Valuing the impact of self-rated health and instrumental support on life satisfaction among the chinese population
Bibliographic record
Abstract
Abstract Background Research has highlighted that satisfaction in health, and instrumental support (IS) are key areas of life affecting an individual’s wellbeing. Many social and public health initiatives use these two intervention mechanisms to improve individual’s wellbeing. For the purpose of cost-benefit assessment, there has been growing interest in expressing these intervention effects in economic terms. However, only a handful of studies have ever estimated these effects in economic terms, none of which examined them in a Chinese context. The aim of this study is to extend this line of valuation work to the Chinese population, estimating the implicit willingness-to-pays on the effects of improving individuals’ self-rated health (SRH) status and IS on their life satisfaction (LS). Methods Using data from a two-wave representative panel survey in Hong Kong (n = 1,109), this study conducted a cross-lagged analysis with a structural equation modelling technique to examine the causal effects of SRH and IS on LS. The use of this cross-lagged approach was an effort to minimise the endogeneity problem. Then, substituting the respective estimates to the formulae of compensating surplus, the marginal rate of substitution of SRH and IS with respect to individual’s equivalised monthly household income (HI) were estimated and were then expressed as the implicit willingness-to-pays on the effect of improving individuals’ SRH and IS on their LS. Results The cross-lagged analysis ascertained the causal effects of SRH (β = 0.074, 95% Confidence Interval: 0.021, 0.127) and IS (β = 0.107, 95% Confidence Interval: 0.042, 0.171) on individuals’ satisfaction with life. Translating into the concept of compensating surplus, the implicit monetary values of improving the sample’s SRH from “poor health” to “excellent health” and their perceived IS from “little support” to “a lot of support” are equivalent to an increase in their equivalised monthly HI by US$1,536 and US$1,523 respectively. Conclusions This study is the first to derive the implicit monetary values of SRH and IS on individual’s LS in a predominantly Chinese society, and it has implications for the cost-benefit assessment in wellbeing initiatives within the population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".