Effects of welfare and antipoverty programs on participants' children.
Bibliographic record
Abstract
understand the effects of reforms targeting parents’ selfsufficiency on both parents and their children. The random-assignment design of these evaluations provides an unusually strong basis for identifying conditions under which policy-induced increases in employment among low-income and mostly single parents can help or hurt young children’s achievement. Evidence from a diverse set of experiments now illustrates some of the conditions under which policy-induced increases in employment among low-income and mostly single parents can help or hurt young children’s achievement. This article summarizes the results of research conducted as part of the Next Generation Project, a collaborative effort involving researchers at MDRC and several universities. 1 The analysis described in this article concentrates on younger children, and on understanding the pathways by which the programs affected children’s achievement. Several theories predict how policies might affect children and adolescents. 2 As in the policy debates, suggested mechanisms include parent employment, family income, child care, maternal mental health, and parenting. We find considerable support for the importance of income and center-based child care, and virtually no support for maternal mental health and parenting, as key policy-induced mediators in promoting child achievement. The analyses conducted under the Next Generation Project are based on seven random-assignment studies that together evaluate the effects of 13 employment-based welfare and antipoverty programs in the United States and two Canadian provinces. These studies provide information on 10,664 children, primarily from single-parent families, who were between our focal ages of 2 and 5 when their studies began. All of the studies began in the early to mid-1990s and were designed to estimate the effects on low-income families and children of programs aimed at increasing parental employment. The great contribution of these studies derives from their design, in which participants were randomly assigned to a program group that received the experimental policy package, or to a control group that continued to live under existing policies. In all but one study, parents were applying for welfare or renewing eligibility when they were randomly assigned. 3
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".