Happiness and economic growth: does the cross section predict time trends? ; evidence from developing countries
Bibliographic record
Abstract
Based on point-of-time comparisons of happiness in richer and poorer countries, it is commonly asserted that economic growth will have a significant positive impact on happiness in poorer countries, if not richer. The time trends of subjective well-being (SWB) in 13 developing countries, however, are not significantly related to predictions derived from the cross sectional relation of happiness to GDP per capita. The point-of-time comparison leads to the expectation that the same absolute increase in GDP per capita will have a bigger impact on SWB in a poorer than a richer country. In fact there is no significant relation between actual trends in SWB and those predicted from the cross sectional relationship. Nor is a higher percentage rate of growth in GDP per capita significantly positively associated with a greater improvement in SWB. In the developing countries studied here a greater increase in happiness does not accompany more rapid economic growth. These conclusions hold true for two measures of SWB that are separately analyzed, overall life satisfaction and satisfaction with finances. The two SWB measures themselves, however, typically trend similarly within a country, providing mutually supporting evidence of the trend in well-being.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".