When and Why Consumers (Erroneously) Believe Income Impacts the Enjoyment of Consumption Experiences
Bibliographic record
Abstract
Abstract We examine how people (as observers) anticipate levels of happiness from psychological consumption experiences (e.g., learning a new language or visiting a park). All else being equal, we propose and demonstrate that people expect differences in happiness based on income. Specifically, we show that relative to observers themselves, individuals simultaneously expect low-income consumers to enjoy psychological consumption experiences less and high-income consumers to enjoy them more. This is because consumers hold a lay theory that human needs must be fulfilled in a sequential, linear manner, which leads to income-based inferences of need prioritization. Thus, observers simultaneously believe that low-income consumers do (and should) prioritize their low-level physical needs first, but high-income consumers (who have presumably already fulfilled their physical needs) can prioritize their high-level psychological needs. Critically, we demonstrate that these lay theory-driven inferences are faulty, showing that the priority level assigned to these needs and the actual happiness resulting from psychological consumption experiences do not follow the predicted pattern. Namely, income either has no relationship with actual happiness (visitors to theme parks, sporting events, and concerts) or the reverse relationship, such that lower-income consumers report greater happiness than higher-income consumers (secondary data from a major league professional sports team).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".