Bibliographic record
Abstract
Recent work has revived two intertwined challenges to aggregate production functions (the 'identity ' and 'aggregation' problems).This paper examines both problems in the context of aggregate industry-by-country analysis, first demonstrating the relevance of the identity problem for industry-level analysis and tracing its origin in the System of National Accounts.Using a case study of materials quality in global forestry and logging, the paper then compares estimates from fully physical versus conventional (monetary) production functions to isolate the aggregation problem and show that credible inference depends on appropriately modelling heterogeneity in production processes.Materials quality is measured via finite mixture modelling applied to global satellite data.Attempting to estimate the parameters of a common production technology yields poor results, because of differences in production processes between countries.The paper offers a practical approach for dealing with heterogeneity via Data Envelopment Analysis and heterogeneous coefficient panel estimators, and concludes with guidance to help applied industry-level analysis recognize and avoid both the identity and aggregation problems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".