Nitrogen Fertilizer Demand from Danish Crop Farms: Regulatory Implications of Heterogeneity
Bibliographic record
Abstract
This paper estimates nitrogen fertilizer demand elasticities for Danish crop farms using the dual profit function approach on micro panel data. The model includes several farm‐specific parameters, allowing us to estimate the mean demand elasticity and test for homogeneity of elasticities across panel farms. The mean own‐price elasticity for nitrogen is −0.45, and there is a significant standard deviation from this mean for individual farms of 0.24. Heterogeneity of demand elasticities implies that regulating fertilizer application through mandated uniform percentage reductions, as is currently used in Danish nitrogen regulation, increases abatement costs relative to tax regulation. Somewhat surprisingly, this causes the abatement costs of quota regulation to be only 8% larger than with tax regulation. Simulation results indicate that the primary threat to the efficiency of uniform reduction schemes comes from inaccurate estimation of baselines rather than from heterogeneity of elasticities. L'auteur estime les élasticités de la demande d'engrais azotés pour l'agriculture danoise en appliquant la fonction du double profit aux données issues de microgroupes de consultation. Le modèle inclut plusieurs paramètres spécifiques à l'agriculture, ce qui permet d'estimer l'élasticité moyenne de la demande et de vérifier l'homogénéité des élasticités pour les agriculteurs consultés. On constate qu'il existe une élasticité‐prix moyenne de −0,45 pour l'azote et que cette moyenne présente un écart‐type significatif de 0,24 par exploitation agricole. Les élasticités hétérogènes de la demande supposent que si on régule les engrais azotés par une réduction uniforme du volume employé en pour cent, ainsi que l'exige présentment le règlement danois, on accroît le coût des mesures de lutte, comparativement à ce qui se produit avec les mesures fiscales. Il est un peu surprenant de voir que la régulation par quota n'augmente le coût des mesures de lutte que de 8%, par rapport aux mesures fiscales. Les résultats de la simulation révèlent que l'efficacité des programmes de réduction uniforme est surtout menacée par une estimation inexacte des valeurs de base et non par l'hétérogénéité des élasticités.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".