The cost of private transportation in the Netherlands, 1992-1999
Bibliographic record
Abstract
Abstract – User-cost indexes provide important information about the welfare implications of durable goods purchases. In this paper indexes for the cost of private transportation are developed, based on the costs associated with using new passenger cars sold in 1992-1999, in the Netherlands. These user costs consist of both fixed costs such as economic depreciation, and operating costs such as fuel consumption and maintenance. The paper examines the existence of differences between car model based user-cost indexes and CPI based indexes. Use is made of information about prices and technical characteristics of about 7,000 car models sold in the Dutch passenger car market in the period 1992-1999. In addition, data on user costs are used. The data are obtained from two different sources: ANWB (the Royal Dutch Tourist Office) and RDC (the RAI Data center, which is responsible for car registrations in the Netherlands). The same question has been analyzed by Schultz (2001) for new passenger cars in two cities of Canada (Montréal and Toronto). Schultz found minor differences between the CPI approach and the user-cost approach with respect to total fixed costs, but considerable differences for operating costs, especially for Montréal. The results in our paper show that total annual user costs have increased about 4.8 % over the observation period vs. 10.6 % according to the CPI based index. Fixed costs have increased about 9 % both for the car model and the CPI based index. Operating costs have decreased more than 3% according to the car model based index, whereas an increase of more than 13 % is observed for the CPI based index. 1.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".