Will friends and family still be there after you have left? Evidence from return migrants in Colombia
Bibliographic record
Abstract
It is widely acknowledged that immigrants use their social networks (friends, family and colleagues) to find a job upon arrival to their destination country. However, there is little evidence on how return migrants find jobs upon their return to their home country, one of the main pillars of re-integration. This paper examines the role of social networks for returnees finding work. It draws on data from two years of Colombian nationally representative surveys conducted in 2016 and 2017. Colombia has one of the highest numbers of displaced people due to protracted internal conflict. I exploit a mass deportation event of Colombian migrants from Venezuela in 2015 which prompted a wave of return migrants and show that return migrants are more likely to rely on networks in their search than never migrants, and that this is potentially due to inability to find jobs through other means. I also show that jobs found through networks for return migrants may be lower quality than jobs found through other means. This paper contributes to the literature on return migrant integration, and speaks to an important question in the literature: Will friends and family still be there for you after you have left?
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".