Measuring Price Elasticities of Demand and Supply of Water Entitlements Based on Stated and Revealed Preference Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 it