The Causal Structure of Land Price Determinants
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
This paper investigates causation contemporaneously and dynamically to elucidate the persistent lack of agreement about what “causes” changes in farmland prices. The analysis synthesizes and extends previous investigations in this area by employing a combination of directed acyclic graphs (DAG), a recently developed modeling technique, and cointegrated VAR model. DAG theory and algorithms offer a powerful tool for analyzing contemporaneous causal relationships among economic variables. The results from this study confirm the importance of measures of return to farming, financial (credit market constraints) and/or macroeconomic activity as significant determinants of fluctuations in farmland prices. Le présent article examine la causalité de façon contemporaine et dynamique pour élucider le manque persistant de consensus quant aux causes de variations du prix des terres. L'analyse est une synthèse des études antérieures de même qu'un prolongement effectuéà l'aide d'une combinaison de graphes acycliques orientés (DAG), technique de modélisation mise au point récemment, et du modèle VAR cointégré. La théorie des DAG et les algorithmes constituent un outil puissant pour analyser les liens causals contemporains des variables économiques. Les résultats confirment l'importance du rendement de l'activité agricole, des contraintes financières (contraintes du crédit) et/ou de l'activité macroéconomique comme déterminants significatifs des variations du prix des terres agricoles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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