International Comparisons of Poverty Intensity: Index Decomposition and Bootstrap Inference
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an alternative formulation for the Sen-Shorrocks index of poverty intensity for survey data with sampling weights, and decomposes the Sen-Shorrocks index into the poverty rate, the average poverty gap ratio among the poor, and the overall Gini index of poverty gap ratios. This decomposition allows the percentage change in poverty intensity to be approximated as the sum of the percentage changes in the poverty rate and average poverty gap ratio. To account for sampling variation in estimates of poverty intensity, this paper also uses the bootstrap method to compute confidence intervals in international comparisons using Luxembourg Income Study data. Cross-sectional and longitudinal analyses indicate that in the early 1970s poverty intensity in Canada and the U.S. was almost indistinguishable, but in the 1970s Canadian poverty intensity decreased. Large increases in poverty intensity occurred in the 1980s in the United States, the United Kingdom, and Sweden.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it