Saving, Sharing, or Spending? The Wealth Consequences of Raising Children
Bibliographic record
Abstract
This study uses 1986-2012 National Longitudinal Survey of Youth 1979 cohort data to investigate the relationship between raising children and net worth among younger Baby Boomer parents. I combine fixed-effects and unconditional quantile regression models to estimate changes in net worth associated with having children in different age groups across the wealth distribution. This allows me to test whether standard economic models for savings and consumption over the life course hold for families at different wealth levels. My findings show that the wealth effects of children vary throughout the distribution. Among families at or below the median, children of all ages were associated with wealth declines, likely due to the costs of child-rearing. However, at the 75th percentile and above, wealth increased with the presence of younger children but decreased after those children reached age 18. My results, therefore, provide evidence for a saving and investment model of child-rearing among wealthier families but not among families at or below median wealth levels. For these families, the costs of raising children largely outweighed motivations for saving.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".