Measuring Price Elasticities of Demand and Supply of Water Entitlements Based on Stated and Revealed Preference Data
Bibliographic record
Abstract
Estimates of price elasticities of water entitlements (known as permanent water or water rights in the United States) are complicated by data limitations and problems of endogeneity. To overcome these issues, we develop an approach to generate stated preference data and combine them with revealed preference data to estimate price elasticities from various types of water entitlement sales in the southern Murray‐Darling Basin, Australia. Our results suggest that price elasticities of demand and supply of high security water entitlements are inelastic in the relevant market price range between AUD $1,700 to $2,100 per mega‐liter, and that supply is relatively more inelastic than demand. For lower reliability water entitlements, the price elasticity of demand is estimated to be even more inelastic than high security water entitlements. The price elasticity of supply for general security water entitlements is similar to high security water entitlements, while the supply of low reliability water entitlements is extremely inelastic for our data set. The comparison between the stated and revealed preference data provides strong evidence of support for a data fusion approach; nevertheless, some differences in water sale preferences were found for irrigators choosing not to sell all of their water. The consistency of our results signals support for the use of this methodology in other water basins around the world.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".