The Effect of National Business Systems on Social and Environmental Disclosure: A Comparison between Brazil and Canada
Bibliographic record
Abstract
Purpose -This paper evaluates the influence of the institutional environment on the extent of social and environmental disclosure of companies from institutionally distant countries.Design/methodology/approach -We analyze basic materials, oil and gas, and public utility companies with shares traded on the Brazilian stock exchange (BM&FBovespa) and Canadian stock exchange (Toronto Stock Exchange) from 2007 to 2015.Quantitative methods are adopted through descriptive statistics and panel data analysis.The econometric modeling considers environmental and social disclosure as dependent variables, independent variables that represent the political, financial, educational and labor systems, and firm size, ROA, and indebtedness as control variables.Findings -In the case of the companies operating in Brazil, the extent of environmental and social disclosure is positively related to the political and labor systems, and negatively related to the financial system.In Canada, disclosure is negatively influenced by the financial system and the education system.The control variables, which represent characteristics of financial performance, were not significant.Originality/value -The study shows that isomorphic forces operate in the institutional field and affect the adoption of socially responsible behavior.By basing the study on institutionally distinct countries, such as Brazil and Canada, it reinforces the influence of the national business system on the extent of disclosure of environmental and social practices.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".