Earnings Dynamics and Intergenerational Transmission of Skill
Bibliographic record
Abstract
This paper develops and estimates a two-factor model of intergenerational skill transmission when earnings inequality reflects differences in individual skills and other non-skill shocks.We consider heterogeneity in both initial skills and skill growth rates, allowing variation in skill growth to change over the lifecycle.Using administrative tax data on two linked generations of Canadians covering 37 years, we exploit covariances in log earnings (at different ages) both across and within generations to identify and estimate the intergenerational correlation structure for initial skills and skill growth rates, lifecycle skill growth profiles, and the dynamics of nonskill earnings shocks.We estimate low intergenerational elasticities (IGEs) for earnings in Canada (less than 0.2, even when based on 5-and 9-year average earnings); however, skill IGEs are typically 2-3 times larger due to considerable (and persistent) variation in earnings conditional on skills.Both earnings and skill IGEs decline substantially for more recent cohorts and are lower for children born to younger fathers.We estimate significant heterogeneity in both initial skills and skill growth rates, showing that intergenerational transmission of these factors explains up to 40% of children's skill variation.Skills become a more important determinant of earnings over the first part of workers' careers, while intergenerational transmission of skills becomes less important with age.Although "inherited" initial skills (compared to skill growth) are a more important determinant of children's skills throughout their lives, parents' initial skills and skill growth rates are equally important determinants of children's skills, largely because both strongly influence children's initial skills.Finally, we study intergenerational mobility for the 35 largest cities in Canada, determining the extent to which considerable differences in earnings and skill IGEs vary with the extent of local heterogeneity in parental skills vs. earnings instability.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".