Investor perceptions of the benefits of political connections
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how foreign and domestic investors differ in their beliefs about the relative merits of a firm's political connections. Design/methodology/approach – These differences are employed to explain cross-sectional variation in the previously documented premium in A-share prices relative to otherwise equivalent foreign currency denominated B-shares for Chinese firms. Findings – Chinese domestic individual investors were excluded from owning B-shares of Chinese firms prior to February 20, 2001. The authors find that firms with more political connections have higher premiums and a smaller reduction in premiums associated with this event. Research limitations/implications – This is consistent with domestic block holders deriving additional benefits from politically connected firms. Practical implications – The findings also have important policy implications by showing that government can have a strong effect on the economy even without applying macro-policy tools. Social implications – Government ownership in listed companies can result in discrepancies among classes of investors with respect to their valuations. Furthermore, the prohibition of short sales prevents arbitrage from correcting this bias, and eventually the role of the market in allocating resources efficiently is undermined. Originality/value – The authors investigate the role of political connections as implied by the proportion of state ownership in explaining the A-share premium. Unlike previous studies that associate state ownership with political risk, the paper relates state ownership to political connections that are particularly beneficial to domestic large block shareholders. This interpretation is consistent with the findings and with previous literature on state ownership and political connections of Chinese firms.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".