Impact of Child Subsidies on Child Health, Well-Being, and Investment in Child Human Capital: Evidence from Russian Longitudinal Monitoring Survey 2010–2017
Bibliographic record
Abstract
This study evaluates the impact of introducing the Maternity Capital (MC) program-a child subsidy of 250,000 Rub (7,150 euros or 10,000 USD, in 2007)-provided to mothers giving birth to/adopting a second or subsequent child since January 2007. Eligible Russian families could use this subsidy to improve family housing conditions, fund child's education/childcare, or invest in the mother's retirement fund. This study evaluates the impact of MC eligibility on various child health and developmental outcomes, household consumption patterns, and housing quality. Using data from the representative Russian Longitudinal Monitoring Survey 2010-2017, I tested regression discontinuity models and found that MC eligibility may have led to a small improvement in child health status, which could be explained by improved housing conditions, particularly in rural areas. However, children living in MC-eligible families were also more likely to report reduced socialisation. Heterogeneity analysis by child gender, household poverty status, and urban/rural residence suggests that MC incentives may have had a differential impact on some analysed outcomes. Results are robust to different polynomial and nonparametric RDD specifications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".