The Economic Benefits of Irrigation Districts under Prior Appropriation Doctrine: An Econometric Analysis of Agricultural Land‐Allocation Decisions
Bibliographic record
Abstract
Abstract The economic literature has established that prior appropriation doctrine induces heterogeneity in risk among water users, which leads to an inefficient allocation of resources. In this study, we show that irrigation districts alleviate that risk by deviating from the strict application of prior appropriation doctrine. As a result, farmers inside irrigation districts are able to plant more water‐intensive crops than farmers outside irrigation districts, which increases average profitability. We empirically examine this hypothesis by leveraging a georeferenced panel data set at the spatial scale of the individual water right and spanning 2007–14 in Idaho's Eastern Snake River Plain. Our results indicate that on average, irrigation districts allocate larger portions of their land to drought‐sensitive, high‐value crops such as sugar beets and potatoes. As a result of differences in planting decisions, members of irrigation districts earn on average $16.20 per acre, or 6.0% more per year than those outside of irrigation districts.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".