The Varying Effects of Incarceration, Conviction, and Arrest on Wealth Outcomes among Young Adults
Bibliographic record
Abstract
Abstract Previous research indicates that incarceration leads to declines in rates of homeownership and net worth, especially among baby boomers, but questions remain as to how other types of criminal justice system contact affect wealth outcomes during the transition to adulthood. Using data from the 1997 National Longitudinal Survey of Youth, we investigate how arrests, convictions, and incarceration influence net worth, financial assets, and debt among young adults. We find that most contact with the criminal justice system limited the ability of young adults to accumulate wealth between the ages of 25 and 30, an especially important time for building life-cycle wealth. Arrests were associated with asset and debt declines of 52–53 percent, and incarceration led to net worth and asset declines of 34 and 76 percent, respectively. These direct effects were also bolstered by the indirect effects of these variables through their relationship with marriage and earnings, especially in the case of incarceration. This study draws attention to how criminal justice system contact affects early adult wealth, thereby setting the stage to influence a host of life course dynamics for individuals and their families.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".