Theory, Evidence, and Policy on Dual-Class Shares: A Country-Specific Response to a Global Debate
Bibliographic record
Abstract
Dual-class shares have become one of the most controversial issues in today’s capital markets and corporate governance debates. In the past years, academics, regulators, policymakers and stock exchanges from all over the world have been discussing whether companies should be allowed to go public with dual-class shares and, if so, which restrictions should be imposed. After analysing the regulatory approach to dual-class shares existing in several jurisdictions around the world, this article shows that countries seem to have adopted three primary models to deal with dual-class share structures: (i) the imposition of bans traditionally existing in the United Kingdom, Australia and several jurisdictions in Asia, Europe and Latin America; (ii) the permissive model allowing dual-class structures without any significant restrictions, as it happens in the United States, Sweden, and the Netherlands; and (iii) the restrictive approach implemented in Singapore, Hong Kong, Canada, India and Mainland China. It will be argued that, despite the global nature of the debate on dual-class shares, regulators should be careful when analysing foreign studies and approaches since the optimal regulatory model to deal with dual-class shares depends on a variety of local factors. Namely, this article argues that, in countries with sophisticated markets and regulators, strong legal protection for minority investors, and low private benefits of control, regulators should allow companies to go public with dual-class shares with no restrictions or minor regulatory intervention. By contrast, in countries without sophisticated markets and regulators, high private benefits of control, and weak legal protection for minority investors, dual-class shares should be prohibited or subject to higher restrictions. Finally, intermediate solutions should be adopted for countries with mixed features. Therefore, the key question to be addressed from a policy perspective is not whether companies should be allowed to go public with dual-class shares, as many authors and regulators have been discussing in the past years, but whether dual-class class shares should be allowed and, if so, under which conditions, taking into account the particular features of a country.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".