The influence of recession and macroeconomic variables on sectorial capital structure
Bibliographic record
Abstract
Abstract The aim of this paper is to analyze the influence of the recent recession and of macroeconomic variables over the indebtedness in Brazilian industry sectors. The gap derives from the preference for investigating the reaction of capital structure according to economic sectors. However, it has to be considered that industry sectors react differently to variations in the economic context, since they have different optimal points of capital structure composition. The relevance of the chosen topic lies in carrying out a sectorial analysis of the effect of recession and of macroeconomic variables on capital structure composition, identifying the most sensitive sectors. It is also relevant in terms of being based on classical financial theories applied to the current context, in order to help predict the proportion of debt given fluctuations in a set of macroeconomic variables. Standing out among the main contributions of this article are the analysis of the level of indebtedness of Brazilian companies given the occurrence of recession and variations in the macroeconomy, identifying sectors that are most exposed to modifying their capital structure due to these factors. Six research hypotheses were formulated and tested using multiple linear regression, with two-stage fixed effects based on panel data collected from 211 companies, classified into six sectors, with data relating to the first quarter of 2010 up to the first quarter of 2018. The results revealed that the recent Brazilian recession was relevant for the capital structure of the sectors studied, with inflation only being significant for the health sector. The level of indebtedness of the basic materials sector was shown to be the most dependent on economic fluctuations and that of telephony and utilities was shown to be the least dependent. In addition, it was verified that the company-specific variables have greater relevance in determining capital structure compared to the macroeconomic ones.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".