Gender in Environmental Context: The Effect of Property Rights, Fines, and Empathy Nudging
Bibliographic record
Abstract
Experimental economics research shows that gender can often explain some of the variation in individual behavior in experiments. This is especially true for contextualized games (corruption, environmental protection) in which participants’ behavior is guided by homegrown values and predispositions. We examine the gender differences in environmental behavior and the sharing of payoffs between a farmer and a water user under two alternative property rights assignments (farmer/polluter vs. water user/victim) and three methods of feedback (inducing empathy vs. imposing fine vs. no feedback). We found mixed evidence of gender differences concerning the choice of levels of pollution. Overall, albeit not always statistically significant, it seems that females are sharing with their group members more than males. Specifically, the results suggest that females are often more empathetic than males when they are in a position of a victim (water user). In a position of a polluter (farmer), in contrast, females and males are almost equally empathetic. Overall imposing monetary fines is counterproductive and decreases environmentally friendly behavior (however it does not significantly affect sharing), while empathy nudging increases sharing behavior (however it does not significantly affect environmentally friendly behavior). Empathy nudging is more effective for females than for males. Imposing fines, however, has no significant gender effect for either conservation or sharing behavior. Our findings provide another argument for increased gender equality based on environmentally sustainable economic development and thus propose a push by national governments as well as international organizations to increase the economic role of women.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".