Integration of Refugees in the Labor Market:: Effects of 2017 Immigration Policy
Bibliographic record
Abstract
The world witnessed a surge in refugee migration and immigration policy changes after the “Migrant Crisis” of 2015. As millions of families fled violence in their home countries, they sought safety in developed countries, including Canada, the United States, and countries in the European Union. Overwhelmed by the rise in migration, many of these countries began to implement immigration policies designed to prevent or deter refugees from crossing their borders. In the United States, the Trump administration implemented executive orders in 2017, including the order titled “Protecting the Nation from Foreign Terrorist Entry into the United States,” which impacted the numbers and demographics of migrants arriving in the U.S. This study used a difference-in-difference analysis to estimate the effects of these immigration policies on refugees’ income and employment outcomes. Using data from the 2016 and 2018 5-Year American Community Survey and the USCIS Yearbook of Immigration Statistics, this study found that overall, refugees experience lower wages, and lower educational premiums than their native counterparts. However, the difference-in-difference analysis showed that refugee males’ wages increased at a higher rate than natives’ wages between 2016 and 2018 and both refugee males and females experienced higher growth in employment than natives in this period. The implications of this research are limited due to data limitations. Further research should complete a difference-in-difference analysis between two similar refugee communities rather than between refugee and native communities to fully understand the effects of these 2017 immigration policy changes in the United States.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".