Determinants of leasing propensity in Canadian listed companies
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between a firm's propensity to lease and several firm characteristics: tax position, financial constraint, ownership structure, growth, and size. Design/methodology/approach Controlling for industry, total lease share, operating and capital lease share ratios, obtained using an income statement approach, are regressed on a trichotomous tax variable, a dichotomous cash flow coverage ratio variable, debt over fixed assets, ownership concentration, market to book value of shares and the natural log of sales. Findings Total lease share increases with leverage, tax position and growth; it decreases with cash flow coverage, ownership concentration and firm size. Results for operating lease share are similar to those for total lease share. In contrast, capital lease share decreases with tax position and increases with ownership concentration and size. Research limitations/implications The results suggest that leasing offers added debt capacity and increases in financially constrained firms. Firms that pay high taxes seem to place more value on the constant stream of tax deductions from the rental payments than on deductions from decreasing interest costs and amortization. Finally, highly concentrated Canadian firms may use less leasing because they are more family‐controlled. Originality/value The literature offers mixed reasons for firms' decisions to lease or purchase assets. This study provides further evidence in a rich setting. In 2001, the Canadian tax authorities changed the tax treatment of leases, thus providing an opportunity to validate prior results on the impact of taxes on leasing. By including two different measures of financial constraint, this study disentangles the substitution and the added debt capacity hypotheses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".