Generational differences in income trajectories in the Nordic welfare state
Bibliographic record
Abstract
How much it matters for your income development what generation you happen to be born? We answer this question by using registers of the total population, we study generational income inequality during 1970–2018 and, for men and women in Finland. We follow the income trajectories of the cohorts born in 1920–1983 over their adult life course and observed, how certain structural factors explain differences in income trajectories. Our study expands state-of-the-art knowledge, as previous research has often bypassed the question of how much generational income differences explains of populations total income inequalities and what factors may explain the different generational income trajectories. Results show that overall generational income differences explained quarter for women and 6 percent for men total income inequality. Each successive cohort until 1980s had a higher average income trajectory. However, generation born in the 1980s has been falling behind. For both men and women, age structure and education were the most important factors associated with income inequality. On contrary to previous findings on Nordic welfare state, our results also indicate that, generational income trajectories are affected by economic shocks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".