Tax Credits and Child Outcomes: Lessons from the U.S., U.K., and Canada
Bibliographic record
Abstract
Over the last several decades, there have been historic shifts in the structure of cash transfer programs in Western, developed countries, including the U.S., Canada, and U.K. For all three of these countries, the turn of the 21st century marked a shift away from unconditional cash transfer programs like traditional cash welfare, towards an emphasis on benefits that encourage or require work.In this paper, I review the evidence on the impact of tax credits on child outcomes, focusing on what is known about child-oriented tax credits in the U.S. (EITC, CTC), the U.K. (WFTC, CTC, WTC), and Canada (Canada Child Tax Benefit (CCTB), National Child Benefit (NCB), and the Canada Child Benefit (CCB).Overwhelmingly, the evidence from these three countries suggests that tax credits have positive impacts on children on a host of different outcomes, including infant birthweight, childhood health and achievement, educational attainment, wages, and poverty in adulthood.While there is a large, growing body of evidence on the impact of these tax credits on children, future work should further investigate the precise mechanisms through which tax credits impact child outcomes, the characteristics of children most impacted by these credits, and the importance the frequency of credits distribution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".