems and life insurance policies. National statistical agencies construct life tables from death rate estimates (𝑚���𝑥���), or death probabilities (𝑞���𝑥��� ), after applying various hypotheses to the aggregated figures of demographic events (deaths, migrations and births). The use of big data has become extensive across many disciplines, including population statistics. We take advantage of this fact to create new (more unrestricted) mortality estimators within the family of period-based estimators, in particular, when the exposed-to-risk population is computed through mid-year population estimates. We use actual data of the Spanish population to explore, by exploiting the detailed microdata of births, deaths and migrations (in total, more than 186 million demographic events), the effects that different assumptions have on calculating death probabilities. We also analyse their impact on a sample of insurance product. Our results reveal the need to include granular data, including the exact birthdate of each person, when computing period midyear life tables.