The Curiously Continuing Saga of Choosing the Measure of Welfare Changes
Bibliographic record
Abstract
The results of the vast array of willingness to accept compensation/ willingness to pay (WTA/WTP) disparity studies provide strong evidence that people value many losses and reductions of losses, more, and often much more, than otherwise commensurate gains or foregoing of gains. These findings also make it clear that people commonly value many changes not as final states as standard theory assumes, but as positive or negative changes relative to a neutral reference state. Consequently, not only are losses to be most accurately assessed with the WTA measure, but most positive changes that reduce losses are as well. Current practice, which rarely takes such reference dependence into account, is therefore likely to substantially understate the value and importance of projects, policies, and programs that reduce losses. Failing to take the possibilities of valuation disparities into account also appears to undermine other kinds of analyses as well, including, for example, the estimation of elasticities and setting effective levels of Pigouvian taxes.
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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 | 0.067 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".