Explaining subjective social status and health
Beyond education, occupation and income
Bibliographic Data
| ID | 4592159 |
|---|---|
| Authors | Matthew Robson (0000-0003-4558-7637, Erasmus University Rotterdam), Gang Chen (0000-0002-8385-5965, Monash University), Joseph A Olsen (0000-0001-9472-2669, Monash University) |
| Year | 2025 |
| Volume | 371 |
| Pages | 117869 |
| Publication date | 2025-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Science & Medicine (JOURNAL) |
| Journal identifiers | ISSN: 0277-9536 • E-ISSN: 1873-5347 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.socscimed.2025.117869 |
| PMID | 40058244 |
| OpenAlex | W4408011611 |
| Language | EN |
| References cited | 41 |
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
Demographic economics · Economic growth · Economics · Health care · Population · Public health · Social determinants of health · Social medicine · Social science · Social status · Socioeconomic status · Sociology · Demography · Employment and Welfare Studies · Health disparities and outcomes · Health Systems, Economic Evaluations, Quality of Life · Medicine · Psychology
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| Highly cited | No |
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