Money speaks: Reductions in severe food insecurity follow the Canada Child Benefit
Bibliographic record
Abstract
Food insecurity is a pervasive public health problem in high income countries, disproportionately affecting households with children. Though it has been strongly linked with socioeconomic status and investments in social protection programs, less is known about its sensitivity to specific policy interventions, particularly among families. We implemented a difference-in-difference (DID) design to assess whether Canadian households with children experienced reductions in food insecurity compared to those without following the roll-out of a new country-wide income transfer program: the Canada Child Benefit (CCB). Data were derived from the 2015-2018 cycles of Canadian Community Health Survey. We used multinomial logistic regressions to test the association between CCB and food insecurity among three samples: households reporting any income (N = 41,455), the median income or less (N = 18,191) and the Low Income Measure (LIM) or less (N = 7579). The prevalence and severity of food insecurity increased with economic vulnerability, and were both consistently higher among households with children. However, they also experienced significantly greater drops in the likelihood of experiencing severe food insecurity following CCB; most dramatically among those reporting the LIM or less (DID: -4.7%, 95% CI: -8.6, -0.7). These results suggest that CCB disproportionately benefited families most susceptible to food insecurity. Furthermore, our findings also indicate that food insecurity may be impacted by even modest changes to economic circumstance, speaking to the potential of income transfers to help people meet their basic needs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".