Stochastic Biases in Technical Change in U.S. Agriculture: A Bootstrap Approach
Bibliographic record
Abstract
Having argued that the modeling of technical change as a smooth deterministic function of time is likely to misrepresent the true nature of technical change, this paper reexamines biased technical change in U.S. agriculture using a system of share equations with unobserved components errors, with technology treated as a stochastic unobserved variable. Employing data to represent the aggregate output and input of the U.S. agricultural sector over the period 1947–94, significant factor biases were found that appear to be linearly independent and do not appear to be smooth and deterministic. Technical change in U.S. agriculture appears to have been biased toward saving expenditure on labor at the expense of expenditure on intermediate inputs, with some small saving on the expenditure on capital inputs over the entire period 1947‐94. The paper also employs a bootstrapping approach in order to obtain finite sample tests with approximately the correct size under less stringent assumptions about the data generating process than assumed by maximum likelihood (ML) based approaches. Using these finite sample values significantly alters the conclusions reached regarding the nature of technical change. Après avoir démontré que la modélisation du changement technique par une fonction lissée déterministe du temps peut ne pas représenter correctement sa vraie nature, cet papier re‐examine le biais du changement technique dans l'agriculture américaine à partir d'un systè d'équations de parts avec des erreurs non observées et une technologie traitée comme une variable stochastique non observée. A partir de données représentant la production et les intrants agrégés dans le secteur agricole américain sur la période 1947–1994, nous constatons que les biais significatifs sont indépendants linéairement et qui ne sont pas des fonctions déterministes lissées du temps. Le changement technique dans l'agriculture américaine est biaisé en faveur des réductions de dépenses de travail aux dépends des dépenses sur les intrants intermédiaires et montre quelques économies sur les dépenses de capital sur la période 1947‐1994. L'emploi des techniques de bootstrapping permet d'obtenir des tests sur échantillons finis avec des hypothèses moins exigeantes que celles imposées par les approches de maximum de vraisemblance. Les conclusions obtenues à partir de ces tests sont significativement différentes que celles obtenues par les méthodes traditionnelles concernant le changement technique.
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 | 0.010 | 0.046 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".