The Wealth of Generations, With Special Attention to the Millennials
Bibliographic record
Abstract
We examine household wealth across birth cohorts and over time using data from the Survey of Consumer Finances.We show that although the Great Recession reduced wealth in every age group, longer-term trends indicate that the wealth of older age groups has increased while the wealth of younger age groups has declined.A substantial share of these changes, in both directions, can be explained by changes in household demographic and economic characteristics.As for the millennial generation, their median wealth in 2016 was lower than the wealth of any similarly aged cohort between 1989 and 2007.Millennials will have several advantages in wealth accumulation relative to previous generations, such as more education and longer working lives, but also several disadvantages, including weak prospects for economic growth and delays in home purchase and marriage.The millennial generation contains a significantly higher percentage of minorities than previous generations.We estimate that minority households have tended to accumulate less wealth than whites in the past, controlling for household characteristics, and the difference appears to be growing over time for Blacks relative to whites.These results apply to the period before the COVID-19 pandemic and are best interpreted as addressing generational wealth patterns through 2016 and providing a pre-COVID benchmark against which future studies can be compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".