Earning Profiles for Italian Male Workers: Is There Evidence of a Premium for Education?
Bibliographic record
Abstract
Are younger generations better off than older ones? Can younger cohorts starting with lower real wages catch up with previous generations? Are young or old generations becoming more unequal? In this study we concentrate on the study of inter-generational and intra-generational patterns of earnings for Italian male dependent workers for the period 1987-2006. Using data from the Bank of Italy's Survey of Household Income and Wealth, we construct cohort-education-(macro) region-specific age profiles for mean real wages (the measure of central location here adopted) and for the 90-10 percentile differential (the inequality measure), allowing for region-specific price indexes. We verify how different cohorts have been doing comparatively and finally we test whether, with time, the (mean) returns to experience and education have increased. Our results indicate that, for the two education groups considered, each successive generation has benefited from higher entry wages, but we also find that the wage age-profiles for both education groups have become flatter so that we cannot conclude that more recent cohorts are better off than their immediate predecessors. When looking at high/low education relative wages, we find only scant evidence of positive cohort profiles (i.e. that the education premium has been rising across cohorts), while we notice that the relative wage tends to increase over the life-cycle. Finally, we find that inequality tends to increase with age, while we also find evidence of across-cohort variation, in the direction of increasing inequality. Our provides a clear framework in which between and within cohort comparisons are meaningful and easily interpretable. Moreover, it allows us to relate our results to those obtained by MaCurdy and Mroz (1995) and Beaudry and Green (2000) in their studies of the earning patterns of, respectively, American and Canadian workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.002 |
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".