How We Fall Apart: Similarities of Human Aging in 10 European Countries

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may January 2017Abstract. We analyze human aging, understood as health deficit accumulation, for a panel of European individuals. For that purpose, we use four waves of the Survey of Health, Aging and Retirement in Europe (SHARE dataset) and construct a health deficit index. Results from log-linear regressions suggest that, on average, elderly European men and women develop about 2.5 percent more health deficits from one birthday to the next. In non-linear regression (akin to the Gompertz-Makeham model), however, we find much greater rates of aging and large differences between men and women as well as between countries. Interestingly, these differences follow a particular regularity (akin to the compensation effect of mortality). They suggest an age at which average health deficits converge for men and women and across countries.

How Season of Birth Affects Health and Aging

“…We dropped Southern Europe because the seasons' effects are again insignificant. As shown in Table 5, the Makeham term (A) is significantly positive and the point estimate of about 8% is of the same size as in our earlier study (Abeliansky and Strulik, 2018a). Men start out healthier than women (lower estimate of R for men than for women) but age faster (higher estimate of α for men than for women).…”

“…As shown in Abeliansky and Strulik (2018a), attrition by death does not alter the estimated age coefficients. Since the Mundlak estimator models the correlation of the unobserved heterogeneity assuming that the mean at the individual level of the explanatory variables is correlated with the factors that are unobserved (Wooldridge, 2010, Ch.…”

“…We estimate (3) separately for men and women. The non-linear form, akin to the Gompertz-Makeham law of mortality, has been suggested by Mitnitski et al (2002) and used in our study on convergence of aging in Europe (Abeliansky and Strulik, 2018a Regression results for all countries and the Northern European sample are shown in Table 5. We dropped Southern Europe because the seasons' effects are again insignificant.…”

Hungry Children Age Faster

“…In Appendix The force of aging is estimated as 0.035 for females and 0.028 for males, suggesting that women age faster than men. As shown in Abeliansky and Strulik (2017), attrition by death does not alter the estimated age coefficients. The outcome of faster aging of women conflicts with findings from previous studies (Mitnitski et al, 2002;Abeliansky and Strulik, 2017).…”

“…Mitnitski and Rockwood, 2016), which seems to be a more appropriate term when the investigated population consists, to a significant degree, of non-frail persons. A handful of studies have investigated the health deficit index (frailty index) using the SHARE data (Romero-Ortuno and Kenny, 2012;Harttgen et al, 2013, Theou et al, 2013Romero-Ortuno, 2014;Abeliansky and Strulik, 2017). 3 Only five waves of the SHARE have information on health related variables (1-2 and 4-6) but we use six waves in total since we use the information on the hunger episodes from wave 3. then regress the health deficits of individuals at a given age on age and the hunger indicator.…”