Housing systems, housing insecurity, and life satisfaction: a multilevel analysis of 158,765 individuals in 32 countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across nations, housing insecurity has been shown to affect life satisfaction. However, it is unclear (a) for whom housing insecurity affects life satisfaction and (b) whether and how housing systems moderate the association between housing insecurity and satisfaction. This study aims to reduce such knowledge gaps. We used data from the Gallup Poll that collects socioeconomic status and living standards from 158,765 individuals in 32 countries between 2016 and 2022. Multi-level regression was conducted to estimate the association between housing insecurity and life satisfaction, and the moderating effects of individual level employment status and country level housing systems. Housing insecurity significantly predicts a decrease in one’s life satisfaction. However, the association between housing insecurity and life satisfaction is moderated by individual level employment status and country-level housing characteristics. In low homeownership countries compared to high homeownership countries, the impact of housing insecurity on life satisfaction is more attenuated for part-time workers than for full time workers. Similar results are found for countries with a larger social housing stock when compared to those with lower stock. Investing in social housing not only reduces housing insecurity, but it is also conducive to mitigating the impact on the life satisfaction of the socioeconomically disadvantaged.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it