Yours is bigger than mine! Could an index like the Producer Subsidy Equivalent help in understanding the comparative incidence of industrial subsidies?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it