Life Histories: Real and Synthetic

Frans Willekens, Max Planck Institute for Demographic Research

Life history data are generally incomplete. Respondents enter observation late (left truncation) or leave early (right censoring). In life history analysis, these limitations are considered in the estimation of transition rates. By combining data from different but similar individuals and a continuous-time Markov model, life histories can be modeled. The life history that results is a synthetic life history. Life histories are generated from transition rates using microsimulation in continuous time. Several indicators may be derived to characterize life histories. The methods are illustrated using data from the US Health and Retirement Survey. Extensive use is made of Biograph, a new R package in the CRAN library. The book documenting the package and related packages for life history analysis was recently published in Springer's Use R! Series (Multistate analysis of life histories with R, Springer, 2014)