Is There a ‘Price’ to Pay for Agricultural <scp>TFP</scp> Measurement? Limitations of the Distance Function Approach
Bibliographic record
Abstract
ABSTRACT Cross‐country comparisons of agricultural total factor productivity (TFP) diverge markedly depending on method: superlative indices (e.g., Törnqvist) leverage price and quantity data, while quantity‐only indices (e.g., Malmquist) rely solely on quantities, yielding inconsistent estimates. We theoretically demonstrate that this disparity stems from measurement errors in the quantity‐only approach's implicit shadow prices, which deviate substantially from market prices employed by superlative methods, introducing noise and bias. Utilising a novel, cross‐country consistent dataset of agricultural production accounts for the United States, Canada and Australia (1961–2006), we empirically affirm that the superlative index consistently outperforms its quantity‐only counterpart in accuracy and aggregation stability across scales. This superiority, rooted in price data's capacity to reflect economic realities (e.g., input cost shifts), underscores the critical need for comprehensive price information in international productivity assessments. Our findings offer actionable guidance for agricultural economists and policymakers prioritising robust TFP metrics.
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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 | 0.019 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".