Bibliographic record
Abstract
There are two competing views on how immigration would affect local labor markets. When immigrants offer skills similar to those of native-born workers, they may compete directly with them, and this competition may lead to lower economic returns for native-born workers. This view can be called the “substitution†hypothesis. The alternative view is that immigrants may provide “complementary†skills, which can raise the productivity of other workers. If the substitution argument is effective, immigration might lead to out-migration of the nonimmigrant population from a community in the short run. Models in location-choice studies usually examine the migration decision in two separate processes: whether-to and where-to decisions about moving. The present study investigates how location choices of native-born workers can be influenced by the conditions in both the potential destinations and the departure regions. To validate either the substitution or complementary view, we apply choice-specific, clustered fixed-effect response models, which use industry- and occupation-specific regional attributes that allow us to control for unobserved regional heterogeneity as well as to identify regional factors that affect location choices. This study uses the 20 percent sample of the 2006 Census that covers the entire country with 282 census divisions. The results show that location-choice models are sensitive to how regional attributes are defined. When industry-specific immigration density differentials across regions are measured only at destinations, they have strong and negative effects on the location choices of the native born. However, when the models control choice-specific attributes relative to the origin, immigration variables become insignificant on the desirability of destinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".