Low-income Intensity During the 1990s: The Role of Economic Growth, Employment Earnings and Social Transfers
Bibliographic record
Abstract
All countries look to economic growth to reduce low-income. This paper focuses on the 1990s and assesses the role played by changes in economic growth, employment earnings and government transfers in the patterns of low-income intensity in Canada during the 1990s. We find that low-income intensity was higher in most provinces during the 1990s than during the 1980s (comparing comparable positions in the business cycle). The largest increase was in Ontario. In particular, in spite of the slow economic growth and falling unemployment between 1993 and 1997, low-income intensity continued to rise. Both increases in the low-income rate and the low-income gaps contributed to this higher level. Employment earnings continued to decline among low-income families over the 1990s, contributing to the increase in low-income intensity in central and eastern Canada in particular. This is related in part to the more severe recession of the early 1990s east of Manitoba, and the lack of recovery among poorer families. During the 1990s changes in government transfers did not offset the fall in employment earnings among lower-income families, as they did during the 1980s, resulting in rising low-income intensity. Declining transfer benefits were associated with a rising low-income gap in some provinces, particularly Alberta. The latest data available at the time of writing was 1998. The strong economic growth of 1999 and 2000 will likely have reduced low-income intensity, but it remains to be seen if it falls back to the level of the 1980s cyclical peak.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".