Expected service lives and depreciation profiles for capital assets. Evidence based on a survey of Norwegian firms
Bibliographic record
Abstract
In the Norwegian national accounts, as in many other countries, it is quite common to use information on depreciation rates and profiles based on studies from the US, Canada and the Netherlands due to a lack of national studies. We present new results based on a survey of Norwegian firms concerning their perception of the expected economic service life of different types of capital assets and their assessments of the most realistic depreciation profiles. For some capital categories, information on acquisition prices and second-hand market prices were also collected, together with information on the age of capital assets when they were sold in second-hand markets. We present the companies' answers about expected service lives and depreciation profiles, and carry out an econometric analysis for two types of capital where second-hand markets exist, Machinery and equipment for mining and manufacturing, and Tools, instruments, furnishings etc. For the first group, the expected service life is estimated to be between 9 and 10 years, while, for the second group, the estimate is about 8 years. According to the descriptive analysis, the reported mean expected service lives are around 10 and 7 years, respectively. Our results are quite similar to those obtained in the literature.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".