Yours is bigger than mine! Could an index like the Producer Subsidy Equivalent help in understanding the comparative incidence of industrial subsidies?
Bibliographic record
Abstract
Abstract State support remains a leading cause of tension in international commercial relations. Governments see trade distortions that look like they were caused by industrial subsidies, but lack data to illuminate that state support. In the 1980s, the Organisation for Economic Co‐operation and Development (OECD) developed an index that helped countries to see the overall incidence of agricultural subsidies, initially called the Producer Subsidy Equivalent (PSE) and the Consumer Subsidy Equivalent (CSE). Are there lessons for today in the PSE approach? I try to answer that question from the standpoint of economics: how did the PSE evolve, what is it, is the concept relevant to industrial subsidies? And of politics: how was OECD able to create the tool, and do present conditions permit something similar? The PSE was a response to a shared perception of crisis. It drew on well‐established concepts in the agricultural economics and trade literatures. And it works best in a context where market power is sufficiently diffuse that a price gap between domestic and world prices can be calculated. Only some of those conditions can be met when applying the approach to concentrated industries dominated by large firms that operate in multi‐country supply chains.
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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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".