The moderating role of director’s financial expertise in political connections and corporate financial performance in Pakistan
Bibliographic record
Abstract
Prior theoretical and empirical studies have suggested that political influence affects the application of corporate governance and firm performance enormously. However, several fundamental questions remain to be answered. To fill this knowledge gap,the study's main objectives are examining the direct impact of political connection on firm financial performance in Pakistani non-financial listed companies and the moderating effect of director's financial expertise on political connections and firm financial performance. The study utilised panel data of 220 firms from 2008 to 2017 and used panel corrected standard error regression analysis. The results show that political connection negatively impacted firm financial performance, and director financial expertise as a moderator strengthened the relationship between political connections and firm financial performance. This study's results supported political economy theory in that weak judicial systems and unstable political systems have immense effects on investor’s rights. The study contributes to extending the existing literature on political connection by providing evidence of the impact of politically connected firms on firm performance in an emerging market. The study also deliberates on how the director’s financial expertise contributes towards the relationship. The findings could be generalised to other countries with similar degrees of development and culture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".