The Impact of Leased and Rented Assets on Industry Productivity Measurement
Bibliographic record
Abstract
Leasing is an important means of gaining access to assets, of obtaining finance, and of reducing a lessee?s exposure to the risks inherent to asset ownership. A lease can be either a financial lease (capital lease) or an operating lease (capital rental). A financial lease is one where the legal owner of an asset (lessor) passes the economic ownership to the user of the asset (lessee), who then accepts the operating risks and receives the economic benefits from using the asset in a productive activity. Under an operating lease, the lessor is both the legal owner and the economic owner of the asset leased (rented), bearing the operating risks and receiving the economic benefits from the asset. The lessor transfers only the right to use the asset to the lessee. Leasing offers firms the possibility to acquire the right to use capital assets under terms that differ from those prevailing through other financial instruments. The recording of leased assets in the Canadian System of National Accounts is ownership-based rather than user-based. The separation of capital ownership, in particular legal ownership, from the use of capital assets poses challenges to productivity measurement. To obtain consistent productivity measures at an industry level, leased and rented capital assets must be reallocated from owners? accounts to users? accounts. By using the General Index of Financial Information (GIFI) corporate balance sheets and detailed input-output tables, this paper tests the robustness of existing practices of data collection on leased and rented capital.
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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 | 0.003 | 0.004 |
| 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".