Production Structure, Factor Substitution, and Total Factor Productivity in the South African Agricultural Sector
Bibliographic record
Abstract
The production structure of the South African agricultural sector is analyzed using duality theory in production and cost. An unrestricted translog cost function is estimated, and a number of model restrictions (homothetic, homogenous, unitary elasticity of substitution, Hick’s neutral technical change, and no technical change) are tested for, but none of them was found to be statistically significant. The Allen Elasticity of Substitution (AES) and the Morishima Elasticity of Substitution (MES) are calculated to analyze factor substitution, and found that the AES may give erroneous results in the case of number of factors exceeding two. The substitution of land is found to be easiest while that of fuel to be hardest. Furthermore, technical change is found to have a negative impact on agriculture, but there are increasing returns to scale in South African agriculture. However, technical change dominates over scale effect, and results in negative total factor productivity growth. When these results are combined with the finding that, it is easier to substitute machinery by labor than vice-versa, it appears that labor-intensive technologies may be useful for agriculture growth.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".