The Interpretation of Unit Value Indices - Unit Value Indices as Proxies for Price Indices
Bibliographic record
Abstract
The unit value index (UVI) as compiled in Germany for exports and imports is compared with two other indices, viz. an index of Drobisch which unfortunately is likewise known as "unit value index" and the "normal" Laspeyres price index (PI) of exports and imports. The UVI may be viewed as a Paasche index compiled in two stages where unit values instead of prices are used in the low level aggregation stage. Unit values are average prices referring to an ag-gregate of (more or less homogeneous) commodities. The focus of the paper is on the decom-position of the discrepancy between UVIs and PIs (the "unit value bias") into a (well known) Laspeyres (or substitution) effect or "L-effect" and a structural component or "S-effect" due to substituting unit values for prices. It is shown that amount and sign of S depends on the corre-lation between the change of quantities of those goods that are included in the aggregate and their respective base period prices. By contrast to L the correlation between quantity and price movement is not relevant for S. This paper is a revised version of my contribution to the 11th Ottawa Group Meeting in Neuchatel (Switzerland) 27th to 29th May 2009 http://www.ottawagroup2009.ch/
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".