NEW METHODS FOR ANALYZING ACTIVE LIFE EXPECTANCY

The increment-decrement life-table methods used in several recent anal yses of active life expectancy depend on parameters representing rates pf movement between functional states such as ''active'' or ''disable d.'' Available data often pose severe problems for the derivation of t hese parameters. For example, panel-survey data typically fail to reco rd functional status between interviews. The time intervals between in terviews also tend to vary across respondents, often substantially. Th e Longitudinal Study of Aging, used in this research, exhibits these p roblems. The authors develop a discrete-time Markov chain model of fun ctional status dynamics that accommodates these features of the data a nd present maximum-likelihood estimates of the model. Also introduced is a new technique for the calculation of active life expectancy: micr osimulation of functional status histories. The microsimulation techni que permits the derivation of several new indexes of late life-course outcomes.