Risk Taking With Social Consequences
Bibliographic record
Abstract
Strong egalitarian norms and preferences may affect entrepreneurship. If people feel guilty of their success they may take fewer risks, whilst if they expect their successes to be celebrated, they would take more risks. In this paper we ask whether anticipated social consequences influence risky choices. Do people take more, less or the same risk when inequality results from risky choice? We provide experimental evidence from rural Uganda. Subjects choose lotteries for themselves and a partner under different risk resolutions, allowing us to identify their type. We find anticipated social consequences influence risk taking for most people, as only one quarter are indifferent. Two-fifths are ex post inequality seeking, holding their own pay off constant, and take more risk when inequality is common. This possibility is not considered by previous experiments in the West, but is the largest category for our sample. Only one-third are ex-post inequality averse, reducing inequality of outcomes at a cost to their expected earnings. We show types are robust, and document large gender-based heterogeneity. These results imply inequality-aversion is not holding back risk taking on average. Rather there is great heterogeneity in how people respond to anticipated social consequences.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".