Scanner Data and the Construction of Inter‐Regional Price Indexes
Bibliographic record
Abstract
ABSTRACT The paper uses scanner data to measure the welfare effects of differing product availability across six Japanese regions. To eliminate the chain drift problem associated with the use of scanner data, various multilateral indexes were computed: GEKS, Geary–Khamis, and Weighted Time Product Dummy Hedonic price indexes. Chain drift can also be eliminated by estimating purchaser preferences using consumer demand theory. Thus, the paper also estimated linear preferences, CES preferences, and Konüs Byushgens Fisher (KBF) preferences using inverse demand functions, which dispenses with reservation price estimation. The various methods gave very different results, so the choice of method matters. A major problem with the Feenstra (1994) CES methodology for measuring the gains (or losses) of utility from new and disappearing products was illustrated. The results offer practical guidance for National Statistical Offices and researchers using scanner data to construct consumer price indexes in environments with high product turnover and regional heterogeneity.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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 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".