Population aging, migration, and productivity in Europe
Bibliographic record
Abstract
This paper provides a systematic, multidimensional demographic analysis of the degree to which negative economic consequences of population aging can be mitigated by changes in migration and labor-force participation. Using a microsimulation population projection model accounting for 13 individual characteristics including education and immigration-related variables, we built scenarios of future changes in labor-force participation, migration volumes, and their educational composition and speed of integration for the 28 European Union (EU) member states. We study the consequences in terms of the conventional age-dependency ratio, the labor-force dependency ratio, and the productivity-weighted labor-force dependency ratio using education as a proxy of productivity, which accounts for the fact that not all individuals are equality productive in society. The results show that in terms of the more sophisticated ratios, population aging looks less daunting than when only considering age structure. In terms of policy options, lifting labor-force participation among the general population as in Sweden, and education-selective migration if accompanied by high integration, could even improve economic dependency. On the other hand, high immigration volumes combined with both low education and integration leads to increasing economic dependency. This shows the high stakes involved with integration outcomes under high migration volumes.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".