Simultaneous Estimation of Cost and Distance Function Share Equations
Bibliographic record
Abstract
The study considers the simultaneous estimation of share equations using cost and distance functions. Simultaneous rather than single system estimation utilizes full as opposed to limited information. Econometric results exploit the nonstationary nature of the data and that variables are cointegrated. Under cointegration all variables are endogenous and so it is not necessary to undertake the somewhat ad hoc exercise of choosing instruments to achieve parameter consistency. Johansen's maximum likelihood estimator is applied to data from Central Canada and Western Canada (1935–2006). Symmetry and homogeneity restrictions are not rejected for either region. Monotonicity held for all data points and concavity held at 92% of the data points. Long‐run constant returns and Hicks neutral technological change are rejected for both regions. Morishima elasticity estimates coming from the cost function in Western Canada indicate highly elastic long‐run substitution between the land/fertilizer input pair and mildly elastic long‐run substitution between land and both machinery and labor. In contrast, substitution for land and other inputs is inelastic for the land/machinery pair and the land/labor pair, with only the land/fertilizer pair being mildly elastic. The results indicate the limiting nature of land as a fundamental constraint on long‐term agricultural production is a real possibility in Central Canada because other inputs are inelastic, or at best only mildly elastic, substitutes for land. In Western Canada, fertilizer is the only factor that is highly substitutable for land and, therefore, could mitigate the limiting nature of land in that region. However, given that fertilizer applications are often considered to be environmentally unfriendly, the long‐run substitution of fertilizer for land as a fundamental mitigating factor to land scarcity in Western Canada is at a cost to the environment.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".