1 The Receipts Approach to the Collection of Household Expenditure Data
Bibliographic record
Abstract
The receipts approach to the collection of household expenditure data consists of allowing Household Expenditure Survey (HES) participants to collect and turn in bar code receipts. This detailed data can then be utilised in a systematic way. The present paper describes the use of such an approach in the Icelandic HES, analyses the results, and evaluates the main elements of the receipts approach method. This approach was first used in the Icelandic HES in 1995 and has been continuously employed in the HES since 2000. Information gathered in this way now covers nearly one-third of total HES expenditures and approximately 75 per cent of all survey transactions. HES data of this sort has been the source of the very detailed weights used in calculating the Icelandic CPI. Furthermore, it was the main source for analysing the sudden increase in shopping substitution bias when inflation rose suddenly in Iceland during the second quarter of 2001. This occurrence is analysed here with receipts data from the HES. The agenda for future research on the receipts approach is also discussed.
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.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".