Bibliographic record
Abstract
Researchers interested in estimating productivity can choose from an array of methodologies, each with its strengths and weaknesses.Methods differ by the assumptions they rely on and imply very different calculations.I compare five widely used techniques: (a) index numbers, (b) data envelopment analysis, and three parametric methods, (c) instrumental variables estimation, (d) stochastic frontiers, and (e) semi-parametric estimation.I compare the estimates directly and evaluate three productivity debates using a panel of manufacturing plants in Colombia.The different methods generate surprisingly similar results.Correlations between alternative productivity estimates are invariably high.All methods confirm that exporters are more productive on average and that only a small portion of the productivity advantage is due to scale economies.Productivity growth is correlated more strongly with export status, frequent investments in capital equipment, and employment of managers than with the use of imported inputs or foreign ownership.On the debate whether aggregate productivity growth is driven by plant-level changes or output share relocation, all methods point to the importance of plant-level changes, in contrast to results from the U.S.
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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 | 0.040 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".