What do citizens in tax havens think? The EU blacklist and public opinion in Switzerland
Bibliographic record
Abstract
Blacklisting is a widely used yet controversial instrument aimed at encouraging tax havens to alter their domestic policies, particularly in the wake of the Global Financial Crisis. International organizations such as the OECD and the EU have published tax haven blacklists, but these have been criticized for their limited effectiveness as policy tools. This paper examines the political rationale behind the EU’s blacklist, by focusing on the potential role of public opinion as a driver of policy change. Specifically, we ask whether the threat of blacklisting – through naming-and-shaming or economic sanctions – can shape public attitudes toward tax reform in low-tax jurisdictions. To do this, we conduct an original survey experiment in Switzerland, and show that both a ‘naming-and-shaming’ and an ‘economic threat’ treatment significantly increase public support for tax reform, though estimated effect sizes are modest. These results provide insight into the potential of international instruments like blacklists to mobilize public opinion in support of policy change.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".