Citizenship, Co-ethnic Populations and Employment Probabilities of Immigrants in Sweden
Bibliographic record
Abstract
Over the last decades, Sweden has liberalized its citizenship policy by reducing the required number of years of residency to five for foreign citizens and only two for Nordic citizens. Dual citizenship has been allowed since 2001. During the same period, immigration patterns by country of birth changed substantially, with an increasing number of immigrants arriving from non-western countries. Furthermore, immigrants were settling in larger cities as opposed to smaller towns as was the case before. Interestingly, the employment integration of immigrants has declined gradually, and in 2006 the employment rate for foreign-born individuals is substantially lower compared to the native-born. The aim of this paper is to explore the link between citizenship and employment probabilities for immigrants in Sweden, controlling for a range of demographic, human capital, and municipal characteristics such as city and co-ethnic population size. The information we employ for this analysis consists of register data on the whole population of Sweden held by Statistics Sweden for the year 2006. The basic register, STATIV, includes demographic, socio-economic and immigrant specific information. In this paper we used instrumental variable regression to examine the "clean" impact of citizenship acquisition and the size of the co-immigrant population on the probability of being employed. In contrast to Scott (2008), we find that citizenship acquisition has a positive impact for a number of immigrant groups. This is particularly the case for non- EU/non-North American immigrants. In terms of intake class, refugees appear to experience substantial gains from citizenship acquisition (this is not, however, the case for immigrants entering as family class). We find that the impact of the co-immigrant population is particularly important for immigrants from Asia and Africa. These are also the countries that have the lowest employment rate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".