American Exceptionalism Revisited: Tax Relief, Poverty Reduction, and the Politics of Child Tax Credits
Bibliographic record
Abstract
In the 1990s, several liberal welfare regimes (LWRs) introduced child tax credits (CTCs) aimed at reducing child poverty. While in other countries these tax credits were refundable, the United States alone introduced a nonrefundable CTC. As a result, the United States was the only country in which poor and working-class families were paradoxically excluded from these new benefits. A comparative analysis of Canada and the United States shows that American exceptionalism resulted from the cultural legacy of distinct public policies. We argue that policy changes in the 1940s institutionalized different "logics of appropriateness" that later constrained policymakers in the 1990s. Specifically, the introduction of family allowances in Canada and other LWR countries naturalized a logic of income supplementation in which families could legitimately receive cash benefits without the stigma of "welfare." Lacking this policy legacy, American attempts to introduce a refundable CTC were quickly derailed by policymakers who saw it as equivalent to welfare. Instead, they introduced a narrow, nonrefundable CTC under the alternative logic of "tax relief," even though this meant excluding the lowest-income families. The cultural legacy of past policies can explain American exceptionalism not only with regard to CTCs but to other social policies as well.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".