Migration as an Adjustment Mechanism in the Crisis? A Comparison of Europe and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The question of whether migration can be an equilibrating force in the labour market is an important criterion for an optimal currency area. It is of particular interest currently in the context of high and rising levels of labour market disparities, in particular within the Eurozone where there is no exchange-rate mechanism available to play this role. We shed some new light on this question by comparing pre- and post-crisis migration movements at the regional level in both Europe and the United States, and their association with asymmetric labour market shocks. We find that recent migration flows have reacted quite significantly to the EU enlargements in 2004 and 2007 and to changes in labour market conditions, particularly in Europe. Indeed, in contrast to the pre-crisis situation and the findings of previous empirical studies, there is tentative evidence that the migration response to the crisis has been considerable in Europe, in contrast to the United States where the crisis and subsequent sluggish recovery were not accompanied by greater interregional labour mobility in reaction to labour market shocks. Our estimates suggest that, if all measured population changes in Europe were due to migration for employment purposes – i.e. an upper-bound estimate – up to about a quarter of the asymmetric labour market shock would be absorbed by migration within a year. However, in the Eurozone the reaction mainly stems from migration of third-country nationals. Even within the group of Eurozone nationals, a significant part of the free mobility stems from immigrants from third countries who have taken on the nationality of their Eurozone host country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it