The Synergistic Financial Effect of Corporate Political Activities: The Case of Listed Canadian Companies
Bibliographic record
Abstract
Corporate political activities can bring genuine political capital to firms and are an effective way to access key resources to boost financial capital and maximize profits. These activities fall into three categories: coopting ex-politicians to decision-making bodies (board of directors and top management) to benefit from their social capital; lobbying to directly influence public policy; and making financial contributions to the activities of political parties and committees. This study asks the following question: what is the combined effect of two of these activities (political connections and lobbying) on the financial and accounting indicators of Canadian listed companies? We argue that engaging in corporate political activities allows firms to accumulate a type of political capital that we define as the sum of all political activities conducted by an individual company. To perform our research, we analyzed Canadian companies listed on the S&P/TSX composite index from 2012 through 2016. Results show that firms with this type of political capital are generally in a better financial position than those without it. A significant correlation was found between a firm’s political capital and its main sources of financing (equity and long-term debt) as well as with its ROE. Political capital has more positive impacts on key firm financial indicators than does each type of political activity on its own (synergistic effect).
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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 | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".