Microfoundations of Rising Experience Premia
Bibliographic record
Abstract
During the past three decades both the skill and the experience premium increased significantly in English-speaking countries, leading to higher wage inequality. The skill premium increases within any experience group. On the contrary, the experience premium rises significantly within the group of unskilled workers, while it remains flat among the skilled ones. Existing theoretical literature fails to provide a unified explanation of these facts. Using a model of asymmetric information, credit market imperfections and employer learning, I propose a microfounded justification for these recent patterns of wage inequality. In particular, I show that the relaxation of credit constraints decreased the initial wage for unskilled and inexperienced labor and this generated an increase in the experience premium within the group of unskilled workers. This suggests that a decrease in real minimum wages, allows initial salaries to fall, which in turn amplifies economic inequality. The mirror image between real minimum wages and economic inequality is a pattern that finds strong empirical support in many countries and especially in US, UK and Canada. My theory is also consistent with a rising skill premium within both the group of experienced and inexperienced 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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".