Mathematical Discussion on the Relationship Between SUTs and SIOTs
Bibliographic record
Abstract
Since the 1993 System of National Accounts (SNA) and especially the 2008 SNA, traditional input-output table (IOT) of Leontief has been modified quite a lot with many variations. The supply and use tables (SUTs) seem to be substituted for the IOT, although there has not been a complete guiding to SUTs to IOT conversion.Originally in 1968 SNA, SUTs was called make and use matrices as an intermediate step to compile IOT. However, 1993 and 2008 SNA seem to replace IOT with SUTs, of which regulations make it difficult to convert from SUTs to IOT such as regulation on the size of supply and use tables and regulation on the prices of the intermediate input matrix.Some countries use computable general equilibrium (CGE) model, while others use both the CGE model and input-output analysis, so they need to convert SUTs into symmetric input-output tables (SIOTs). The construction of SIOTs is a controversial issue as regards the choice of model to construct both product-by-product and industry-by-industry SIOTs. This paper discusses the SUTs given in 1968, 1993, 2008 SNA, and the method for converting SUTs to SIOTs.Although there have been several articles on how to convert SUTs into SIOTs, this article is an effort to provide an easier, more understandable way to convert SUTs to SIOTs based on the arrangement of supply and use matrices.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 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".