Social Transfers, Earnings and Low-income Intensity Among Canadian Children, 1981-96: Highlighting Recent Development in Low-income Measurement
Bibliographic record
Abstract
In this paper, we revisit trends in low-income among Canadian children by taking advantage of recent developments in the measurement of low-income intensity. We focus in particular on the Sen-Shorrocks-Thon (SST) index and its elaboration by Osberg and Xu. Low-income intensity declined in the 1980s but rose in the 1990s. Declining earnings put upward pressure on low-income levels over much of the period. Higher transfers more than offset this pressure in the 1980s and continued to absorb a substantial share of the increase through 1993. In contrast, the rise in low-income intensity after 1993 reflected reductions in UI and social assistance benefits that were not offset by increased employment earnings, at least to 1996 the latest year used in this paper. A major aim of the paper is methodological. We contrast results using the SST index with results produced by the more familiar low-income rate, the usual measure for indexing low-income trends. The low-income rate is embedded in the SST index, but unlike the index, the rate incorporates only partial information on the distribution of low-income. Consequently, the low-income rate is generally unable to detect the changes we describe and this is true irrespective of the choice of low-income cut-off. Compared to the low-income intensity measure, the rate is also relatively insensitive to changes in transfer payments and employment earnings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".