A Comparison of Inequality and Living Standards in Canada and the United States Using an Expanded Measure of Economic Well-Being
Bibliographic record
Abstract
We use the Levy Institute Measure of Economic Well-being (LIMEW), the most comprehensive income measure available to date, to compare economic well-being in Canada and the United States in the first decade of the 21st century. This study represents the first international comparison based on LIMEW, which differs from the standard measure of gross money income (MI) in that it includes noncash government transfers, public consumption, income from wealth, and household production, and nets out all personal taxes. We find that, relative to the United States, median equivalent LIMEW was 11 percent lower in Canada in 2000. By 2005, this gap had narrowed to 7 percent, while the difference in median equivalent MI was only 3 percent. Inequality was notably lower in Canada, with a Gini coefficient of 0.285 for equivalent LIMEW in 2005, compared to a US coefficient of 0.376 - a gap that primarily reflects the greater importance of income from wealth in the States. However, the difference in Gini coefficients declined between 2000 and 2005. We also find that the elderly were better off relative to the nonelderly in the United States, but that high school graduates did better relative to college graduates in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".