Labor Mobility of Immigrants: Training, Experience and Opportunities
Bibliographic record
Abstract
The transition pattern of immigrants to a new labor market is characterized by high wage growth, fast decrease in unemployment as immigrants first find blue-collar jobs, followed by a gradual movement to white-collar occupations. A central aspect of this process is the acquisition of local human capital in the form of the local language, on the job learning (experience) and the participation in training programs provided by the government. This paper focuses on the labor mobility and human capital accumulation of male immigrants who moved from the former Soviet Union to Israel and are characterized by their high levels of skills and education. We formulate a dynamic choice model for employment and training in blue and white-collar occupations, where the labor market randomly offered opportunities are affected by the past choices of the immigrant. The estimated model fits well the observed patterns of unemployment, employment by occupation and training. The estimated rates of return to training are very high (13 % to 19%) for most of the male immigrants. However, the estimated disutility from training and the two percent rate of return per quarter for local experience deter the immigrants from participation in training. The wage return to language knowledge is large, but imported skills have zero return in the new country. We find that the effect of training on job offer probabilities has a larger impact on the immigrant’s welfare than the wage return. Furthermore, the total individual welfare gain from the existence of training programs is estimated to be between one percent to one and half percent increase in the expected present value utility at arrival to the new country. The social gross rate of return from the availability of the government provided vocational training programs, is estimated to be only about.85 percent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".