Implementing Dynamics of Immigration Integration in Labor Force Participation Projection in EU28
Bibliographic record
Abstract
Many developed countries have turned to immigration in order to mitigate the consequences of population aging, particularly the expected decline in the labor force population. Yet, few projection models take in consideration explicitly the differentials in labor force participation of population sub-groups. This paper describes the labor force participation module of CEPAM-Mic, which is a microsimulation model that projects several demographic, ethnocultural, and socioeconomic dimensions of the EU28 member countries population. Then, the microsimulation model is used to project EU labor force population for the period 2015–2060 under different scenarios illustrating how implementing sex- and country-specific dynamics of immigrants’ integration may affect the future labor force in terms of size, rates, and gender composition. We estimated the parameters of the labor force module using logistic regressions based on the EU-Labour Force Survey (EU-LFS). In addition to age, sex, and education, immigrant-related variables are also included, such as immigrant status, place of birth, age at immigration, and duration of residence in the estimation of the probability of being active. Our results demonstrate the importance of taking into account differentials in labor force participation of population sub-groups when asserting the potential of immigration as a tool for managing population aging. In the European context, adding immigration differentials in labor force participation affects mainly downward the number of female immigrants in the labor force, while smaller differences are observed for male immigrants. An increase in immigration levels leads obviously to an increase in the total labor force size, but may also widen gender inequalities in labor force participation and has limited impact on the total labor force participation rate. Our findings suggest that relying on immigration as a tool to alleviate economic issues arising from population aging must imperatively be accompanied by strong and efficient measures to promote a full economic integration of immigrants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".