How legal patterns over lifetime of migration shape migrants' labour market outcomes: Evidence from Mexican migrants in the United States
Bibliographic record
Abstract
Abstract This article proposes a life‐course measure of the legal pattern that accounts for the overall legal pattern over the lifetime of migration and explores the two main pathways to labour market outcomes: selecting into legal patterns associated with different levels of labour market outcomes and realising different labour market outcomes within each pattern. Results suggest that labour market outcomes depend on which legal patterns migrants end up with instead of what they realise within each pattern. Particularly, migrants with more human capital and better social capital select into legal patterns associated with better labour market outcomes, but they do not realise better labour market outcomes given the legal patterns they experienced. From the dynamic perspective, the economic integration of migrants depends on legal patterns. Migrants in legal patterns that initiate with temporary resident status experience economic integration over time. The growth rate is larger for the patterns that involve a transition in legal status. This paper makes important contributions to the literature. First, it identifies holistic legal patterns that account for the legal status over migrants' entire migration history. Second, it sheds light on the selection into different legal patterns and highlights selection as the major process in explaining migrants' labour market outcomes. Third, it shows how legal patterns, jointly shaped by initial legal status and legal transition, determine migrants' economic integration. Finally, the inclusion of temporary resident status, beyond the illegal–legal dichotomy, enriches our understanding of how liminal legality over the lifetime of migration shapes labour market outcomes.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".