Childhood Circumstances and Health Inequality in Old Age: Comparative Evidence from China and the USA

Abstract

This paper estimates the extent to which childhood circumstances contribute to health inequality in old age and evaluates the importance of major domains of childhood circumstances to health inequalities in the USA and China. We link two waves of the China Health and Retirement Longitudinal Study in 2013 and 2015 with the newly released 2014 Life History Survey, and two waves of the Health and Retirement Study in 2014 and 2016 with the newly released 2015 Life History Mail Survey in the USA, to quantify health inequality due to childhood circumstances for which they have little control. Using the Shapley value decomposition approach, we show that childhood circumstances may explain 7–16 and 14–30% of health inequality in old age in China and the USA, respectively. Specifically, the contribution of childhood circumstances to health inequality is larger in the USA than in China for self-rated health, mental health, and physical health. Examining domains of childhood circumstance, regional and rural/urban status contribute more to health inequality in China, while family socioeconomic status contributes more to health inequality in the USA. Our findings support the value of a life course approach in identifying the key determinants of health in old age. Distinguishing sources of health inequality and rectifying inequality due to early childhood circumstances should be the basis of policy promoting health equity.

Appendices

Appendix 1

See Figs. 5, 6, 7, 8 and 9.

Fig. 5

Distributions of self-rated health (the USA vs. China, age 60 +). Notes: USA14 and USA16 respectively represent self-rated health in 2014 and 2016 in the USA, CHN13 and CHN15 respectively represent self-rated health in 2013 and 2015 in China

Fig. 6

Sensitivity analysis of main findings in Fig. 1 (the USA vs. China, on Age Cohort 60 +, Restricting to More Comparable Childhood Circumstances Defined in Table 2). Notes: This set of results only consider childhood circumstances comparable between the USA and China. Specifically, the family SES domain only includes parents’ educational attainments, household type, family financial status; the domain of parents’ health status and health behaviors only includes parents’ longevity status; the domain of relationship with parents only includes physical abuse by parents; the domain of health and nutrition conditions in childhood only includes self-rated health

Fig. 7

Robustness Checks Comparing Age Cohorts 45–49, 50–59, and 60 + using the Chinese Sample (Restricting to More Comparable Childhood Circumstances Defined in Table 2). Note: O represents age cohort 60 +, Y4 represents age cohort 45–49, Y5 denotes age cohort 50–59

Fig. 8

Robustness Checks Comparing Age Cohorts 50–64 and 65 + using the American Sample (Restricting to More Comparable Childhood Circumstances Defined in Table 2). Note: Y represents age cohort 50–64, O represents age cohort 65 +

Fig. 9

Robustness Checks Comparing Alternative Samples of the USA in physical health (the USA vs. China, age 65 +). Notes: ***p < 0.01. Frailty5_USA represents results using the sample in original submission with 2266 individuals who participated in at least one of the two waves of frailty tests. Frailty5_USA_R represents results using the sample with 2075 individuals who participated in both waves of HRS frailty tests

Appendix 2: Measuring Relative Contribution of Each Domain of Childhood Circumstances

The overall contribution $\hat{r}$ can be neatly decomposed into components $\hat{r}^{j}$ for each category j in childhood circumstances C with the idea of the Shapley approach.

$$\hat{r} = \mathop \sum \limits_{j} \hat{r}^{j} = \mathop \sum \limits_{j} \left( {var\,Y} \right)^{ - 1} \left[ {a_{j}^{2} var\,C^{j} + \frac{1}{2}\mathop \sum \limits_{k} a_{k} a_{j} cov\left( {C^{k} ,C^{j} } \right)} \right]$$

(4)

where j, k=1, 2, … are categories of childhood circumstances. $\alpha_{j}$ and $\alpha_{k}$ are coefficients of categories j and k. Equation (4) presents an example of a Shapley Value Decomposition. This approach provides an appropriate way to assign roles to sources in generating health inequality (Björklund et al. 2012; Ferreira and Gignoux 2013; Jusot et al. 2013; Shorrocks 2013; Roemer and Trannoy 2016).

A particular category j’s overall contribution to the variance in Y—$\hat{r}^{j}$—corresponds to an average between two channels. Intuitively, childhood circumstances may not only directly impact health in old age, but exert their effects indirectly through sha** other childhood circumstances and adulthood efforts. Formally, all $C_{j \ne J}^{j}$ are held constant in the direct contribution of category j, i.e. $a_{j}^{2} \left( {var Y} \right)^{ - 1} var C^{j}$. Regarding the indirect contribution, category j itself is held constant, and its indirect contribution, i.e. $\frac{1}{2}\left( {var\,Y} \right)^{ - 1} \sum\nolimits_{k} {a_{k} a_{j} cov\left( {C^{k} ,C^{j} } \right)}$, is taken as the difference between the total variance and the ensuing variance.

To compute the Shapley value decomposition, we first estimate the inequality measure for all possible permutations of the circumstance variables. In a second step, the average marginal effect of each circumstance variable on the measure of IOP is computed (Juarez and Soloaga 2014). This procedure is very computationally intensive as 2 ^K (K= number of circumstances) must be computed.