Tax Relief for Breadwinners or Caregivers? The Designs of Earned and Child Tax Credits in Five Anglo-American Countries
Bibliographic record
Abstract
Efforts to reconcile work-life balance goals are at the heart of the design of tax-benefit programs. Yet this relationship between work-life balance and tax-benefit programs is relatively unexplored. To help address this lacuna this paper compares approaches to reconciling work-life balance goals in the designs of earned and child tax credits in Australia, Canada, New Zealand, the United Kingdom, and the United States. These designs indicate different approaches to reconciling work-life balance. In their design of earned and child tax credits the United States places emphasis upon targeting tax relief by paid employment (to breadwinners), Australia and Canada by family structures (to caregivers), and New Zealand and the United Kingdom by both paid employment and family structures, although in New Zealand assistance is provided on a more residual basis. Of these designs the dual objective approach, particularly of the United Kingdom, appears to offer greater opportunity for both directly addressing relatively high rates of child poverty and increasing low wage caregivers' labor supply (as part of a broader poverty reduction strategy).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".