A Graphical Depiction of Hicksian Partial-Equilibrium Welfare Analysis
Bibliographic record
Abstract
An inescapable conclusion to be drawn from the literature on the measurement of welfare is that the use of consumer’s surplus is a bad idea. This is especially true in the light of the fact that modern computing power facilitates the straightforward calculation of equivalent variation, the operational welfare indicator that is most strongly justified by economic theory. Consequently, a correct welfare analysis of a price-income change of the sort discussed in virtually every cost-benefit text should be principally in terms of Hicksian demand curves, not ordinary (Marshallian) ones. The present paper explains how to construct a graphical depiction of such an analysis, in partial equilibrium, which may be adopted in teaching the principles of cost-benefit analysis to graduate and advanced undergraduate students. This is done by way of several detailed examples covering the scope of applicability of the technique. More than two decades have now passed since the lack of validity of consumer’s surplus as a welfare measure was demonstrated rigorously and reasonable alternatives were proposed. Chipman and Moore (1976, 1980) showed that restrictive and empirically
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".