Bibliographic record
Abstract
capital structure has been done in the US context and finds puzzlingevidence that the US MNCs have lower leverage ratio compared to their domestic peers (see for example, Fatemi (1988), Burgman (1996), Chen, Cheng, He and Kim (i997) and Doukas and Pantzalis ( 2003)).Several explanations, such as higher agency costs of debt, higher business and political risk of MNCs, have been offered for the US evidence but there is no consensus on which factors drive this puzzlingevidence.I compare the capital structure difference between Canadian MNCs and DCs, and also compare the Canadian sample with a US matched sample for the period of 1998-2002.I contribute to the MNCs capital structure literature in three ways.First, contrary to the US evidence, I find that Canadian MNCs display higher leverage than DCs.Second, the higher leverage of Canadian MNCs is associated largely with their US operations, and is explained by their larger firm size and better access to the US capital market.I also show that the negative impact of agency costs of debt and business risk on leverage is more pronounced for Canadian MNCs' non-IJS operations compared to their US operations, and the agency costs of debt is the dominant factor.Third, to minimize the sample variation between Canada and the US, I construct a US sample that is matched with the Canadian sample based on year, industry and firm size.I show that the sensitivity of leverage to the firm-specific factors also differs between the two country samples.Overall, Chapter 2 shows that the capital structure of MNCs is influenced by a complex interaction of home and host country factors as well as the differences in the leverage determinants across countries.It also suggests that future research on MNCs' capital structure should differentiate between MNCs' regional and global expansions.Chapter 3 examines the impact of bond market access on Canadian firms' capital structure during the period of 1990-2003.Access to the public bond market could be very important for firms' capital structure decisions as it can change the maturity as well as the costs of debt.The traditional capital structure literature, however, largely focuses on the capital demand-side effects (e.g. firms with larger size, lower growth opportunity and lower business risk could have higher debt ratios), implicitly assuming that supply-side effects do not matter.In a recent study, Faulkender and Petersen (2006) examine whether the source of capital affects frrms' capital structure and show that the US firms with bond market access, as measured by having a credit rating, have signif,rcantly higher leverage ratios than firms without access.I show two main f,rndings in Chapter 3. First, I test the impact of supply-side effects on leverage by comparing the leverage difference between Canadian firms with and without bond market access, after controlling for demand-side effects.My evidence REFERBNCES
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".