Bibliographic record
Abstract
Material deprivation data are collected annually by the national statistics offices of many advanced economies and the resulting statistics are used by academics, policy makers and interest groups as a complement to low-income statistics. This paper presents the first nationally and provincially representative statistics on material deprivation in Canada. Using the one-time Canadian Survey of Economic Well-being (2013) we construct a material deprivation index, study the incidence and correlates of material deprivation across socio-demographic groups, and explore the overlap in incidence between material deprivation, low income and economic hardship.Our tests indicate that all available deprivation items meet the scientific criteria (suitability, validity, reliability and additivity) for inclusion in a material deprivation index. We further develop an empirical strategy that uses supplementary information in the CSEW to help set the material deprivation threshold: we assess whether a materially deprived person has a relatively high or low likelihood of being poor, thereafter analyzing how the composition of these groups changes as the threshold changes. The resulting material deprivation index includes 17 items and reflects the percentage of Canadians living in households that are deprived of two or more items. Setting the threshold is the most influential methodological decision: a threshold of two items yields a material deprivation rate of 18 percent, while thresholds of one item and three items yield rates of 29 and 13 percent respectively. We proceed the analysis with a threshold of two items. The appendix also offers all results for a threshold of three items. Other than finding a lower incidence of materialdeprivation, the general findings described below also hold for a threshold of three items.We find that the population identified as materially deprived only partially overlaps with the population identified as low-income using the Low-Income Measure (LIM). Because some Canadians are identified as having (only) a low income (8 percent), others as being (only) materially deprived (11 percent), and another group as both (8 percent), the total population that could be experiencing poverty level living conditions is considerably larger than what is measured by Canada’s low-income indicators (27 percent).Moreover, most socio-economic groups that have a high risk of low income also have a high risk of material deprivation. For these groups, the total population that could be experiencing poverty is substantively higher than for the general population. For instance, sixty percent of lone-parent households are either deemed poor by both indicators (33 percent), (only) materially deprived (17 percent) or (only) low-income (10 percent). However, some socio-demographic groups known to have a high risk of poverty according to one indicator do not also have a high risk according to the other indicator (or vice versa). For instance, persons aged 65 years and above have an above-average risk of having low income but an on average risk of being materially deprived. Families consisting of a couple with children have a below-average risk of having low income but an on average risk of being materially deprived.Finally, even though by far most materially deprived persons also report experiencing economic hardship (88 percent), there is also a significant population reporting economic hardship without being materially deprived. This suggests that economic hardship affects a broader population than that experiencing poverty. Economic hardship is defined as having, in the past year: experienced difficulty meeting necessary expenses; asked for help from friends or family, taken on debt, sold assets, or turned to a charity when short of money; and/or experienced financial difficulty due to a long-term disability or health problem.Concluding, these novel findings for Canada corroborate those of a large body of international research: identifying persons experiencing poverty level living conditions requires more than measuring low incomes alone; and, material deprivation indicators complement low-income indicators because they are better at screening the material well-being of persons with above (or below) ‘typical’ needs, costs of living, access to subsidized services, non-income financial resources and/or debt service.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".