Sensitivity of the Index of Economic Well-Being to Different Measures of Poverty: LICO vs LIM
Bibliographic record
Abstract
This report uses an exercise similar to comparative statics to show that the growth rate of the Index of Economic Well-being (IEWB) for 1981-2011 was much greater when poverty was measured using Statistics Canada’s Low Income Cut-Offs (LICOs) than it was when poverty was measured using Statistics Canada’s Low Income Measures (LIMs). The LICO, an absolute definition of poverty, also exhibited greater cyclical variation than the LIM, a relative definition of poverty. The IEWB appears to reflect these trends. Real income growth was determined to be a key factor in explaining these trends because absolute poverty lines remain fixed while relative poverty lines shift in response to changes in real income. The report concludes that there is a significant difference in the growth rate of the IEWB between measures, although not as large as it would be in the absence of linear scaling methodology. Consequently, the use of the LIM instead of the LICO results in a downward bias on economic well-being growth in Canada. The choice of the ‘appropriate poverty measure’ therefore has significant consequences for the discussion of trends in economic well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".