Intergenerational Persistence of Earnings: The Role of Early and College Education
Bibliographic record
Abstract
Recent empirical studies show that the intergenerational persistence of economic status in the U.S. is much higher than previously thought. We develop a quantitative theory of inequality and intergenerational transmission of human capital where parents invest in early and college education of their children subject to borrowing constraints. Children differ exogenously in innate abilities, which can be correlated with their parent's innate ability. An important feature of the environment is that the quality of early education determines the probability of college completion. We calibrate a stationary equilibrium of this economy to relevant statistics in aggregate U.S. data, and use it to investigate the sources of inequality and persistence in earnings. In our benchmark model, about half of the intergenerational persistence and one fourth of the inequality in earnings are accounted for by endogenous investments in education. We find that early investments in education account for most of the endogenous persistence in earnings, while college education generates most of the endogenous inequality in earnings. Our theory is suited to study the effect of educational policies on the persistence of inequality. We show that public resources devoted to early education have the largest impact on earnings mobility. Moreover, non-progressive college subsidies generate more intergenerational persistence of earnings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".