Explaining subjective social status and health: Beyond education, occupation and income

被引:0
|
作者
Robson, Matthew [1 ]
Chen, Gang [2 ,4 ]
Olsen, Jan Abel [3 ,4 ]
机构
[1] Erasmus Univ, Erasmus Sch Econ, Rotterdam, Netherlands
[2] Univ Melbourne, Melbourne Sch Populat & Global Hlth, Melbourne, Vic 3010, Australia
[3] UiT Arctic Univ Norway, Dept Community Med, N-9019 Tromso, Norway
[4] Monash Univ, Ctr Hlth Econ, Monash Business Sch, Caulfield, Vic 3145, Australia
基金
英国惠康基金;
关键词
Subjective social status; Health-related quality of life; EQ-5D-5L; Health inequality; Decomposition; Quantile regressions; SOCIOECONOMIC-STATUS; POSITION; SHAPLEY;
D O I
10.1016/j.socscimed.2025.117869
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Subjective measures of social status often explain variations in health better than the typical objective measures of education, occupation, and income. This raises the question: if status affects health, then what affects status? To answer this, we ran a survey using representative samples of adult populations in the UK, US and Canada (n = 3,431) to gather data on respondents' subjective social status (SSS) and health-related quality of life (HRQoL), alongside an extensive, rarely gathered set of socioeconomic variables: education, occupation, income, comparative income, wealth, childhood circumstances, parents' education, partner's education, and social and cultural capital. We conduct Shapley-Owen decompositions to identify the relative contributions of these variables in explaining variation in SSS and HRQoL and use RIF (recentered influence function) -regressions to go beyond the mean and identify how these contributions change across the quantiles of SSS and HRQoL. Results show that education, occupation, and income explain relatively little of the explained variation in SSS (26%), while comparative income, wealth and childhood circumstances together explain more than 60%. We find that at higher quantiles of SSS and HRQoL the more subjective and relativistic measures of socioeconomic status contribute more to the explained variation, whilst at lower quantiles, variation is better explained by the more objective socioeconomic variables (i.e. education, occupation, income and wealth). These findings shed light on how policy makers could consider intervening to reduce health inequalities.
引用
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页数:19
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