The impact of placing adolescent males into foster care on their education, income assistance and incarcerations
Bibliographic record
Abstract
Understanding the causal impacts of taking youth on the margins of risk into foster care is an element of the evidence-base on which policy development for this crucial function of government relies. Yet, there is little research looking at these causal impacts; neither is there much empirical work looking at long-term outcomes. This paper focuses on estimating the impact of placing 16 to 18 year old male youth into care on their rates of high school graduation, and post-majority income assistance receipt and incarceration. Two distinct sources of exogenous variation are used to generate instrumental variables, the estimates from which are interpreted in a heterogeneous treatment effects framework as local average treatment effects (LATEs). And, indeed, each source of exogenous variation is observed to estimate different parameters. While both instruments are in accord in that placement in foster care reduces (or delays) high school graduation, the impact of taking youth into care on income assistance use has dramatically different magnitudes across the two margins explored, and, perhaps surprisingly, one source of exogenous variation causes an increase, and the other a decrease, in the likelihood of the youth being incarcerated by age 20. Our results suggest that it is not enough to ask whether more or fewer children should be taken into care; rather, which children are, and how they are, taken into care matter for long-term outcomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".