Belloni, Michele

Chiara Dal Bianco,

Danilo, Cavapozzi

Serena, Trucchi

Yao, Pan

English

Institutional Research Information System University of Turin

Michele Belloni, Danilo Cavapozzi, Chiara Dal Bianco,Yao Pan and Serena Trucchi9 How do early-life conditions shapehealth age profiles late in life?► We investigate how health dynamics late in life vary with early-life conditions► Better early-life conditions are associated with better health outcomes► Education as well as current income and wealth are important mediating factors of thisrelationship9.1 Health profiles and early-life conditionsThe change in the demographic structure of the population poses concernsover the sustainability of public healthcare programmes because the increasein the number of older people is likely to increase the demand for care services.Understanding how health patterns evolve with age and whether their trendsdiffer by the socioeconomic conditions experienced during the lifecycle mighthelp predict how demand for care services will evolve.A growing body of economic literature has focused on the long-run conse-quences of the early stage of the lifecycle and investigated the effect of the so-cioeconomic status of individuals during their childhoods on several adultoutcomes, such as earnings (Brunello et al., 2017), cognitive and non-cognitiveskills (Cunha et al., 2010) and health conditions (Mazzonna, 2014). These stud-ies point out the significant role played by socioeconomic conditions duringchildhood on human capital accumulation for children and, thus, on adultsocioeconomic outcomes. This chapter contributes to the literature by examin-ing the impact of early-life conditions on not only the health status but also itsdynamics in later stages of the lifecycle.We describe early-life conditions using a multidimensional index based ona battery of indicators designed to describe the socioeconomic context in whichrespondents grew up. Building on the SHARE and SHARELIFE questionnaires,we focus on the socioeconomic background of respondents at age 10. Our mul-tidimensional indicator for early-life conditions considers whether parents werealive, their education, the number of books at home, the number of rooms percapita and other relevant accommodation characteristics. For respondents’Open Access. ©2019 Michele Belloni, et al., published by De Gruyter. This work is licensedunder a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.https://doi.org/10.1515/9783110617245-009 Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMcurrent health status, we use alternative measures. We focus on overall healthself-assessment, an objective indicator of physical health, as well as on theEURO-D scale score for mental health.Further, we investigate the extent to which the long-run influence ofearly-life conditions on the health of older individuals is indirect, in otherwords, the extent to which it is mediated by the current individual and house-hold characteristics (see, e.g. Bengtsson and Brostrӧm, 2009). Empirically, weassess whether the characteristics of socioeconomic status during childhoodremain significantly associated with health later in life once we condition oneducation and current socioeconomic status indicators (e.g., household composi-tion, household income and wealth measures). Analysing these patterns providesevidence of the channels according to which childhood conditions can shapehealth later in life and can inform policymakers about the intergenerational con-sequences of public policies designed to improve the social and economic inclu-sion of individuals.We find that socioeconomic status in childhood is positively correlatedwith health in later life. This association holds for both physical and mentalhealth and is stronger for females than for males. In most cases, it remains sta-ble over the age range we consider. We find evidence that this effect is mostlyindirect; that is, it is mediated by socioeconomic status in adulthood. This evi-dence suggests that an individual who grows up in bad conditions is penalizedin the areas of education, income and wealth over their lifecycle, which leadsto worse health in old age. However, a small direct effect remains, suggestingthat childhood circumstances partly act as an indelible imprint on individuals’health.9.2 Data and variablesThe sample includes respondents aged between 50 and 80 years, living in coun-tries that participated in all SHARE waves, namely, Austria, Belgium, Denmark,Germany, France, Italy, Spain, Sweden and Switzerland. After excluding re-spondents with missing information on relevant variables, we end up with85,519 observations referring to 15,502 women and 12,643 men.As anticipated in the previous section, we focus on three different out-come variables that measure current physical and mental health. We drawcurrent health indicators from Waves 1, 2, 4, 5 and 6. The first health indicatorconsidered is the self-assessed overall health status, which takes the values1 (poor) to 5 (excellent). To provide an objective assessment of an individual’s92 Michele Belloni et al.Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMphysical health status, we construct an indicator combining information onActivities of Daily Living (ADLs), Instrumental Activities of Daily Living(IADLs), mobility limitations and chronic diseases (we consider only thechronic condition items included in all waves). Our objective physical healthindicator is the weighted sum of individual outcomes with respect to thesehealth indicators. Weights are defined by running a principal componentanalysis. We assigned to each indicator a weight derived from the corre-sponding item in the first principal component. The objective physical healthindicator is standardized to lie between 0 (worst physical health) and 1 (bestphysical health). We also consider mental health status, which is measuredby the EURO-D indicator. The EURO-D indicator counts the number of mentaldiseases suffered by individuals. For comparability with the other healthmeasures considered, we reversed its scale and standardized its range to be-tween 0 and 1, where 0 indicates worst mental health and 1 indicates bestmental health.To investigate the impact of early-life conditions on health dynamics laterin life, we exploit a unique characteristic of the SHARE dataset. In SHARELIFE(Wave 3 and Wave 7) and SHARE (Wave 5 and Wave 6) information is collectedon family background during childhood, along with several well-establishedmeasures.We specifically exploit the following information: the number of books athome at age 10 (we create a variable equal to 1 for those with more than 10books and 0 otherwise), the number of rooms per-capita (we create a variableequal to 1 if the number equals or is higher than the country median and0 otherwise), plus some features of the family home at age 10 (whetherequipped with central heating, an inside toilet, a fixed bath, cold runningwater and hot running water, each treated as a separate dummy variable).We also use information on whether both parents were alive at age 10 andtheir education attainment (a dummy equal to 1 if at least one parent washighly educated, that is, an education level classified with an ISCED codeequal to 3 or higher).Our early-life conditions index is defined as the weighted sum of individualoutcomes with respect to this battery of binary indicators, where the weightsare defined by a principal component analysis. The early-life condition indica-tor takes values between 0 (worst conditions) and 1 (best conditions). The coun-try median of the index is lowest in southern Europe (0.3 in Italy and Spain)and highest in Sweden, where the median is approximately 0.9. This evidenceparallels the evidence by Mazzonna (2014) that shows cross-country heteroge-neity in rooms per capita, accommodation facilities and books at home basedon SHARELIFE.9 How do early-life conditions shape health age profiles late in life? 93Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AM9.3 Methods and resultsTo examine the health dynamics of individuals later in life, we estimate the ageprofile of our three health indicators. Each current health measure is regressedon our early-life condition indicator, a second-order polynomial of age and itsinteractions with the early-life condition indicator. Interaction terms play a cru-cial role in our identification strategy because they allow the role of early-lifeconditions to vary with age. We consider two alternative sets of control varia-bles. The first set of controls (‘basic set of controls’) includes a full set of coun-try dummies and birth-cohort dummies. In the second set of controls (‘full setof controls’), the set of covariates is augmented to reflect respondents’ socioeco-nomic status in adulthood. More specifically, we add educational attainmentdummies (ISCED levels 0–2, 3–4 and 5–6), country-specific quartile dummiesfor household income and wealth and family current characteristics (householdsize, number of children and grandchildren). Each regression is run separatelyby gender. Standard errors are clustered at the individual level to account forthe longitudinal dimension of the dataset. Different degrees of the age polyno-mial lead to unchanged findings.50 60 70 80AgeMen, basic set of controls50 60 70 80AgeMen, full set of controls50 60 70 80AgeWomen, basic set of controls50 60 70 80AgeWomen, full set of controls1.522.533.51.522.533.51.522.533.51.522.533.5Most favourable early-life conditions Least favourable early-life conditionsFigure 9.1: Health age profiles: Self-assessed overall health indicator.Note: Solid lines indicate 95 per cent confidence intervals.Source: SHARE Wave 1–6 release 6.1.0, Wave 7 release 0.94 Michele Belloni et al.Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMBased on the results from these regressions and holding the control varia-bles fixed, we computed the age profile of each health outcome for individualswith the least and the most favourable early-life conditions (which implies set-ting our early-life condition indicator alternatively equal to 0 and 1). The pre-dictions are plotted in Figures 9.1–9.3, which show the predicted (mean and95% confidence interval) health-age profiles for respondents with the most(grey dots and lines) or least favourable (orange dots and lines) early-life condi-tions. Each figure reports results for a specific health outcome by gender andby set of control variables used.Figure 9.1 illustrates the dynamics of self-assessed overall health. As ex-pected, health declines after the age of 50 for both men (upper panels) andwomen (lower panels). Observing the most parsimonious specification (leftgraphs in Figures 9.1), the health status of individuals who experienced theworst early-life