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Demography

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01.12.2014

Heterogeneity in Expected Longevities

verfasst von: Josep Pijoan-Mas, José-Víctor Ríos-Rull

Erschienen in: Demography | Ausgabe 6/2014

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Abstract

We develop a new methodology to compute differences in the expected longevity of individuals of a given cohort who are in different socioeconomic groups at a certain age. We address the two main problems associated with the standard use of life expectancy: (1) that people’s socioeconomic characteristics change, and (2) that mortality has decreased over time. Our methodology uncovers substantial heterogeneity in expected longevities, yet much less heterogeneity than what arises from the naive application of life expectancy formulae. We decompose the longevity differences into differences in health at age 50, differences in the evolution of health with age, and differences in mortality conditional on health. Remarkably, education, wealth, and income are health-protecting but have very little impact on two-year mortality rates conditional on health. Married people and nonsmokers, however, benefit directly in their immediate mortality. Finally, we document an increasing time trend of the socioeconomic gradient of longevity in the period 1992–2008, and we predict an increase in the socioeconomic gradient of mortality rates for the coming years.

Vorheriger Artikel Long-Term Trends in Adult Mortality for U.S. Blacks and Whites: An Examination of Period- and Cohort-Based Changes

Nächster Artikel Direct and Indirect Effects of Unilateral Divorce Law on Marital Stability

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See, for instance, Montez et al. (2011) and references therein for recent findings of mortality differences by education level. Deaton and Paxson (1994) documented the negative relationship between mortality and family income, after controlling for education. Attanasio and Hoynes (2000) found a negative relationship between mortality and wealth. The Whitehall studies have uncovered important mortality differences according to the employment grade among British civil servants; see, for instance, Marmot et al. (1984, 1991). For mortality rates and marital status, see Hu and Goldman (1990) and the references therein.

The standard errors are obtained by drawing 25,000 samples of parameter values from the estimated asymptotic distribution of the model parameters and computing a life expectancy with each of them. See Appendix B for details.

The life expectancies we compute in the HRS should be somewhat larger than the ones reported by the NVSS because the HRS refers to the noninstitutionalized population. For instance, Brown et al. (2012) find that life expectancies at 65 in the HRS are about one year larger than in the NVSS.

See Appendix A for the exact definition of all these variables.

To point to particular examples, only 47 % of white males with wealth in the top quintile of the distribution at age 50 were in the same quintile by age 65 (with most of the movers going to the second quintile), and 88 % of white males who were married at age 50 were also married by age 65. Even more important are the changes in labor market status, because people clearly drop from the labor force as they age.

Allowing for interactions between age, type, and year would increase the parameterization of our logit and multilogit models beyond tractability. In addition, the rationale for interacting time effects with age comes from the evidence that long-run gains in survival rates are different at different ages. However, these findings relate to both age differences and time intervals much wider than ours. See Lee and Carter (1992) for details.

Despite the fact that the standard error associated to the change in female life expectancy is large (0.6 years), this discrepancy between the HRS and the NVSS is worrisome. In a sense, we are stretching the HRS to its limits. As shown in Fig. 1, information on deaths for old individuals contain limited time variation. For instance, individuals aged 85 and older come only from the original AHEAD cohort. This problem is more acute for women, who die an average of four years later.

Preston and Elo (1995) showed that the education gradient of mortality rates computed with the National Longitudinal Mortality Study (NLMS) between 1979 and 1985 is larger than the one obtained by Kitagawa and Hauser (1973) with the death certificates and census data of 1960.

The results for the 1980s and 1990s are based on data from the NLMS, whereas the comparison between 1990 and 2000 is based on data from the death certificates in the Multiple Cause of Death files.

Others include the Medical Expenditure Survey (MEPS), the National Health Interview Survey (NHIS), the National Health and Nutrition Examination Survey (NHANES), the National Longitudinal Study of Youth (NLSY), the Survey of Health Ageing and Retirement in Europe (SHARE), and the Panel Study of Income Dynamics (PSID).

Some authors choose to estimate the health and survival functions together through an ordered logit, thinking of death as an extra (and absorbing) health state (see, e.g., Yogo 2009). Our specification has two advantages. First, it is designed to estimate not only the effects of the type variables z into health but also the evolution of the type variables z and how this is affected by health itself. Second, it imposes less structure than an ordered logit model by allowing the marginal effect of any variable z on future health to differ from its marginal effect on mortality. This distinction is important. For instance, the effect of education on mortality is null after health is controlled for, but it is still an important determinant of the law of motion of health (see Appendix B). The decompositions in the next section are based precisely on this distinction.

See Lancaster (1990) for an overview of duration analysis.

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Brown, D. C., Hayward, M. D., Montez, J. K., Hummer, R. A., Chiu, C.-T., & Hidajat, M. M. (2012). The significance of education for mortality compression in the United States. Demography, 49, 819–840.CrossRef

Brown, J. R. (2002). Differential mortality and the value of individual account retirement annuities. In M. Feldstein & J. B. Liebman (Eds.), The distributional aspects of social security and social security reform (pp. 401–446). Chicago, IL: University of Chicago Press.CrossRef

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Deaton, A., & Paxson, C. (1994). Mortality, education, income and inequality among American cohorts. In D. A. Wise (Ed.), Themes in the economics of aging (pp. 129–170). Chicago, IL: University of Chicago Press.

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Titel: Heterogeneity in Expected Longevities
verfasst von: Josep Pijoan-Mas
José-Víctor Ríos-Rull
Publikationsdatum: 01.12.2014
Verlag: Springer US
Erschienen in: Demography / Ausgabe 6/2014
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-014-0346-1

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