Explaining the labour performance of immigrant women in Spain: The interplay between family, migration and legal trajectories
Bibliographic record
Abstract
The labour performance of migrants has been largely assumed to vary with individuals’ reasons to migrate. Accordingly, migrants who migrate to join their relatives at destination are commonly expected to be negatively selected in terms of their labour characteristics. However, family and economic reasons for migration are not mutually exclusive but often complementary, especially among women. In addition, there exists a large variation in the post-migration employment rates of the so-called ‘family migrants’. In this article, I argue that the temporal sequence of migration and key family life-cycle events may help us in explaining the post-migration employment patterns of migrants, especially that of females. To test this hypothesis I first construct a comprehensive typology that classifies all immigrants according to the timing of marriage and migration for each spouse, and their immigrant or native origin. Next, I examine the explanatory power of this typology by estimating the employment probability of migrants in multivariate logit regressions that include the resulting types of ‘family migrants’ as independent. The obtained results confirm the theoretical and empirical utility of studying marriage and migration jointly in order to explain differences in the labour performance of immigrant women. Moreover, the main conclusions concerning cross-type differences in female labour behaviour remain valid ever after controlling for current legal status and legal status at entry, and running separate analyses for the main origin groups in Spain.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".