conditions is on average significantly lower with respect to re-spondents who grew up in the best socioeconomic environments. This patternis confirmed throughout the age range considered. However, the distance be-tween the groups shrinks when we allow for the full set of controls, which in-cludes the level of education and the position in the country-specific income50 60 70 80AgeMen, basic set of controls.8.85.9.951.8.85.9.951.8.85.9.951.8.85.9.95150 60 70 80AgeMen, full set of controls50 60 70 80AgeWomen, basic set of controls50 60 70 80AgeWomen, full set of controlsMost favourable early-life conditions Least favourable early-life conditionsFigure 9.2: Health age profiles: Physical health indicator.Note: Solid lines indicate 95 per cent confidence intervals.Source: SHARE Wave 1–6 release 6.1.0, Wave 7 release 0.9 How do early-life conditions shape health age profiles late in life? 95Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMand wealth distribution (right graphs in Figures 9.1). This evidence supportsthe hypothesis of a relevant role played by these variables in explaining hetero-geneity in health over the lifecycle, where adult socioeconomic status partiallymediates the impact of early-life socioeconomic background.The results in Figure 9.2 replicate the analysis considering the objectivephysical health indicator as an outcome variable. The differences in the pre-dicted health outcomes for men are narrower than in Figure 9.1 but still statisti-cally significant. Individuals who experienced better early-life conditions enjoybetter physical health outcomes later in life. For both genders, the differencebetween the two profiles increases after a certain age, approximately 70 formen and 60 for women. After controlling for the richest set of covariates (rightgraphs in the figure), we find that the divergence between the two age profilesshrinks, and the age when it starts increasing is slightly postponed (to age 70for men and 65 for women). Attrition related to mortality can affect the compo-sition of our sample and our findings, inasmuch as life expectancy depends ongender, education and, potentially, early-life conditions..65.7.75.8.85.65.7.75.8.85.65.7.75.8.85.65.7.75.8.8550 60 70 80AgeMen, basic set of controls50 60 70 80AgeMen, full set of controls50 60 70 80AgeWomen, basic set of controls50 60 70 80AgeWomen, full set of controlsMost favourable early-life conditions Least favourable early-life conditionsFigure 9.3: Health age profiles: Mental health indicator.Note: Solid lines indicate 95 per cent confidence intervals.Source: SHARE Wave 1–6 release 6.1.0, Wave 7 release 0.96 Michele Belloni et al.Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMAlong with physical health, we also analyse the dynamics of mental healthlater in life. As emerges from Figure 9.3, gender differences are more pronouncedwith respect to physical health. In line with the previous literature (Riecher-Rössler, 2016), we find that women are more likely to experience the worst mentalhealth at each age. In addition, the divergence in the age profile of mental healthbetween individuals who were exposed to good or bad early-life conditions ismore pronounced in the female sample. Regarding overall and physical health in-dicators, after controlling for adult socioeconomic conditions, the differences inthe age profiles related to early-life conditions are significantly reduced.9.4 ConclusionsThe evidence in this chapter supports the hypothesis that early-life conditionsare powerful predictors of health dynamics later in life. Individuals who grew upin a more favourable socioeconomic context are characterized by better physicaland mental conditions later in life. This pattern appears to be largely explainedby the positive effect played by a more inclusive parental background in improv-ing the educational attainment, income and wealth outcomes of individuals overthe lifecycle. These results highlight the long-run effects of public policies: pro-moting the social and economic inclusion of current generations improves theirown lifetime well-being and that of their offspring.ReferencesBengtsson, T. and Brostrӧm, G. (2009). Do conditions in early life affect old-age mortalitydirectly and indirectly? Evidence from 19th-century rural Sweden. Social Science &Medicine, 68, pp. 1583–1590.Brunello, G., Weber, G. and Weiss, C. T. (2017). “Books are forever: Early life conditions,education and lifetime earnings in Europe”. Economic Journal, 127, pp. 271–296.Cunha, F., Heckman, J. J. and Schennach, S. M. (2010). “Estimating the technology of cognitiveand noncognitive skill formation”. Econometrica, 78, pp. 883–931.Mazzonna, F. (2014). The long-lasting effects of family background: A European cross-countrycomparison. Economics of Education Review, 40, pp. 25–42.Riecher-Rössler, A. (2016). Sex and gender differences in mental disorders. The LancetPsychiatry, 4(1), pp. 8–9.9 How do early-life conditions shape health age profiles late in life? 97Brought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AMBrought to you by | Universita Studi di TorinoAuthenticatedDownload Date | 6/6/19 10:12 AM

How do early-life conditions shape health age profiles late in life?

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How do early-life conditions shape health age profiles late in life?

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