The effect of political connections on companies’ performance and value
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the effect of companies’ political connections (PCs) on their financial and stock performance, as well as on their market values. Design/methodology/approach A sample of non-financial companies listed on the Tunis Stock Exchange (TSE) between 2012 and 2014 was used. The accounting and financial data of these companies were obtained from their financial statements, whereas data on PCs of their officers and directors were collected manually from various sources. Correlation and multivariate regression analyses were performed to test the hypothesis of this research. Findings The results showed that PCs improve companies’ performance and value. These results could be explained, on the one hand, by the benefits and favors that companies can get from their political ties and, on the other hand, by investors’ tendency to invest in politically connected companies to benefit from these advantages. Research limitations/implications The limited number of non-financial companies listed on the TSE is a limit for this research. Practical implications The results show that investment in companies which are politically inter-connected may be beneficial for investors, and especially for small minority shareholders. Social implications The results confirm that political links are essential for business success in emerging economies, such as Tunisia. However, the positive link between politics and business might highlight the issue of corruption after the revolution. Originality/value To the best of the authors’ knowledge, this is the first study to examine the effect of PCs on the performance and value of Tunisian companies after the 2011 revolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it