Understanding Societal Investments in Children
Notice bibliographique
Résumé
The support that society provides to children from low-income families plays a critical role in helping them grow and thrive. This support encompasses a wide range of policies, including increased funding for education and direct assistance to families in the form of cash or in-kind benefits. While it is well established that these forms of support significantly improve child outcomes, questions remain about the most effective strategies for targeting, distributing, and designing programs to maximize the impact of these resources. This dissertation focuses on deepening our understanding of three specific types of social supports for low-income children: education policies that target resources to schools serving low-income students, social policies that provide unconditional cash transfers to low-income families, and policies that focus on improving the environmental conditions of schools. \nFirst, many education policies depend on valid measures of school economic disadvantage. Recent research raises questions about the validity of commonly used free-or-reduced-price-lunch measures, particularly in light of the increasing availability of universal free school meals. The first study links confidential federal tax return data and program participation data housed at the U.S. Census Bureau to examine the validity of several measures of school economic disadvantage. Results suggest that direct certification measures provide the best widely available measure, both over time and across the distribution of school poverty. \nSecond, parental spending on children is important for child development. Using data from a randomized control trial of an unconditional cash transfer to low-income mothers of young children, the second study examines the extent to which the cash transfer is spent on goods directly related to children, relative to other sources of household income. I find that the unconditional cash transfer is more likely to be spent on children than any other household income source, including mothers’ earned income alone. The results suggest that money in the household is differentiated for spending on children. \nFinally, no level of lead is safe in a child’s blood. Moreover, low-income children have higher blood lead levels than non-economically disadvantaged children. Most research and policy has focused on lead abatement in home environments. However, researchers estimate that 73 percent of schools have lead in the drinking water. The third study uses school water lead data linked to education administrative records to estimate a causal effect of school water lead exposure on educational outcomes. The results suggests that water lead exposure may negatively affect students, although the effect is sensitive to model specification. In addition, students’ exposure to lead in schools is curiously correlated with students’ prior achievement, making a causal effect of lead in schools unclear. \nMy dissertation concludes with a discussion of themes and lessons from the three studies for improving social policies to support low-income families.\n
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».