The Long-Term Economic Impact of Reducing Migration in the UK
Bibliographic record
Abstract
This paper uses an OLG-CGE model for the UK to illustrate the long-term effect of migration on the economy. We use the current Conservative Party migration target to reduce net migration “from hundreds of thousands to tens of thousands” as an illustration. Achieving this target would require reducing recent net migration numbers by a factor of about 2. We undertake a simulation exercise to compare a baseline scenario, which incorporates the principal 2010-based ONS population projections, with a lower migration scenario, which assumes that net migration is reduced by around 50 per cent. The results show that such a significant reduction in net migration has strong negative effects on the economy. By 2060 the levels of both GDP and GDP per person fall by 11.0 per cent and 2.7 per cent respectively. Moreover, this policy has a significant impact on public finances. To keep the government budget balanced, the effective labour income tax rate has to be increased by 2.2 percentage points in the lower migration scenario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".