Political Connections, Governance, and Financial Performance: The Case of the Biggest Canadian Listed Companies
Bibliographic record
Abstract
We examined the links between companies' political connections and their accounting performance (ROA and ROE), market performance (Ln of Market Value), and the moderating role of governance in this relationship. The S&P/TSX Composite Index companies were used from 2008 to 2018, inclusive. We used two indicators to measure political connections: being politically connected (PC) and the Total number of such connections (TPC). About 37% of the companies studied have at least one political connection, mainly in the mining, oil, and gas sectors. LnMV only shows a significant difference between politically connected and non-connected companies. Politically connected companies have, on average, a higher market value than those without political connections, with a significant positive correlation between LnMV and the TPC. Regression analyses indicate that the relationship between Political connections and accounting performance (ROA, ROE) is not statistically significant. However, LnMV is positively related to PC but negatively associated with the TPC. Governance has a positive but weakly significant link with LnMV. The interaction between governance and PC is significant, showing a negative link with LnMV. In contrast, the interaction between Governance and the TPC shows a positive relationship with LnMV. Thus, the study suggests that political connections can influence companies' performance, but this effect depends on the quality of governance and the number of such connections. Our research is the first in Canada to show a relationship between companies’ political connections, governance, and market performance. Therefore, governance aspects must be considered when analyzing the impact of corporate political connections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".