Corporate Taxation and Firm-Specific Determinants of Capital Structure: Evidence from the UK and US Multinational Firms
Bibliographic record
Abstract
This paper aims to examine whether effective tax rate and firm-specific factors (such as firm size, growth opportunities, tangibility, risk, profitability, non-debt tax shields and liquidity) impact the capital structure of multinational firms in the energy sector. We employ regression models consisting of OLS, fixed effect and random effect to test balanced panel dataset of multinational firms based in the UK and USA over the period 2011–2019. We show a positive and significant effect of tangibility, risk, profitability and non-debt tax shields on long-term and total debt measures of capital structure. In the case of short-term debt, however, we reveal that it is significantly negatively related to tangibility, non-debt tax shields and liquidity, and positively associated with firm risk. Moreover, we report that the effective tax rate and firm size are insignificantly negatively related to the leverage choices of multinational firms, and liquidity has a significant inverse relationship with long-term debt and total debt. This study reveals mixed support for the prevailing capital structure theories and evidence that multinational firms are unequivocally responsive to the capital structure. The results significantly contribute to evaluating multinational firms in the energy sector and show how managers can achieve an optimal level of capital structure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".