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Record W3126078199

Expected service lives and depreciation profiles for capital assets. Evidence based on a survey of Norwegian firms

2016· preprint· en· W3126078199 on OpenAlexaboutno aff
Terje Skjerpen, Nina Barth, Ådne Cappelen, Steinar Todsen, Thom Åbyholm

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDepreciation (economics)NorwegianService (business)Capital (architecture)Carry (investment)BusinessCapital assetFixed assetEconomicsHuman capitalFinanceMarketingCapital formationFinancial capitalMicroeconomicsProduction (economics)GeographyMarket economy
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.312
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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