Japan's Prefectural-Level KLEMS: Productivity Comparisons and Service Price Differences
Bibliographic record
Abstract
We compile a prefectural-level KLEMS database for Japan and conduct productivity comparisons for Japanese 47 prefectures. One of the difficulties in compiling regional KLEMS database is how to handle variation in service prices across regions. To cope with this problem, we estimated cross-regional pricelevel differences in each industry in the service sector based on prefectural-level item-wise data of service prices. For estimation, we applied the Country-ProductDummy (CPD) method, a method used to estimate absolute purchasing power parities among countries. As a result of re-calculation, the standard deviation of cross-regional TFP difference indices in 2009 decreased by around 13 per cent. In addition, by using the derived cross-regional price difference indices, we confirmed that the Balassa-Samuelson effect, which holds among international economies, also holds among regional economies in Japan.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".