Earnings dynamics and its intergenerational transmission: Evidence from Norway
Bibliographic record
Abstract
Using administrative data, we provide an extensive characterization of labor earnings dynamics in Norway. Some of our findings are as follows: (i) Norway has not been immune to the increase in top earnings inequality seen in other countries, (ii) the earnings distribution compresses in the bottom 90% over the life cycle but expands in the top 10%, and (iii) the earnings growth distribution is left‐skewed and leptokurtic, and the extent of these nonnormalities varies with age and past income. Linking individuals to their parents, we also investigate the intergenerational transmission of income dynamics. We find that children of high‐income, high‐wealth fathers enjoy steeper income growth over the life cycle and face more volatile but more positively skewed income changes, suggesting that they are more likely to pursue high‐return, high‐risk careers. Income growth for children of poorer fathers is more gradual and more left skewed, displaying higher left tail risk. Furthermore, the income dynamics of fathers and children are strongly correlated: children of fathers with steeper life‐cycle income growth, more volatile incomes, or higher downside risk also have income streams of similar properties. These findings shed new light on the determinants of intergenerational mobility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".