Transfers Matter Most: How Changes in Transfer Systems of Canada and the United States Explain the Divergence in Household Poverty Levels from 1974-1994
Bibliographic record
Abstract
From 1974 to 1994, Canada and the United States experienced quite substantial divergences in relative household poverty rates and inequality levels from similar starting points.Although several scholars have attempted to explain Canadian and U.S. differences in poverty and inequality levels at one point in time, none have satisfactorily explained the causes of these divergent trends. Utilizing high quality, comparable data from the Luxembourg Income Survey, my analysis of the household poverty rates demonstrates that differences in the policies and reforms of the two country’s transfer systems explains the divergence relative household poverty rates. The data also seriously cast doubt on other potential market, cultural, or tax system explanations. Further, by selectively removing the income from each specific category and then specific type of transfer income and recalculating the household poverty rate, my “sensitivity-type” analysis clearly demonstrates the predominant explanatory power for differences in the structure and reforms of social insurance transfers income and, more specifically, social retirement benefits. In Canada, the expansion of the Guaranteed Income Supplement (GIS) for low-income elderly families over this period provides the only plausible explanation for the dramatic reduction in the poverty rate of the elderly households relative to the United States and, perhaps somewhat surprisingly, explains most of the divergence in household poverty rates between the two countries from 1974 to 1994. As a large part of the divergence in inequality rates is also driven by reduction in household poverty rates in Canada relative to the United States, the expansion of the GIS benefits also provides a major explanation for the divergence in levels of household inequality over this period.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".