Bibliographic record
Abstract
When making the decision to move to another country for work, people take into consideration the likelihood of obtaining work, the value of that work (often relative to work in the home country), and the costs faced when moving and living away from home. In order to understand the effects of costs of migration to workers, I build a two-country search model with costs of migration for workers. I focus on move costs and flow cost faced anytime a worker is away from his/her country of origin. Since the characteristics of the labor markets between EU countries and the US and Canada differ along separate dimensions, they can be used to illuminate the importance of costs in a worker’s migration decision. The model in this paper tends to over-predict migration, and implies that costs to workers moving between EU countries are higher than those for moving between the US and Canada. This second result is in contrast with the higher observed migration in the EU, and highlights important general equilibrium effects and the need for better understanding the migration decision. The benefit of the theoretical model employed here is that sending and receiving countries are considered individually. Natives, new migrants, and existing migrants are followed separately, shedding light on distinctions previously shown to matter in determining whether workers are helped or hurt by migration. The model supports empirical findings that the effects of migration on unemployment are sometimes mixed, but typically decrease unemployment overall. Importantly for policy implications, unemployment rates for all groups are lower when workers are permitted to move. This paper fills the gap in existing work by tracking migrants between countries and separates out within-skill wage effects of labor migration in both the sending and receiving countries. Migration can both help and hurt the wages of workers of all migration histories depending on the context, and wage outcomes for workers can vary drastically across a number of labor market characteristics. Differing experiences of migrants across migration and employment histories observed in the data can be predicted with the model, and is strongly influenced by costs to workers in the form of one time move costs and ongoing costs to living away from home, characteristics of the model in this paper which are frequently missing from existing work. The politicized nature of immigration policy and the increase in migration around the world makes it important to separate out myth from truth of the employment effects of immigration. Added to the highly charged nature of the political and news cycle discussion of immigrants is the disagreement in academic circles on the effects of immigration on labor market conditions. Empirical investigations of the effects of migrants on labor markets are necessarily limited, making a theoretical model necessary to weigh the sometimes contradicting effects of increased competition versus market growth. General equilibrium effects in the face of frictional labor markets and migration need to be understood before any policy is implemented responsibly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".