Demography, Vol. 31, No.2, May 1994
Estimating Increment-Decrement Life Tables
with Multiple Covariates from Panel Data:
The Case of Active Life Expectancy*
Kenneth C. Land
Department of Sociology and
Center for Demographic Studies and
Center for Aging and Human Development
Duke University
Durham, NC 27708-0088
Jack M. Guralnik
Epidemiology, Demography, and Biometry Program
National Institute on Aging
7201 Wisconsin Avenue
Bethesda, MD 20892
Dan G. Blazer
Office of Medical Education
Box 3005
Duke University Medical Center
Durham, NC 27710
A fundamental limitation of current multistate life table methodology-evident in
recent estimates of active life expectancy for the elderly-is the inability to estimate
tables from data on small longitudinal panels in the presence of multiple covariates
(such as sex, race, and socioeconomic status). This paper presents an approach to
such an estimation based on an isomorphism between the structure of the stochastic
model underlying a conventional specification of the increment-decrement life table
and that of Markov panel regression models for simple state spaces. We argue that
Markov panel regression procedures can be used to provide smoothed or graduated
group-specific estimates of transition probabilities that are more stable across short
age intervals than those computed directly from sample data. We then join these
estimates with increment-decrement life table methods to compute group-specific
total, active, and dependent life expectancy estimates. To illustrate the methods, we
describe an empirical application to the estimation of such life expectancies specific to
sex, race, and education (years of school completed) for a longitudinal panel of
elderly persons. We find that education extends both total life expectancy and active
life expectancy. Education thus may serve as a powerful social protective mechanism
delaying the onset of health problems at older ages.
During the past three decades, much attention has focused on the development of
health status measures that combine mortality and disability data. In particular, building on
* Data analyzed in this study are from the Duke Established Populations for Epidemiologic Studies of the
Elderly, which were established pursuant to NIA Contract N02-AG-I-2102. The content of this publication does
not necessarily reflect the views or policies of the U.S. Department of Health and Human Services. nor does
Copyright © 1994 Population Association of America
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