Firm-level Productivity Differences: Insights from the OECDs MultiProd Project
Bibliographic record
Abstract
Productivity plays a central role in shaping the welfare of societies and the competitiveness of countries. Productivity differences, for instance, explain a large share of the differences in income per capita across countries. This paper investigates the role of productivity heterogeneity across 18 countries over the period 2001-2012. In particular, it analyses the evidence that emerges from the distributed micro-data approach carried out in the OECD MultiProd project. The main outcome of the project is a unique dataset of harmonised crosscountry moments that are representative for the population of firms and comparable across countries even at a detailed industry level. We look at the 90-10 percentile ratio of LP and MFP and show that: i) productivity dispersion is high in both manufacturing and nonfinancial market services; ii) it has increased over time, especially in services; iii) a substantial part of this dispersion comes from differences among firms within the same sector of activity in each country; iv) this within sector dispersion remains the most important component of the overall dispersion for the entire period.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".