How spending decisions shape happiness in everyday life
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
This study examines the emotional consequences of spending choices in everyday life across a diverse multinational sample. Based on a dataset of 200 participants across 7 countries who received $10,000 USD, we analyzed how happy they felt from different types of purchases made with that money. Participants derived high levels of happiness from some types of purchases that have been examined in past research (e.g., buying experiences), but also from other purchases (e.g., education) that have not been the focus of previous work. We found some evidence that the emotional benefits of spending choices varied depending on whether participants lived in higher vs. lower-income countries; specifically, we found differences in the benefits of spending on gifts, housing, debt, and time-saving services. Around the world, people who spent money in ways that made them happy experienced greater improvements in overall subjective well-being 3 and 6 months later. This study presents an analysis of reported happiness following spending decisions of an endowment of 10,000 USD. Participants in high vs low-income countries differed regarding what spending decisions contributed more to happiness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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