Should I Pay or Should I Go? Dutch Evidence on Tax-Induced Out-Migration
Bibliographic record
Abstract
We investigate the mobility response of workers to a loss in preferential tax treatment, exploiting a radical policy change that shortened the duration of the tax break available to high-skilled migrants to the Netherlands. The policy change only affected migrants who arrived from specific countries within a certain period. Using tax and population-wide data and a difference-in-differences approach, we uncover the causal effect of tax-induced emigration and show that taking away tax breaks from migrants strongly increases their likelihood of leaving. Crucially, our findings show that this effect is entirely driven by out-migration from the top 1% of the income distribution. No migration response was detected for those below the 95th earnings percentile. Individuals in the top 1% of the income distribution decrease their length of stay by 11.1% and are 18.3% less likely to remain beyond the end of the five years that they receive the tax break, while ‘highly mobile’ individuals also react in terms of both intensive and extensive duration margins when among top 5% of earners. We estimate that the elasticity of migration with respect to the net-of-tax rate for the top 1% of earners is 1.48 to 1.74. Regarding the distortionary effect of national policies on international tax competition, we show that the most mobile tax earners become much more likely to leave for a tax-friendly country post reform, and we conclude that, at least in terms of the change in taxable receipts from labor in the Netherlands, this policy was cost neutral.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".