When Do People Prefer Carrots to Sticks? A Robust “Matching Effect” in Policy Evaluation
Bibliographic record
Abstract
For a policy to succeed, it must not only be effective in changing behavior but must also be accepted by stakeholders. Here, we report seven sets of studies demonstrating strong framing effects on the acceptance of equivalent policies. Policies targeting desirable voluntary behavior are preferred when they are framed as advantaging those who act desirably (rather than disadvantaging those who do not). Conversely, policies targeting obligations are preferred when they are framed as disadvantaging those who fail to act desirably (rather than advantaging those who do). These differences in policy acceptance do not result from common causes of framing effects, such as a misunderstanding of outcomes or insufficient deliberation about the implications. Rather, the framing effects we document follow from beliefs about when punishment is and is not appropriate. We conclude with a field experiment demonstrating framing effects in a setting where policy acceptance directly affects respondents’ outcomes. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2539 . This paper was accepted by Yuval Rottenstreich, judgment and decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".