PRELIMINARY AND INCOMPLETE DO NOT QUOTE OR CITE WITHOUT PERMISSION OF AUTHOR You Can't Always Get What You Want: Observations on Self-Reported Satisfaction, Consumption, and Underlying Utility
Bibliographic record
Abstract
Although social science surveys have long asked respondents to report levels of life satisfaction or happiness, economists have only begun to use those data in the last ten years or so. In their survey of the growing economics literature that studies happiness, Frey and Stutzer (2002) suggest that economists can learn much if they would directly study data on life satisfaction. Among other things, they suggest that measures of life satisfaction or subjective well-being can proxy for utility. If one accepts these measures as a proxy for utility then one can then estimate those goods and activities that directly yield utility. Gruber and Köszegi (2001) rely on this interpretation of life satisfaction measures to argue that smokers are irrational consumers because they report lower levels of life-satisfaction. They use this association to motivate a model of cigarette consumption in which they assume that consumers of cigarettes fail to make decisions consistently over time and that they suffer self-control problems (because they assume cigarettes are addictive). Gruber and Mullainathan (2002) adopt these assumptions to argue that cigarette excise taxes may make smokers better off. They use life-satisfaction measures from the General Social Surveys in the United States and in Canada to show that higher cigarette taxes are
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.442 | 0.377 |
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".