Impacto da carga tributária na estrutura de capital das empresas brasileiras
Bibliographic record
Abstract
This study investigates the relationship between tax burden and corporate indebtedness in Brazilian companies, testing three hypotheses: first, whether the tax burden positively influences the level of overall indebtedness; second, whether it influences short-term debt; and third, whether it influences long-term debt. The analysis uses accounting data from 161 publicly traded companies across various sectors, covering the period from the fourth quarter of 2010 to the fourth quarter of 2023. totaling 8.533 observations extracted from the open data portal of the Brazilian Securities and Exchange Commission (CVM). To test the hypotheses, generalized linear models were estimated, considering overall debt (OD), short-term debt (STD), and long-term debt (LTD) as dependent variables. The tax burden (TB) was used as the explanatory variable, along with a set of control variables: profitability, current liquidity, asset turnover, firm size, economic crisis, Selic rate, GDP, and IPCA. The results indicate that the tax burden has a positive and significant relationship with all debt metrics, confirming the formulated hypotheses. Moreover, robustness tests did not indicate endogeneity, reinforcing the reliability of the findings. These results suggest that the tax burden plays a relevant role in the capital structure of Brazilian companies, providing empirical support for the Trade-Off theory.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".